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Bovine milk peptides are the protein fragments with diverse bioactive properties having antioxidant, anticarcinogenic, other therapeutic and nutraceutical potentials. These peptides are formed in milk by enzymatic hydrolysis, gastrointestinal digestion and fermentation processes. They have significant health impact with high potency and low toxicity making them a suitable natural alternative for preventing and managing diseases. Antibiotic resistance has increased the quest for better peptide candidates with antimicrobial effects. This article presents a comprehensive review on well documented antimicrobial, immunological, opioid, and anti-hypertensive activities of bovine milk peptides. It also covers the usage of computational biology tools and databases for prediction and analysis of the food-derived bioactive peptides. In silico analysis of amino acid sequences of Bos taurus milk proteins have been predicted to generate peptides with dipeptidyl peptidase IV inhibitory and ACE inhibitory properties, making them favorable candidates for developing blood sugar lowering drugs and anti-hypertensives. In addition to the prediction of new bioactive peptides, application of bioinformatics tools to predict novel functions of already known peptides is also discussed. Overall, this review focuses on the reported as well as predicted biologically active peptide of casein and whey proteins of bovine milk that can be utilized to develop therapeutic agents. [ABSTRACT FROM AUTHOR]

Importance: Neonatal anemia has a long-term effect on children's growth and development. Anemia during pregnancy is also the most widespread nutritional deficiency among pregnant women in the world; If it leads to anemia in newborns, it will affect a wide range of people and be a public health problem worthy of attention. Objective: To study the relationship between maternal anemia during pregnancy and neonatal hemoglobin levels. Data sources: PubMed, Web of science, Scopus, MEDLINE, Embase, ProQuest, Dissertations & Theses Global, The Cochrane Library, China Biology Medicine Database, Chinese CNKI Database, and Chinese Wanfang Database were systematically searched from inception to August 31, 2022. Study selection: The meta-analysis included all original studies which pertain to cohort studies, case-control studies or cross-sectional studies that investigated the relationship between maternal anemia during pregnancy and neonatal hemoglobin levels. Data extraction and synthesis: Hemoglobin level of both anemic and non-anemic pregnant mothers and their paired newborns were pooled from the selected studies. The random-effects model was used to assess the risk of getting a lower neonatal hemoglobin level between mothers with and without pregnant anemia. Data analyses were performed from September 5, 2022, to March 10, 2023. Main outcomes and measures: Maternal anemia during pregnancy is a risk factor of lower neonatal hemoglobin levels. Results: The initial search yielded 4267 records of which 116 articles underwent full-text evaluation, which identified 18 articles and a total of 1873 patients that were included. The findings of the meta-analysis showed a significant difference between the two groups(MD=-1.38; 95%CI:[-1.96,-0.80]. p<0.01), while the co-effect showed that the neonatal hemoglobin value of anemic mothers was 1.38g/dL lower than that of non-anemic mothers(-1.96,-0.80), suggesting a correlation between maternal anemia lower neonatal hemoglobin levels. Conclusions and relevance: This systematic review and meta-analysis demonstrated that maternal anemia during pregnancy were associated with a lower level of newborn hemoglobin levels. This may enable a better understanding of neonatal anemia and provide guidance towards future development of nutritional supplementation during pregnancy and the prediction of postpartum outcomes. Trial registration: PROSPERO Identifier: CRD42022352759. [ABSTRACT FROM AUTHOR]

Background: Lymph node metastasis (LNM) is a critical factor affecting the outcomes of head and neck squamous cell carcinoma (HNSCC) and the main reason for treatment failure. This study was designed to examine the effects of the key genes involved in the LNM of HNSCC. Methods: Tissue samples (HNSCC) were examined by transcriptome sequencing, and the core genes associated with LNM were detected via bioinformatics analysis. The functions of these core genes were then validated using the TCGA biological database and their effects on the propagation, invasion, and metastasis of HNSCC cells were evaluated through cell culture experiments. Moreover, the effect of core gene expression on the LNM capability of HNSCC was confirmed via a footpad xenograft mice model. Results: In the findings, a key gene involved in the LNM of HNSCC was identified as SLC7A2. It was correlated with adverse clinical prognosis and expressed with low expression in HNSCC tissues. As shown in cell culture experiments, FaDu and SCC15 cell growth, invasion, and migration were inhibited when SLC7A2 was overexpressed. Further, cell apoptosis was stimulated, and lymphangiogenesis was suppressed through the downregulation of CPB2 expression. Animal studies demonstrated that the growth and LNM of HNSCC cells were inhibited by SLC7A2 overexpression. Conclusion: It is concluded that SLC7A2 is involved in HNSCC lymphatic metastasis by controlling CPB2 function. The results are anticipated to offer new directions for the effective treatment of HNSCC. [ABSTRACT FROM AUTHOR]

AbstractPurposeMethodsResultsConclusion\nIMPLICATIONS FOR REHABILITATIONTo examine the effects of inspiratory muscle training (IMT) on pulmonary and diaphragmatic function, exercise capacity, balance and quality of life (QOL), in post-stroke individuals.A literature search was conducted using MEDLINE, CINAHL, EMBASE, PubMed, PEDro, Web of Science and China Biological Medicine databases. Randomized controlled trials (RCTs) with a PEDro score ≥6 focusing on the effects of IMT were included. The GRADE system was used to determine the certainty of evidence for each outcome.Nine studies (255 participants) were included. IMT significantly increased forced expiratory volume in one second (FEV1) (4 studies, 112 participants, mean difference (MD)=0.18 litre, 95% confidence interval (CI): 0.14–0.23); maximal inspiratory pressure (MIP) (8 studies, 226 participants, MD = 6.37 cm H2O, 95% CI: 1.26–11.49); and diaphragm thickness fraction (DTf) on both sides (MD of affected side vs. unaffected side: 51 vs. 37%). The evidence certainty for diaphragmatic function was moderate. No significant change was observed in forced vital capacity (FVC), balance function, exercise capacity and QOL.This review reveals moderate evidence certainty in support of IMT improving diaphragmatic function. It appears that a training intensity between 30 and 50% MIP results in a significantly improved MIP.Stroke not only impacts motor function but also affects respiratory function, especially diaphragmatic function.Inspiratory muscle training (IMT) is an intervention that has been used to improve the strength and endurance of inspiratory muscles.There is evidence of moderate certainty which cautiously recommends IMT to improve diaphragmatic function after stroke.The evidence supporting IMT for the enhancement of forced vital capacity, balance function, exercise capacity and quality of life in stroke patients is inadequate. [ABSTRACT FROM AUTHOR]

Periodontal disease, a multifactorial inflammatory condition affecting the supporting structures of the teeth, has been increasingly recognized for its association with various systemic diseases. Understanding the molecular comorbidities of periodontal disease is crucial for elucidating shared pathogenic mechanisms and potential therapeutic targets. In this study, we conducted comprehensive literature and biological database mining by utilizing DisGeNET2R for extracting gene–disease associations, Romin for integrating and modeling molecular interaction networks, and Rentrez R libraries for accessing and retrieving relevant information from NCBI databases. This integrative bioinformatics approach enabled us to systematically identify diseases sharing associated genes, proteins, or molecular pathways with periodontitis. Our analysis revealed significant molecular overlaps between periodontal disease and several systemic conditions, including cardiovascular diseases, diabetes mellitus, rheumatoid arthritis, and inflammatory bowel diseases. Shared molecular mechanisms implicated in the pathogenesis of these diseases and periodontitis encompassed dysregulation of inflammatory mediators, immune response pathways, oxidative stress pathways, and alterations in the extracellular matrix. Furthermore, network analysis unveiled the key hub genes and proteins (such as TNF, IL6, PTGS2, IL10, NOS3, IL1B, VEGFA, BCL2, STAT3, LEP and TP53) that play pivotal roles in the crosstalk between periodontal disease and its comorbidities, offering potential targets for therapeutic intervention. Insights gained from this integrative approach shed light on the intricate interplay between periodontal health and systemic well-being, emphasizing the importance of interdisciplinary collaboration in developing personalized treatment strategies for patients with periodontal disease and associated comorbidities. [ABSTRACT FROM AUTHOR]

