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There is a growing area of research showing that complex diseases have been found to be caused by significant genetic variants that is multiple changes to the normal genome across multiple locations Predicting the risk of these diseases is difficult due to the limited knowledge of variant causation and the leading approaches currently focus on gene disease association In this work we propose a cardiovascular disease based analysis using an enhanced indel deep neural network EI DNN comprised of two deep neural networks using novel indel variants alongside conventional variant sites to predict disease risk This model uses two deep neural networks in series the first to process indel data and the second to provide the risk score The experiments were performed on our proposed algorithm using the MGRB database and compared against a conventional PRS calculation and a single DNN algorithm The experimental results validate the effectiveness of the proposed method and highlight the capabilities with combining indel variants to enhance disease prediction

The recent progress in phenotypic information and machine learning has led to a remarkable development in the accuracy of binary seizure detection. Yet the performance of classifying specific seizure types remains suboptimal due to the limited availability of annotated data with accurate seizure type labels. Transfer learning is promising to mitigate data scarcity to improve classification accuracy on smaller datasets. However, finding the best transferable model based on the specific training and testing dataset can be a complex and repetitive process, and a single-modelled approach may not fully capture the best feature representation of the input data. Moreover, genotypic data is often neglected in previous AI-based seizure detection studies, where analyses like Polygenic Risk Scores (PRS) could offer insights into genetic predispositions to seizures. To mitigate these challenges, we propose a seizure-type classification framework incorporating a multi-model weighting system designed to assign weights to different models, thus reducing computational complexity and processing time. In addition, we carry out a PRS analysis, aiming to bridge the gap between genotypic and phenotypic data, further enhancing the comprehensiveness and precision of seizure detection. Our model outperformed similar classifiers by more than 13–16% on the Temple University Hospital EEG Seizure Corpus dataset. This study represents a pioneering examination of the multi-source transfer learning framework in the field of type-specific seizure classification.

Recent decades have seen significant advancements in contemporary genetic research with the aid of artificial intelligence (AI) techniques. However, researchers lack a comprehensive platform for fully exploiting these AI tools and conducting customized analyses. This paper introduces BiblioEngine, a literature analysis platform that helps researchers profile the research landscape and gain genetic insights into diseases. BiblioEngine integrates multiple AI-empowered data sources and employs heterogeneous network analysis to identify and emphasize genes and other biomedical entities for further investigation. Its effectiveness is demonstrated through a case study on stroke-related genetic research. Analysis with BiblioEngine uncovers valuable research intelligence and genetic insights. It provides a profile of leading research institutions and the knowledge landscape in the field. The gene co-occurrence map reveals frequent research of NOTCH3, prothrombotic factors, inflammatory cytokines, and other potential risk factors. The heterogeneous biomedical entity network analysis highlights infrequently studied genes and biomedical entities with potential significance for future stroke studies. In conclusion, BiblioEngine is a valuable tool enabling efficient navigation and comprehension of expanding biomedical knowledge from scientific literature, empowering researchers in their pursuit of disease-specific genetic knowledge.

As genomic research becomes more complex and data-rich, artificial intelligence (AI) has emerged as a crucial tool for processing and analyzing high-dimensional genomic data, accelerating biomarker discovery, and enhancing genomic sequence annotations. Despite the increasing application of AI in genomic research, challenges persist, particularly regarding the integration of biomedical knowledge into algorithm development. We reviewed high-quality, AI-driven biomedical genomic studies from the past five years, covering applications in disease prediction, detection, diagnosis, and treatment. Each category highlights how different AI techniques are applied in biomedical contexts. Furthermore, we identify current challenges and potential solutions in AI-assisted biomedical genomics. This comprehensive review is designed to encourage collaboration among computer scientists, healthcare professionals, and interested communities, propelling the development of AI applications that can be smoothly integrated into routine medical services.

Peripartum cardiomyopathy (PPCM) is a rare form of acute onset heart failure that presents in otherwise healthy pregnant women around the time of delivery. While most of these women respond to early intervention, about 20% progress to end-stage heart failure that symptomatically resembles dilated cardiomyopathy (DCM). In this study, we examined two independent RNAseq datasets from the left ventricle of end-stage PPCM patients and compared gene expression profiles to female DCM and non-failing donors. Differential gene expression, enrichment analysis and cellular deconvolution were performed to identify key processes in disease pathology. PPCM and DCM display similar enrichment in metabolic pathways and extracellular matrix remodeling suggesting these are similar processes across end-stage systolic heart failure. Genes involved in golgi vesicles biogenesis and budding were enriched in PPCM left ventricles compared to healthy donors but were not found in DCM. Furthermore, changes in immune cell populations are evident in PPCM but to a lesser extent compared to DCM, where the latter is associated with pronounced pro-inflammatory and cytotoxic T cell activity. This study reveals several pathways that are common to end-stage heart failure but also identifies potential targets of disease that may be unique to PPCM and DCM.