Motivation Dictionary-based named entity recognition (NER) allows terms to be detected in a corpus and normalized to biomedical databases and ontologies. However, adaptation to different entity types requires new high-quality dictionaries and associated lists of blocked names for each type. The latter are so far created by identifying cases that cause many false positives through manual inspection of individual names, a process that scales poorly. Results In this work, we aim to improve block list s by automatically identifying names to block, based on the context in which they appear. By comparing results of three well-established biomedical NER methods, we generated a dataset of over 12.5 million text spans where the methods agree on the boundaries and type of entity tagged. These were used to generate positive and negative examples of contexts for four entity types (genes, diseases, species, and chemicals), which were used to train a Transformer-based model (BioBERT) to perform entity type classification. Application of the best model (F1-score = 96.7%) allowed us to generate a list of problematic names that should be blocked. Introducing this into our system doubled the size of the previous list of corpus-wide blocked names. In addition, we generated a document-specific list that allows ambiguous names to be blocked in specific documents. These changes boosted text mining precision by ∼ 5.5 % on average, and over 8.5% for chemical and 7.5% for gene names, positively affecting several biological databases utilizing this NER system, like the STRING database, with only a minor drop in recall (0.6%). Availability and implementation All resources are available through Zenodo https://doi.org/10.5281/zenodo.11243139 and GitHub https://doi.org/10.5281/zenodo.10289360. [ABSTRACT FROM AUTHOR]

Compared with analyzing omics data from a single platform, an integrative analysis of multi-omics data provides a more comprehensive understanding of the regulatory relationships among biological features associated with complex diseases. However, most existing frameworks for integrative analysis overlook two crucial aspects of multi-omics data. Firstly, they neglect the known dependencies among biological features that exist in highly credible biological databases. Secondly, most existing integrative frameworks just simply remove the subjects without full omics data to handle block missingness, resulting in decreasing statistical power. To overcome these issues, we propose a network-based integrative Bayesian framework for biomarker selection and disease outcome prediction based on multi-omics data. Our framework utilizes Dirac spike-and-slab variable selection prior to identifying a small subset of biomarkers. The incorporation of gene pathway information improves the interpretability of feature selection. Furthermore, with the strategy in the FBM (stand for "full Bayesian model with missingness") model where missing omics data are augmented via a mechanistic model, our framework handles block missingness in multi-omics data via a data augmentation approach. The real application illustrates that our approach, which incorporates existing gene pathway information and includes subjects without DNA methylation data, results in more interpretable feature selection results and more accurate predictions. [ABSTRACT FROM AUTHOR]

Motivation: Computational techniques for drug-disease prediction are essential in enhancing drug discovery and repositioning. While many methods utilize multimodal networks from various biological databases, few integrate comprehensive multi-omics data, including transcriptomes, proteomes, and metabolomes. We introduce STRGNN, a novel graph deep learning approach that predicts drug-disease relationships using extensive multimodal networks comprising proteins, RNAs, metabolites, and compounds. We have constructed a detailed dataset incorporating multi-omics data and developed a learning algorithm with topological regularization. This algorithm selectively leverages informative modalities while filtering out redundancies. Results: STRGNN demonstrates superior accuracy compared to existing methods and has identified several novel drug effects, corroborating existing literature. STRGNN emerges as a powerful tool for drug prediction and discovery. The source code for STRGNN, along with the dataset for performance evaluation, is available at https://github.com/yuto-ohnuki/STRGNN.git. [ABSTRACT FROM AUTHOR]

Cholangiocarcinoma (CCA) is a hepatobiliary carcinoma with uncontrolled cell proliferation, poor prognosis, and high mortality. The ovarian tumor structural domain (OTU) containing protein 6B (OTUD6B) belongs to the OTU deubiquitin family and is vital in tumor development. However, its expression and biological function in CCA remain unknown. The expression of OTUD6B in CCA was analyzed using TIMER2.0, UALCAN, and GEO databases. MTT, clonal formation assay, immunofluorescence staining, immunohistochemistry staining, and flow cytometry examined the regulation of OTUD6B on cell proliferation, cycle, and apoptosis. The effects of OTUD6B on tumor volume and weight were assessed using the xenograft tumor model. The activities of PTK2 and STAT3 were detected by western blot and CO‐IP. The biological database identified that OTUD6B was upregulated in CCA. In CCA cells, OTUD6B knockdown reduced CCA cell proliferation and promoted apoptosis. Cell cycle analysis indicated that the cycle stopped at the G0/G1 phase after OTU6B downregulation. Furthermore, OTUD6B knockdown resulted in a decrease in tumor volume and weight in xenograft tumor models. Mechanistically, OTUD6B is involved in the deubiquitination of PTK2. PTK2 further affected the phosphorylation of STAT3 thereby regulating the CCA process. Our study demonstrates that OTUD6B knockdown participates in the ubiquitination of PTK2 and phosphorylation of STAT3 to alleviate the process of CCA. These results suggest that OTUD6B may be a potential new strategy for CCA treatment. [ABSTRACT FROM AUTHOR]

Motivation Biological background knowledge plays an important role in the manual quality assurance (QA) of biological database records. One such QA task is the detection of inconsistencies in literature-based Gene Ontology Annotation (GOA). This manual verification ensures the accuracy of the GO annotations based on a comprehensive review of the literature used as evidence, Gene Ontology (GO) terms, and annotated genes in GOA records. While automatic approaches for the detection of semantic inconsistencies in GOA have been developed, they operate within predetermined contexts, lacking the ability to leverage broader evidence, especially relevant domain-specific background knowledge. This paper investigates various types of background knowledge that could improve the detection of prevalent inconsistencies in GOA. In addition, the paper proposes several approaches to integrate background knowledge into the automatic GOA inconsistency detection process. Results We have extended a previously developed GOA inconsistency dataset with several kinds of GOA-related background knowledge, including GeneRIF statements, biological concepts mentioned within evidence texts, GO hierarchy and existing GO annotations of the specific gene. We have proposed several effective approaches to integrate background knowledge as part of the automatic GOA inconsistency detection process. The proposed approaches can improve automatic detection of self-consistency and several of the most prevalent types of inconsistencies.   This is the first study to explore the advantages of utilizing background knowledge and to propose a practical approach to incorporate knowledge in automatic GOA inconsistency detection. We establish a new benchmark for performance on this task. Our methods may be applicable to various tasks that involve incorporating biological background knowledge. Availability and implementation https://github.com/jiyuc/de-inconsistency. [ABSTRACT FROM AUTHOR]

Simple Summary: Bacteria increase their genetic diversity by acquiring genetic material from a variety of organisms across different species, a process known as horizontal gene transfer. Recently, there has been an increasing emphasis in the scientific literature on the importance of HGT for the evolution of eukaryotes such as fungi, animals, and plants. However, this observation presents a paradox since eukaryotes have developed mechanisms to prevent HGT. Our work proposes an alternative scenario to explain the abundance of reported cases of HGT in eukaryotes. We suggest that the misidentification of HGT candidates in eukaryotes is due to the lack of similar sequences (genes or proteins) in public databases. To support this hypothesis, we designed specific databases to identify potential HGT candidates within a particular group of fungi. Our results demonstrate that fewer HGT candidates are detected when more similar sequences exist for these candidates in databases. This finding holds significant relevance as public databases continue to grow; consequently, newly available information may refute several previously identified instances of HGT. Our experiments lead us to conclude that database imbalances need consideration before asserting new occurrences of HGT in eukaryotes. Horizontal gene transfer (HGT) is a widely acknowledged phenomenon in prokaryotes for generating genetic diversity. However, the impact of this process in eukaryotes, particularly interdomain HGT, is a topic of debate. Although there have been observed biases in interdomain HGT detection, little exploration has been conducted on the effects of imbalanced databases. In our study, we conducted experiments to assess how different databases affect the detection of interdomain HGT using proteomes from the Pezizomycotina fungal subphylum as our focus group. Our objective was to simulate the database imbalance commonly found in public biological databases, where bacterial and eukaryotic sequences are unevenly represented, and demonstrate that an increase in uploaded eukaryotic sequences leads to a decrease in predicted HGTs. For our experiments, four databases with varying proportions of eukaryotic sequences but consistent proportions of bacterial sequences were utilized. We observed a significant reduction in detected interdomain HGT candidates as the proportion of eukaryotes increased within the database. Our data suggest that the imbalance in databases bias the interdomain HGT detection and highlights challenges associated with confirming the presence of interdomain HGT among Pezizomycotina fungi and potentially other groups within Eukarya. [ABSTRACT FROM AUTHOR]

Analytical performance specifications (APS) are typically established through one of three models: (i) outcome studies, (ii) biological variation (BV), or (iii) state-of-the-art. Presently, The APS can, for most measurands that have a stable concentration, be based on BV. BV based APS, defined for imprecision, bias, total allowable error and allowable measurement uncertainty, are applied to many different processes in the laboratory. When calculating APS, it is important to consider the different APS formulae, for what setting they are to be applied and if they are suitable for the intended purpose. In this opinion paper, we elucidate the background, limitations, strengths, and potential intended applications of the different BV based APS formulas. When using BV data to set APS, it is important to consider that all formulae are contingent on accurate and relevant BV estimates. During the last decade, efficient procedures have been established to obtain reliable BV estimates that are presented in the EFLM biological variation database. The database publishes detailed BV data for numerous measurands, global BV estimates derived from meta-analysis of quality-assured studies of similar study design and automatic calculation of BV based APS. [ABSTRACT FROM AUTHOR]

Several studies have reported the efficacy of traditional Chinese medicine (TCM) for central serous chorioretinopathy (CSC), while some ophthalmologists are concerned that TCM may be a risk factor for CSC as some chinese herbs contain hormonal ingredients. This study aimed to evaluate the efficacy and safety of TCM in treating patients with CSC. Randomized controlled trials (RCTs) and observational studies of TCM for CSC were searched up to July 10, 2023 on the following biological databases without language and publication time restrictions: PubMed, Ovid Medline, Embase, Cochrane Library, The Chinese National Knowledge Infrastructure Database (CNKI), Technology Periodical Database (VIP), Wanfang, and Chinese Biomedical Literature Service System (SinoMed). Review Manager V.5.4.1 and Stata 14 software were used for data analysis. Finally, thirty-eight studies were finally included including 23 RCTs and 15 cohort studies. The meta-analysis showed that compared with the routine treatment alone, the combination of TCM can not only reduce the recurrence rate (OR = 0.29, 95% CI: 0.21,0.40; I2 = 0%) and central retinal thickness (CRT) (MD = - 35.63, 95% CI: - 45.96,-25.30; I2 = 89%) of CSC, but improve patients' best corrected visual acuity (BCVA) (SMD = 0.86, 95% CI: 0.62,1.11; I2 = 77%); additionally, it has no obvious side effects compared with routine treatment (OR = 0.72, 95% CI: 0.39,1.34; I2 = 10%). Overall, this study shows that the use of TCM does not increase the risk of CSC recurrence; on the contrary, the combination of TCM may reduce the recurrence of CSC and improve BCVA and CRT in patients with CSC compared with conventional treatment. [ABSTRACT FROM AUTHOR]

Summary Functional interpretation of biological entities such as differentially expressed genes is one of the fundamental analyses in bioinformatics. The task can be addressed by using biological pathway databases with enrichment analysis (EA). However, textual description of biological entities in public databases is less explored and integrated in existing tools and it has a potential to reveal new mechanisms. Here, we present a new R package biotextgraph for graphical summarization of omics' textual description data which enables assessment of functional similarities of the lists of biological entities. We illustrate application examples of annotating gene identifiers in addition to EA. The results suggest that the visualization based on words and inspection of biological entities with text can reveal a set of biologically meaningful terms that could not be obtained by using biological pathway databases alone. The results suggest the usefulness of the package in the routine analysis of omics-related data. The package also offers a web-based application for convenient querying. Availability and implementation The package, documentation, and web server are available at: https://github.com/noriakis/biotextgraph. [ABSTRACT FROM AUTHOR]

The increase in molecular data and the use of computer technologies in biology have led to the emergence of professional biocurators, who populate biological databases and knowledgebases with high-quality information. Although crucial to life science knowledge production, biocuration is, to a large extent, invisible labour that takes place behind the scenes of data-centric life science. The field suffers from a lack of recognition and status that has been linked to a language of service and a scientific system that is not equipped to recognise and reward new types of scientific practices. However, as the majority of biocurators are highly educated female biologists, biocuration is also reproducing the problematic pattern of women leaving the scientific tenure track in favour of less prestigious positions. Instead of viewing the issue as just another example of 'the leaky pipeline,' the gendering of biocuration could be seen as an interplay of gendered structures in science, organisations and society which makes a career in biocuration attractive for female scientists while at the same time positioning the activity as non-scientific low-status work. By illuminating some of the ways gender works in the processes which render certain kinds of technoscientific work invisible, biocuration serves as an example of how existing social structures influence the emerging data-centric science. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this study was to evaluate the impact of obesity on the treatment response to secukinumab and drug survival rate in patients with ankylosing spondylitis (AS). Methods: We performed an observational cohort study that included AS patients based on the biological drug database in Turkey (TURKBIO) Registry between 2018 and 2021. The patients were divided into three groups: normal [body mass index (BMI) < 25 kg/m2], overweight (BMI: 25–30 kg/m2), and obese (BMI ≥ 30 kg/m2). Disease activity was evaluated at baseline, 3, 6, and 12 months. Drug retention rates at 12 months were also investigated. Results: There were 166 AS patients using secukinumab (56.6% male, mean age: 44.9 ± 11.6 years). The median follow-up time was 17.2 (3–33.2) months. Forty-eight (28.9%) patients were obese. The mean age was higher in the obese group than in others (P = .003). There was no statistically significant difference in Bath Ankylosing Spondylitis Disease Activity Index 50, Assessment of SpondyloArthritis international Society 20 (ASAS20), ASAS40, Ankylosing Spondylitis Disease Activity Score (ASDAS) low disease activity, and ASDAS clinically important improvement responses between the three groups at 3, 6, and 12 months, although they were numerically lower in obese patients. Drug retention rates at 12 months were similar in all groups (P > .05). Conclusions: This study suggested that obesity did not affect secukinumab treatment response and drug retention in AS patients. [ABSTRACT FROM AUTHOR]

Cannabis has gained popularity in recent years as a substitute treatment for pain following the risks of typical treatments uncovered by the opioid crisis. The active ingredients frequently associated with pain-relieving effects are the phytocannabinoids Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD), but their effectiveness and mechanisms of action are still under research. In this study, we used Caenorhabditis elegans, an ideal model organism for the study of nociception that expresses mammal ortholog cannabinoid (NPR-19 and NPR-32) and vanilloid (OSM-9 and OCR-2) receptors. Here, we evaluated the antinociceptive activity of THC and CBD, identifying receptor targets and several metabolic pathways activated following exposure to these molecules. The thermal avoidance index was used to phenotype each tested C. elegans experimental group. The data revealed for the first time that THC and CBD decreases the nocifensive response of C. elegans to noxious heat (32–35 °C). The effect was reversed 6 h post- CBD exposure but not for THC. Further investigations using specific mutants revealed CBD and THC are targeting different systems, namely the vanilloid and cannabinoid systems, respectively. Proteomic analysis revealed differences following Reactome pathways and gene ontology biological process database enrichment analyses between CBD or THC-treated nematodes and provided insights into potential targets for future drug development. [ABSTRACT FROM AUTHOR]

LncRNAs are non-coding RNAs with a length of more than 200 nucleotides. More and more evidence shows that lncRNAs are inextricably linked with diseases. To make up for the shortcomings of traditional methods, researchers began to collect relevant biological data in the database and used bioinformatics prediction tools to predict the associations between lncRNAs and diseases, which greatly improved the efficiency of the study. To improve the prediction accuracy of current methods, we propose a new lncRNA-disease associations prediction method with attention mechanism, called ResGCN-A. Firstly, we integrated lncRNA functional similarity, lncRNA Gaussian interaction profile kernel similarity, disease semantic similarity, and disease Gaussian interaction profile kernel similarity to obtain lncRNA comprehensive similarity and disease comprehensive similarity. Secondly, the residual graph convolutional network was used to extract the local features of lncRNAs and diseases. Thirdly, the new attention mechanism was used to assign the weight of the above features to further obtain the potential features of lncRNAs and diseases. Finally, the training set required by the Extra-Trees classifier was obtained by concatenating potential features, and the potential associations between lncRNAs and diseases were obtained by the trained Extra-Trees classifier. ResGCN-A combines the residual graph convolutional network with the attention mechanism to realize the local and global features fusion of lncRNA and diseases, which is beneficial to obtain more accurate features and improve the prediction accuracy. In the experiment, ResGCN-A was compared with five other methods through 5-fold cross-validation. The results show that the AUC value and AUPR value obtained by ResGCN-A are 0.9916 and 0.9951, which are superior to the other five methods. In addition, case studies and robustness evaluation have shown that ResGCN-A is an effective method for predicting lncRNA-disease associations. The source code for ResGCN-A will be available at https://github.com/Wangxiuxiun/ResGCN-A. [ABSTRACT FROM AUTHOR]

Motivation Proteomic profiles reflect the functional readout of the physiological state of an organism. An increased understanding of what controls and defines protein abundances is of high scientific interest. Saccharomyces cerevisiae is a well-studied model organism, and there is a large amount of structured knowledge on yeast systems biology in databases such as the Saccharomyces Genome Database, and highly curated genome-scale metabolic models like Yeast8. These datasets, the result of decades of experiments, are abundant in information, and adhere to semantically meaningful ontologies. Results By representing this knowledge in an expressive Datalog database we generated data descriptors using relational learning that, when combined with supervised machine learning, enables us to predict protein abundances in an explainable manner. We learnt predictive relationships between protein abundances, function and phenotype; such as α -amino acid accumulations and deviations in chronological lifespan. We further demonstrate the power of this methodology on the proteins His4 and Ilv2, connecting qualitative biological concepts to quantified abundances. Availability and implementation All data and processing scripts are available at the following Github repository: https://github.com/DanielBrunnsaker/ProtPredict. [ABSTRACT FROM AUTHOR]

Over time, the renowned Kyoto Encyclopedia of Genes and Genomes (KEGG) has grown to become one of the most comprehensive online databases for biological procedures. The majority of the data are stored in the form of pathways, which are graphs that depict the relationships between the diverse items participating in biological procedures, such as genes and chemical compounds. However, the size, complexity, and diversity of these graphs make them difficult to explore and understand, as well as making it difficult to extract a clear conclusion regarding their most important components. In this regard, we present KEGGSum, a system enabling the efficient and effective summarization of KEGG pathways. KEGGSum receives a KEGG identifier (Kid) as an input, connects to the KEGG database, downloads a specialized form of the pathway, and determines the most important nodes in the graph. To identify the most important nodes in the KEGG graphs, we explore multiple centrality measures that have been proposed for generic graphs, showing their applicability to KEGG graphs as well. Then, we link the selected nodes in order to produce a summary graph out of the initial KEGG graph. Finally, our system visualizes the generated summary, enabling an understanding of the most important parts of the initial graph. We experimentally evaluate our system, and we show its advantages and benefits. [ABSTRACT FROM AUTHOR]

Background: More and more studies show that lncRNA is widely involved in various physiological processes of the organism. However, the functions of the vast majority of them continue to be unknown. In addition, data related to lncRNAs in biological databases are constantly increasing. Therefore, it is quite urgent to develop a computing method to make the utmost of these data. Results: In this paper, we propose a new computational method based on global heterogeneous networks to predict the functions of lncRNAs, called DNGRGO. DNGRGO first calculates the similarities among proteins, miRNAs, and lncRNAs, and annotates the functions of lncRNAs according to its similar protein-coding genes, which have been labeled with gene ontology (GO). To evaluate the performance of DNGRGO, we manually annotated GO terms to lncRNAs and implemented our method on these data. Compared with the existing methods, the results of DNGRGO show superior predictive performance of maximum F-measure and coverage. Conclusions: DNGRGO is able to annotate lncRNAs through capturing the low-dimensional features of the heterogeneous network. Moreover, the experimental results show that integrating miRNA data can help to improve the predictive performance of DNGRGO. [ABSTRACT FROM AUTHOR]

Motivation Fungal pathogens are known to cause life threatening invasive infections with rising global mortality rates. Besides, the indiscriminate use of antifungals in both clinics and agriculture has promoted antifungal drug resistance in the last decade. Fungi can show drug resistance by a variety of mechanisms. But primary driver in all these hitherto documented mechanisms is stable and heritable point mutations in the key proteins. Therefore, cataloguing mutations that can confer resistance is the first step toward understanding the mechanisms leading to the emergence of antifungal resistance. Results In the present, work we have described a database of all the mutations responsible for antifungal resistance. Named as antifungal resistance database (AFRbase), it is better than the existing databases of antifungal resistance namely, FunResDB and MARDy which have a limited scope and inadequate information. Data of AFRbase was collected using both text mining and manual curation. AFRbase provides a separate window for visualization of mutations in the 2D and 3D formats making it easy for researchers to analyze the mutation data and ensures interoperability with other standard molecular biology databases like NCBI and UniProtKB. We hope AFRbase can be useful to both clinicians and basic biomedical scientists as we envision it as an important resource for genotypic susceptibility testing of fungi and to study/predict the course of evolution of antifungal resistance. The current version of AFRbase contains manually curated 3691 unique mutations present in 29 proteins of 32 fungal species along with the information of drugs against which resistance is caused. Availability and implementation AFRbase is an open access database available at http://proteininformatics.org/mkumar/afrbase/. [ABSTRACT FROM AUTHOR]

The emergence of massive datasets exploring the multiple levels of molecular biology has made their analysis and knowledge transfer more complex. Flexible tools to manage big biological datasets could be of great help for standardizing the usage of developed data visualizations and integration methods. Business intelligence (BI) tools have been used in many fields as exploratory tools. They have numerous connectors to link numerous data repositories with a unified graphic interface, offering an overview of data and facilitating interpretation for decision makers. BI tools could be a flexible and user-friendly way of handling molecular biological data with interactive visualizations. However, it is rather uncommon to see such tools used for the exploration of massive and complex datasets in biological fields. We believe that two main obstacles could be the reason. Firstly, we posit that the way to import data into BI tools are not compatible with biological databases. Secondly, BI tools may not be adapted to certain particularities of complex biological data, namely, the size, the variability of datasets and the availability of specialized visualizations. This paper highlights the use of five BI tools (Elastic Kibana, Siren Investigate, Microsoft Power BI, Salesforce Tableau and Apache Superset) onto which the massive data management repository engine called Elasticsearch is compatible. Four case studies will be discussed in which these BI tools were applied on biological datasets with different characteristics. We conclude that the performance of the tools depends on the complexity of the biological questions and the size of the datasets. [ABSTRACT FROM AUTHOR]

To systematically evaluate the efficacy of acupuncture in the treatment of schizophrenia.Methods: We searched China National Knowledge Infrastructure (CNKI), Wanfang Database, Chongqing VIP Chinese Science and Technology Periodical Database (VIP), China Biology Medicine Database (CBM), PubMed, Embase, Web of Science, Cochrane Library for relevant literature on the acupuncture treatment of schizophrenia published from database inception to May 17, 2023. The evaluation criteria included total effective rate, incidence of adverse reactions, TESS scale, PANSS scale, BPRS scale, SANA scale, SAPS scale. Two researchers independently screened the literature, extracted data, and assessed the risk of bias of the included studies. The RevMan 5.4 software was used for meta-analysis, risk of bias (ROB) evaluation tool was used to evaluate the risk of bias of the studies, and the GRADE evaluation tool was used to evaluate the quality of evidence. The study was registered on PROSPERO, CRD42023416438.Results: A total of 38 RCTs involving 3143 patients were included in the meta-analysis. The results showed that acupuncture can improve the total effective rate [OR=3.43 (95% CI: 2.71, 4.35), moderate credibility], reduce the incidence of adverse reactions [OR=0.45 (95% CI: 0.32, 0.63), moderate credibility], reduce the TESS score (side effect scale) [MD=− 1.83 (95% CI: − 2.94, − 0.71), very low credibility]. Acupuncture also reduced the PANSS total score [MD=− 5.75 (95% CI: − 8.08, − 3.42), very low credibility], SANA score [MD=− 2.66 (95% CI: − 6.84, 1.51), very low credibility], SAPS score [MD=− 1.26 (95% CI: − 2.55, − 0.02), very low credibility], and BPRS score [MD=− 7.02 (95% CI: − 10.59, − 3.46), very low credibility].Conclusion: Existing evidence indicates that acupuncture as an adjunctive therapy can improve the total effective rate of SZ patients, reduce the incidence of adverse reactions, improve clinical symptoms, and alleviate depression and anxiety in SZ patients. However, more high-quality clinical research evidence is still needed to support these findings. [ABSTRACT FROM AUTHOR]

Biological databases are essential resources for life science research, but finding and selecting the most relevant and up-to-date databases can be challenging due to the large number and diversity of available databases. The Nucleic Acids Research (NAR) journal publishes annual database issues that provide a comprehensive list of databases in the molecular biology domain. However, the information provided by NAR is limited and sometimes does not reflect the current status and quality of the databases. In this article, we present a web-based platform for the annotation and analysis of NAR-published databases. The platform allows users to manually curate and enrich the NAR entries with additional information such as availability, downloadability, source code links, cross-references, and duplicates. Statistics and visualizations on various aspects of the database landscape, such as recency, status, category, and curation history are also provided. Currently, it contains a total of 2,246 database entries of which 2,025 are unique with the majority updated within the last five years. Around 75% of all databases are still available and more than half provide a download option. Cross references to Database Commons are available for 1,889 entries. The platform is freely available online at https://nardbstatus.kalis-amts.de and aims to help researchers in database selection and decision-making. It also provides insights into the current state and challenges of a subset of all databases in the life sciences. [ABSTRACT FROM AUTHOR]

PX domain containing serine/threonine kinase (PXK) is a multitissue, broad-spectrum expressed gene that plays an important role in immune-related diseases. The objective of this study was to analyze the relationship between PXK and the development of acute myelocytic leukemia (AML) by using bioinformatics methods in multiple databases. The UALCAN database was utilized to conduct an online analysis of PXK expression in various subtypes of AML. LinkedOmics was utilized to analyze the correlation between the expression level of PXK and the clinicopathologic features of AML. The Kaplan-Meier Plotter database was used to analyze the mRNA expression of PXK in AML and to evaluate the effect of PXK on survival in AML. PXK interacting protein network and expression correlation were analyzed by String and GEPIA databases. The correlation between PXK gene and tumor infiltrating immune cells was analyzed by quanTIseq algorithm, and" xCell" R package was used to analyze the correlation between PXK gene expression and multiple immune checkpoints. PXK and its co-expressed genes were functionally enriched in the Metascape database to anticipate their biological roles. The findings revealed that PXK was highly expressed in various cancer tumor tissues when compared to normal tissues, and the expression level of PXK in AML was significantly up-regulated, which had an effect on the clinicopathological features, survival rate, and prognosis of AML patients, whereas the prognosis of AML patients with high PXK expression was better. PXK is highly expressed in AML tissues and is closely related to the initiation and progression of AML, which may be one of the important indicators for the diagnosis, treatment and prognosis evaluation of AML. [ABSTRACT FROM AUTHOR]

Problem: Endometriosis (EMS) is an estrogen‐dependent disease which is characterized with estrogen‐dependent growth of ectopic endometrium and increased local estrogen production. EMS performs tumor‐like biological functions such as invasiveness and angiogenesis. Rab27b is a member of the Rab family of GTPases, which is strongly associated with the growth, invasion and metastasis of a variety of tumors. However, little is known about the function of Rab27b in EMS. In this study, we intended to investigate the impact of Rab27b and its downstream molecule in the development of EMS. Method of study: Normal endometrium and endometriotic lesions were collected to investigate the expression levels of Rab27b. Then, ESCs were transfected with Rab27b siRNA. We analyzed the influence of Rab27b on the proliferation and invasive activity of ESCs. Conditioned media harvested from Rab27b siRNA‐treated ESCs were used to treat HUVECs. HUVEC Tube formation and ELISA were performed to explored the interactions between ESCs and HUVEC. In addition, ESCs were treated with different concentrations of estrogen. Based on biological database predictions, we explored possible mechanisms through which estrogen regulates the expression of Rab27b. Results: The expressions of Rab27b were significantly higher in endometriotic lesions than that in normal endometrium. Rab27b can promote the cell proliferation, migration and invasion of ESCs. The elevated expression of Rab27b, on the one hand, promotes the secretion of MMP9 and increases the invasiveness of ESCs. On the other hand, Rab27b may play a key role in the communication between ESC and endothelial cells, by simulating VEGF secretion and neovascularization. Besides, estrogen upregulated phosphorylated FOXO1 levels in ectopic ESCs, resulting in the promotion of Rab27b expression levels. Conclusion: Rab27b plays a key role in the development of EMS, which may provide new insights into the pathogenesis of EMS. Our findings may also contribute to the development of therapeutic interventions for EMS. [ABSTRACT FROM AUTHOR]

The Eukaryotic Pathogen, Vector and Host Informatics Resources (VEuPathDB), a database containing detailed molecular information on parasites and fungi that cause infectious diseases, has lost funding from the U.S. National Institute of Allergy and Infectious Diseases (NI-AID). The database, which had 46,000 unique users per month in over 150 countries, provided valuable information for researchers studying diseases like malaria and leishmaniasis. While two new platforms funded by NI-AID claim to serve the same needs, many scientists argue that they do not adequately replace VEuPathDB's capabilities. Temporary funding has been secured to keep the database online for one year, but long-term sustainability remains uncertain. [Extracted from the article]

The development of tools for the annotation of genes from newly sequenced species has not evolved much from homologous alignment to prior annotated species. While the quality of gene annotations continues to decline as we sequence and assemble more evolutionary distant gut microbiome species, machine learning presents a high quality alternative to traditional techniques. In this study, we investigate the relative performance of common classical and nonclassical machine learning algorithms in the problem of gene annotation using human microbiome-associated species genes from the KEGG database. The majority of the ensemble, clustering, and deep learning algorithms that we investigated showed higher prediction accuracy than CD-Hit in predicting partial KEGG function. Motif-based, machine-learning methods of annotation in new species were faster and had higher precision–recall than methods of homologous alignment or orthologous gene clustering. Gradient boosted ensemble methods and neural networks also predicted higher connectivity in reconstructed KEGG pathways, finding twice as many new pathway interactions than blast alignment. The use of motif-based, machine-learning algorithms in annotation software will allow researchers to develop powerful tools to interact with bacterial microbiomes in ways previously unachievable through homologous sequence alignment alone. [ABSTRACT FROM AUTHOR]

Current biological and chemical research is increasingly dependent on the reusability of previously acquired data, which typically come from various sources. Consequently, there is a growing need for database systems and databases stored in them to be interoperable with each other. One of the possible solutions to address this issue is to use systems based on Semantic Web technologies, namely on the Resource Description Framework (RDF) to express data and on the SPARQL query language to retrieve the data. Many existing biological and chemical databases are stored in the form of a relational database (RDB). Converting a relational database into the RDF form and storing it in a native RDF database system may not be desirable in many cases. It may be necessary to preserve the original database form, and having two versions of the same data may not be convenient. A solution may be to use a system mapping the relational database to the RDF form. Such a system keeps data in their original relational form and translates incoming SPARQL queries to equivalent SQL queries, which are evaluated by a relational-database system. This review compares different RDB-to-RDF mapping systems with a primary focus on those that can be used free of charge. In addition, it compares different approaches to expressing RDB-to-RDF mappings. The review shows that these systems represent a viable method providing sufficient performance. Their real-life performance is demonstrated on data and queries coming from the neXtProt project. [ABSTRACT FROM AUTHOR]

As biological sequence databases continue growing, so do the insight that they promise to shed on the shape of the genetic diversity of life. However, to fulfil this promise the software must remain usable, be able to accommodate a large amount of data and allow use of modern high performance computing infrastructure. In this study we present a reimplementation as well as an extension of a technique using indicator vectors to compute and visualize similarities between sets of nucleotide sequences. We have a flexible and easy to use python program relying on standard and open-source libraries. Our tool allows analysis of very large complement of sequences using code parallelization, as well as by providing routines to split a computational task in smaller and manageable subtasks whose results are then merged. This implementation also facilitates adding new sequences into an indicator vector-based representation without re-computing the whole set. The efficient synthesis of data into knowledge is no trivial matter given the size and rapid growth of biological sequence databases. Based on previous results regarding the properties of indicator vectors, the open-source approach proposed here efficiently and flexibly supports comparative analysis of genetic diversity at a large scale. Our software is freely available at: https://github.com/WandrilleD/pyKleeBarcode. [ABSTRACT FROM AUTHOR]

Background: The rapid development of synthetic biology relies heavily on the use of databases and computational tools, which are also developing rapidly. While many tool registries have been created to facilitate tool retrieval, sharing, and reuse, no relatively comprehensive tool registry or catalog addresses all aspects of synthetic biology. Results: We constructed SynBioTools, a comprehensive collection of synthetic biology databases, computational tools, and experimental methods, as a one-stop facility for searching and selecting synthetic biology tools. SynBioTools includes databases, computational tools, and methods extracted from reviews via SCIentific Table Extraction, a scientific table-extraction tool that we built. Approximately 57% of the resources that we located and included in SynBioTools are not mentioned in bio.tools, the dominant tool registry. To improve users' understanding of the tools and to enable them to make better choices, the tools are grouped into nine modules (each with subdivisions) based on their potential biosynthetic applications. Detailed comparisons of similar tools in every classification are included. The URLs, descriptions, source references, and the number of citations of the tools are also integrated into the system. Conclusions: SynBioTools is freely available at https://synbiotools.lifesynther.com/. It provides end-users and developers with a useful resource of categorized synthetic biology databases, tools, and methods to facilitate tool retrieval and selection. [ABSTRACT FROM AUTHOR]

Background: Chronic atrophic gastritis is a significant premalignant lesion of gastric carcinoma. There is a great need to prevent the progression to gastric carcinoma through early intervention and treatment for chronic atrophic gastritis. Weifuchun, a famous Chinese patent drug, has been widely used for chronic atrophic gastritis in China. However, it remains unclear whether Weifuchun is effective for atrophic gastritis. Objective: To determine the effectiveness and safety of Weifuchun for chronic atrophic gastritis. Methods: We systematically retrieved seven databases (Cochrane Library, EMBASE, PubMed, China National Knowledge Infrastructure, Wanfang database, Chinese Scientific Journals Database, and Chinese Biological Medical Database) from their inception to October 5, 2022. Methodological quality was examined using the Cochrane Risk of bias tool. We also used RevMan 5.4 software for statistical analysis to examine the effectiveness and safety of Weifuchun. Results: Fifteen studies with 1,488 patients were enrolled in this meta-analysis. The study indicated that Weifuchun was more effective (RR 1.52; 95% CI 1.41, 1.63; p<0.00001) than Western medicine and other Chinese patent medicine. In addition, Weifuchun was more effective in improving gastric mucosal under gastroscopy, improving histopathologic changes of gastric mucosal, and inhibiting Helicobacter pylori. However, no significant difference in safety was examined between Weifuchun and the control group (RR 2.83; 95% CI 0.85, 9.38; P = 0.09). Conclusions: The meta-analysis revealed a significant statistical difference with Weifuchun in effectiveness compared to the control group. However, there was no significant difference in safety. Thus, more high-quality clinical studies are needed in the future. Trial registration: Registration number CRD42022365703. [ABSTRACT FROM AUTHOR]

Biological variation (BV) data have many applications in laboratory medicine. However, these depend on the availability of relevant and robust BV data fit for purpose. BV data can be obtained through different study designs, both by experimental studies and studies utilizing previously analysed routine results derived from laboratory databases. The different BV applications include using BV data for setting analytical performance specifications, to calculate reference change values, to define the index of individuality and to establish personalized reference intervals. In this review, major achievements in the area of BV from last decade will be presented and discussed. These range from new models and approaches to derive BV data, the delivery of high-quality BV data by the highly powered European Biological Variation Study (EuBIVAS), the Biological Variation Data Critical Appraisal Checklist (BIVAC) and other standards for deriving and reporting BV data, the EFLM Biological Variation Database and new applications of BV data including personalized reference intervals and measurement uncertainty. [ABSTRACT FROM AUTHOR]

Shotgun metagenomic sequencing analysis is widely used for microbial profiling of biological specimens and pathogen detection. However, very little is known about the technical biases caused by the choice of analysis software and databases on the biological specimen. In this study, we evaluated different direct read shotgun metagenomics taxonomic profiling software to characterize the microbial compositions of simulated mice gut microbiome samples and of biological samples collected from wild rodents across multiple taxonomic levels. Using ten of the most widely used metagenomics software and four different databases, we demonstrated that obtaining an accurate species-level microbial profile using the current direct read metagenomics profiling software is still a challenging task. We also showed that the discrepancies in results when different databases and software were used could lead to significant variations in the distinct microbial taxa classified, in the characterizations of the microbial communities, and in the differentially abundant taxa identified. Differences in database contents and read profiling algorithms are the main contributors for these discrepancies. The inclusion of host genomes and of genomes of the interested taxa in the databases is important for increasing the accuracy of profiling. Our analysis also showed that software included in this study differed in their ability to detect the presence of Leptospira, a major zoonotic pathogen of one health importance, especially at the species level resolution. We concluded that using different databases and software combinations can result in confounding biological conclusions in microbial profiling. Our study warrants that software and database selection must be based on the purpose of the study. [ABSTRACT FROM AUTHOR]

Motivation There now exist thousands of molecular biology databases covering every aspect of biological data. This database infrastructure takes significant effort and funding to develop and maintain. The creators of these databases need to make strong justifications to funders to prove their impact or importance. There are many publication metrics and tools available such as Google Scholar to measure citation impact or AltMetrics covering multiple measures including social media coverage. Results In this article, we describe a series of novel impact metrics that have been applied initially to the UniProt database, and now made available via a Google Colab to enable any molecular biology resource to gain several additional metrics. These metrics, powered by freely available APIs from Europe PubMedCentral and SureCHEMBL cover mentions of the resource in full text articles, including which section of the paper the mention occurs in, grant acknowledgements and mentions in patent applications. This tool, that we call MBDBMetrics, is a useful adjunct to existing tools. Availability and implementation The MBDBMetrics tool is available at the following locations: https://colab.research.google.com/drive/1aEmSQR9DGQIZmHAIuQV9mLv7Mw9Ppkin and https://github.com/g-insana/MBDBMetrics. [ABSTRACT FROM AUTHOR]

Motivation Biologists increasingly turn to machine learning models not just to predict, but to explain. Feature reduction is a common approach to improve both the performance and interpretability of models. However, some biological datasets, such as microbiome data, are inherently organized in a taxonomy, but these hierarchical relationships are not leveraged during feature reduction. We sought to design a feature engineering algorithm to exploit relationships in hierarchically organized biological data. Results We designed an algorithm, called TaxaHFE, to collapse information-poor features into their higher taxonomic levels. We applied TaxaHFE to six previously published datasets and found, on average, a 90% reduction in the number of features (SD = 5.1%) compared to using the most complete taxonomy. Using machine learning to compare the most resolved taxonomic level (i.e. species) against TaxaHFE-preprocessed features, models based on TaxaHFE features achieved an average increase of 3.47% in receiver operator curve area under the curve. Compared to other hierarchical feature engineering implementations, TaxaHFE introduces the novel ability to consider both categorical and continuous response variables to inform the feature set collapse. Importantly, we find TaxaHFE's ability to reduce hierarchically organized features to a more information-rich subset increases the interpretability of models. Availability and implementation TaxaHFE is available as a Docker image and as R code at https://github.com/aoliver44/taxaHFE. [ABSTRACT FROM AUTHOR]

Motivation Positive-unlabeled data consists of points with either positive or unknown labels. It is widespread in medical, genetic, and biological settings, creating a high demand for predictive positive-unlabeled models. The performance of such models is usually estimated using validation sets, assumed to be selected completely at random (SCAR) from known positive examples. For certain metrics, this assumption enables unbiased performance estimation when treating positive-unlabeled data as positive/negative. However, the SCAR assumption is often adopted without proper justifications, simply for the sake of convenience. Results We provide an algorithm that under the weak assumptions of a lower bound on the number of positive examples can test for the violation of the SCAR assumption. Applying it to the problem of gene prioritization for complex genetic traits, we illustrate that the SCAR assumption is often violated there, causing the inflation of performance estimates, which we refer to as validation bias. We estimate the potential impact of validation bias on performance estimation. Our analysis reveals that validation bias is widespread in gene prioritization data and can significantly overestimate the performance of models. This finding elucidates the discrepancy between the reported good performance of models and their limited practical applications. Availability and implementation Python code with examples of application of the validation bias detection algorithm is available at github.com/ArtomovLab/ValidationBias. [ABSTRACT FROM AUTHOR]

Summary We describe the problem of computing local feature attributions for dimensionality reduction methods. We use one such method that is well established within the context of supervised classification—using the gradients of target outputs with respect to the inputs—on the popular dimensionality reduction technique t-SNE, widely used in analyses of biological data. We provide an efficient implementation for the gradient computation for this dimensionality reduction technique. We show that our explanations identify significant features using novel validation methodology; using synthetic datasets and the popular MNIST benchmark dataset. We then demonstrate the practical utility of our algorithm by showing that it can produce explanations that agree with domain knowledge on a SARS-CoV-2 sequence dataset. Throughout, we provide a road map so that similar explanation methods could be applied to other dimensionality reduction techniques to rigorously analyze biological datasets. Availability and implementation We have created a Python package that can be installed using the following command: pip install interpretable_tsne. All code used can be found at github.com/MattScicluna/interpretable_tsne. [ABSTRACT FROM AUTHOR]

Background: Biochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions. The resulting reaction networks can be used for multiple applications such as designing novel biosynthetic pathways and annotating untargeted metabolomics data. It is vital for these tools to provide a robust, user-friendly method to generate networks for a given application. However, existing tools lack the flexibility to easily generate networks that are tailor-fit for a user's application due to lack of exhaustive reaction rules, restriction to pre-computed networks, and difficulty in using the software due to lack of documentation. Results: Here we present Pickaxe, an open-source, flexible software that provides a user-friendly method to generate novel reaction networks. This software iteratively applies reaction rules to a set of metabolites to generate novel reactions. Users can select rules from the prepackaged JN1224min ruleset, derived from MetaCyc, or define their own custom rules. Additionally, filters are provided which allow for the pruning of a network on-the-fly based on compound and reaction properties. The filters include chemical similarity to target molecules, metabolomics, thermodynamics, and reaction feasibility filters. Example applications are given to highlight the capabilities of Pickaxe: the expansion of common biological databases with novel reactions, the generation of industrially useful chemicals from a yeast metabolome database, and the annotation of untargeted metabolomics peaks from an E. coli dataset. Conclusion: Pickaxe predicts novel metabolic reactions and compounds, which can be used for a variety of applications. This software is open-source and available as part of the MINE Database python package (https://pypi.org/project/minedatabase/) or on GitHub (https://github.com/tyo-nu/MINE-Database). Documentation and examples can be found on Read the Docs (https://mine-database.readthedocs.io/en/latest/). Through its documentation, pre-packaged features, and customizable nature, Pickaxe allows users to generate novel reaction networks tailored to their application. [ABSTRACT FROM AUTHOR]

As the number of protein sequences increases in biological databases, computational methods are required to provide accurate functional annotation with high coverage. Although several machine learning methods have been proposed for this purpose, there are still two main issues: (i) construction of reliable positive and negative training and validation datasets, and (ii) fair evaluation of their performances based on predefined experimental settings. To address these issues, we have developed ProFAB: Open Protein Functional Annotation Benchmark, which is a platform providing an infrastructure for a fair comparison of protein function prediction methods. ProFAB provides filtered and preprocessed protein annotation datasets and enables the training and evaluation of function prediction methods via several options. We believe that ProFAB will be useful for both computational and experimental researchers by enabling the utilization of ready-to-use datasets and machine learning algorithms for protein function prediction based on Gene Ontology terms and Enzyme Commission numbers. ProFAB is available at https://github.com/kansil/ProFAB and https://profab.kansil.org. [ABSTRACT FROM AUTHOR]

Developments in sequencing technologies and the sequencing of an ever-increasing number of genomes have revolutionised studies of biodiversity and organismal evolution. This accumulation of data has been paralleled by the creation of numerous public biological databases through which the scientific community can mine the sequences and annotations of genomes, transcriptomes, and proteomes of multiple species. However, to find the appropriate databases and bioinformatic tools for respective inquiries and aims can be challenging. Here, we present a compilation of DNA and protein databases, as well as bioinformatic tools for phylogenetic reconstruction and a wide range of studies on molecular evolution. We provide a protocol for information extraction from biological databases and simple phylogenetic reconstruction using probabilistic and distance methods, facilitating the study of biodiversity and evolution at the molecular level for the broad scientific community. [ABSTRACT FROM AUTHOR]

Objective To conduct a Meta-analysis comparing the effect of immediate versus delayed reconstruction after polyacrylamide hydrogel (PAHG) removal. Methods Articles regarding immediate and delayed breast reconstruction after PAHG removal, were searched in PubMed, Embase, Cochrane Library, Web of Science, Chinese journals fulltext database, WanFang database, VIP database and China biology medicine disc. EndNote X9 and Revman 5.4 were used for study inclusion and Meta-analysis. Results A total of 7 observational studies were included. No significant difference was found between immediate and delayed reconstruction in the incidence of postoperative complications, quality of life and selfesteem score after PAHG removal (P> 0.05). As for the BREASTQ evaluation, there was no significant difference in satisfaction with appearance of breasts, psychosocial wellbeing and physical wellbeing (P> 0.05). Significant difference was only found in sexual wellbeing (P < 0.05). Conclusion Immediate and delayed breast reconstruction after the removal of PAHG can both effectively improve the poor breast shape caused by the removal of filler, and appropriate timing can be selected according to the doctor's judgment and the patient's will. [ABSTRACT FROM AUTHOR]

Cassava mosaic disease (CMD) is caused by several divergent species belonging to the genus Begomovirus (Geminiviridae) transmitted by the whitefly Bemisia tabaci cryptic species group. In India and other parts of Asia, the Indian cassava mosaic virus-Kerala (ICMV-Ker) is an emergent begomovirus of cassava causing damage that results in reduced yield loss and tuber quality. Double-stranded RNA-mediated interference (RNAi) is an evolutionary conserved mechanism in eukaryotes and highly effective, innate defense system to inhibit plant viral replication and/or translation. The objective of this study was to identify and characterize cassava genome-encoded microRNAs (mes-miRNA) that are predicted to target ICMV-Ker ssDNA-encoded mRNAs, based on four in silico algorithms: miRanda, RNA22, Tapirhybrid, and psRNA. The goal is to deploy the predicted miRNAs to trigger RNAi and develop cassava plants with resistance to ICMV-Ker. Experimentally validated mature cassava miRNA sequences (n = 175) were downloaded from the miRBase biological database and aligned with the ICMV-Ker genome. The miRNAs were evaluated for base-pairing with the cassava miRNA seed regions and to complementary binding sites within target viral mRNAs. Among the 175 locus-derived mes-miRNAs evaluated, one cassava miRNA homolog, mes-miR1446a, was identified to have a predicted miRNA target binding site, at position 2053 of the ICMV-Ker genome. To predict whether the cassava miRNA might bind predicted ICMV-Ker mRNA target(s) that could disrupt viral infection of cassava plants, a cassava locus-derived miRNA–mRNA regulatory network was constructed using Circos software. The in silico-predicted cassava locus-derived mes-miRNA-mRNA network corroborated interactions between cassava mature miRNAs and the ICMV-Ker genome that warrant in vivo analysis, which could lead to the development of ICMV-Ker resistant cassava plants. [ABSTRACT FROM AUTHOR]

DNA barcoding and metabarcoding are revolutionizing the study and survey of biodiversity. In order to assign taxonomic labels to the DNA sequence data retrieved, these methods are strongly dependent on comprehensive and accurate reference databases. Producing reliable databases linking biological sequences and taxonomic data can be—and often has been—done using mainstream tools such as spreadsheet software. However, spreadsheets quickly become insufficient when the amount of data increases to thousands of taxa and sequences to be matched, and validation operations become more complex and are error prone if done in a manual way. Thus, there is a clear need for providing scientists with user‐friendly, reliable and powerful tools to manipulate and manage DNA reference databases in tractable, sound and efficient ways. Here, we introduce the R package refdb as an environment for semi‐automatic and assisted construction of DNA reference libraries. The refdb package is a reference database manager offering a set of powerful functions to import, organize, clean, filter, audit and export the data. It is broadly applicable in metabarcoding data generally obtained in biodiversity and biomonitoring studies. We present the main features of the package and outline how refdb can speed up reference database generation, management and handling, and thus contribute to standardization and repeatability in barcoding and metabarcoding studies. [ABSTRACT FROM AUTHOR]

Herein, we constructed the first sepsis biomarker knowledge database, MetaSepsisBase (http://sysbio.org.cn/sbd/), which contains biomedical information on 320 biomarkers collected from PubMed. Despite numerous studies published and many biomarkers identified, there exists an urgent need for a comprehensive database to collect, annotate, and display the information on human sepsis biomarkers [[3]]. Our goal is to collect and integrate all the reported sepsis biomarkers to provide users with comprehensive biomarker information, and assist critical care physicians in finding specific biomarkers or combined biomarkers to improve the rapid diagnosis and treatment effectiveness of human sepsis. [Extracted from the article]

At numerous phases of the computational process, pattern matching is essential. It enables users to search for specific DNA subsequences or DNA sequences in a database. In addition, some of these rapidly expanding biological databases are updated on a regular basis. Pattern searches can be improved by using high-speed pattern matching algorithms. Researchers are striving to improve solutions in numerous areas of computational bioinformatics as biological data grows exponentially. Faster algorithms with a low error rate are needed in real-world applications. As a result, this study offers two pattern matching algorithms that were created to help speed up DNA sequence pattern searches. The strategies recommended improve performance by utilizing word-level processing rather than character-level processing, which has been used in previous research studies. In terms of time cost, the proposed algorithms (EFLPM and EPAPM) increased performance by leveraging word-level processing with large pattern size. The experimental results show that the proposed methods are faster than other algorithms for short and long patterns. As a result, the EFLPM algorithm is 54% faster than the FLPM method, while the EPAPM algorithm is 39% faster than the PAPM method. [ABSTRACT FROM AUTHOR]

Functional connectivity (FC) and neural excitability may interact to affect symptoms of autism spectrum disorder (ASD). We tested this hypothesis with neural network simulations, and applied it with functional magnetic resonance imaging (fMRI). A hierarchical recurrent neural network embodying predictive processing theory was subjected to a facial emotion recognition task. Neural network simulations examined the effects of FC and neural excitability on changes in neural representations by developmental learning, and eventually on ASD-like performance. Next, by mapping each neural network condition to subject subgroups on the basis of fMRI parameters, the association between ASD-like performance in the simulation and ASD diagnosis in the corresponding subject subgroup was examined. In the neural network simulation, the more homogeneous the neural excitability of the lower-level network, the more ASD-like the performance (reduced generalization and emotion recognition capability). In addition, in homogeneous networks, the higher the FC, the more ASDlike performance, while in heterogeneous networks, the higher the FC, the less ASD-like performance, demonstrating that FC and neural excitability interact. As an underlying mechanism, neural excitability determines the generalization capability of top-down prediction, and FC determines whether the model's information processing will be topdown prediction-dependent or bottom-up sensory-input dependent. In fMRI datasets, ASD was actually more prevalent in subject subgroups corresponding to the network condition showing ASD-like performance. The current study suggests an interaction between FC and neural excitability, and presents a novel framework for computational modeling and biological application of a developmental learning process underlying cognitive alterations in ASD. [ABSTRACT FROM AUTHOR]

Saliva as a non-invasive diagnostic fluid has immense potential as a tool for early diagnosis and prognosis of patients. The information about salivary biomarkers is broadly scattered across various resources and research papers. It is important to bring together all the information on salivary biomarkers to a single platform. This will accelerate research and development in non-invasive diagnosis and prognosis of complex diseases. We collected widespread information on five types of salivary biomarkers—proteins, metabolites, microbes, micro-ribonucleic acid (miRNA) and genes found in humans. This information was collected from different resources that include PubMed, the Human Metabolome Database and SalivaTecDB. Our database SalivaDB contains a total of 15 821 entries for 201 different diseases and 48 disease categories. These entries can be classified into five categories based on the type of biomolecules; 6067, 3987, 2909, 2272 and 586 entries belong to proteins, metabolites, microbes, miRNAs and genes, respectively. The information maintained in this database includes analysis methods, associated diseases, biomarker type, regulation status, exosomal origin, fold change and sequence. The entries are linked to relevant biological databases to provide users with comprehensive information. We developed a web-based interface that provides a wide range of options like browse, keyword search and advanced search. In addition, a similarity search module has been integrated which allows users to perform a similarity search using Basic Local Alignment Search Tool and Smith–Waterman algorithm against biomarker sequences in SalivaDB. We created a web-based database—SalivaDB, which provides information about salivary biomarkers found in humans. A wide range of web-based facilities have been integrated to provide services to the scientific community. https://webs.iiitd.edu.in/raghava/salivadb/ [ABSTRACT FROM AUTHOR]