In this review, we present our current understanding of peripartum cardiomyopathy (PPCM) based on reports of the incidence, diagnosis and current treatment options. We summarise opinions on whether PPCM is triggered by vascular and/or hormonal causes and examine the influence of comorbidities such as preeclampsia. Two articles published in 2021 strongly support the hypothesis that PPCM may be a familial disease. Using large cohorts of PPCM patients, they summarised the available genomic DNA sequence data that are expressed in human cardiomyocytes. While PPCM is considered a disease predominately affecting the left ventricle, there are data to suggest that some cases also involve right ventricular failure. Finally, we conclude that there is sufficient evidence to warrant an RNAseq investigation and that this would be most informative if performed at the cardiomyocytes level rather than analysing genomic DNA from the peripheral circulation. Given the rarity of PPCM, the combined resources of international human heart tissue biobanks have assembled 30 ventricular tissue samples from PPCM patients, and we are actively seeking to enlarge this patient base by collaborating with human heart tissue banks and research laboratories who would like to join this endeavour.

Patients with locally advanced head and neck squamous cell carcinoma (HNSCC) may benefit from personalised treatment, requiring biomarkers that characterize the tumour and predict treatment response. We integrate pre-treatment CT radiomics and whole-transcriptome data from a multicentre retrospective cohort of 206 patients with locally advanced HNSCC treated with primary radiochemotherapy to classify tumour molecular subtypes based on radiomics, develop surrogate radiomics signatures for gene-based signatures related to different biological tumour characteristics and evaluate the potential of combining radiomics features with full-transcriptome data for the prediction of loco-regional control (LRC). Using end-to-end machine-learning, we developed and validated a model to classify tumours of the atypical subtype (AUC [95% confidence interval]: 0.69 [0.53-0.83]) based on CT imaging, observed that CT-based radiomics models have limited value as surrogates for six selected gene signatures (AUC<0.60), and showed that combining a radiomics signature with a transcriptomics signature consisting of two metagenes representing the hedgehog pathway and E2F transcriptional targets improves the prognostic value for LRC compared to both individual sources (validation C-index [95% confidence interval], combined: 0.63 [0.55-0.73] vs radiomics: 0.60 [0.50-0.71] and transcriptomics: 0.59 [0.49-0.69]). These results underline the potential of multi-omics analyses to generate reliable biomarkers for future application in personalized oncology.

(1) Background: Patients with locally advanced head and neck squamous cell carcinoma (HNSCC) who are at biologically high risk for the development of loco-regional recurrences af-ter postoperative radiotherapy (PORT) but at intermediate risk according to clinical risk factors may benefit from additional concurrent chemotherapy. In this matched-pair study, we aimed to identify a corresponding predictive gene signature. (2) Methods: Gene expression analysis was performed on a multicentre retrospective cohort of 221 patients that were treated with postoper-ative radiochemotherapy (PORT-C) and 283 patients that were treated with PORT alone. Propen-sity score analysis was used to identify matched patient pairs from both cohorts. From differen-tial gene expression analysis and Cox regression, a predictive gene signature was identified. (3) Results: 108 patient matched patient pairs were selected. We identified a 2-metagene signature that stratified patients into risk groups in both cohorts. The comparison of the high-risk patients between the two types of treatment showed higher LRC after treatment with PORT-C (p<0.001), which was confirmed by a significant interaction term in Cox regression (p=0.027), i.e. the 2-metagene signature was indicative for the type of treatment. (4) Conclusion: We have identi-fied a novel gene signature that may be helpful to identify patients with high-risk HNSCC amongst those at intermediate clinical risk treated with PORT, who may benefit from additional concurrent chemotherapy.

Purpose To assess the relation of the previously reported classification of molecular subtypes to the outcome of patients with HNSCC treated with postoperative radio(chemo)therapy (PORT-C), and to assess the association of these subtypes with gene expressions reflecting known mechanisms of radioresistance. Material and methods Gene expression analyses were performed using the GeneChip Human Transcriptome Array 2.0 on a multicentre retrospective patient cohort (N = 128) of the German Cancer Consortium Radiation Oncology Group (DKTK-ROG) with locally advanced HNSCC treated with PORT-C. Tumours were assigned to four molecular subtypes, and correlation analyses between subtypes and clinical risk factors were performed. In addition, the classifications of eight genes or gene signatures related to mechanisms of radioresistance, which have previously shown an association with outcome of patients with HNSCC, were compared between the molecular subtypes. The endpoints loco-regional control (LRC) and overall survival (OS) were evaluated by log-rank tests and Cox regression. Results Tumours were classified into the four subtypes basal (19.5%), mesenchymal (18.8%), atypical (15.6%) and classical (14.1%). The remaining tumours could not be classified (32.0%). Tumours of the mesenchymal subtype showed a lower LRC compared to the other subtypes (p = 0.012). These tumours were associated with increased epithelial-mesenchymal transition (EMT) and overexpression of a gene signature enriched in DNA repair genes. The majority of the eight considered gene classifiers were significantly associated to LRC or OS in the whole cohort. Conclusion Molecular subtypes, previously identified on HNSCC patients treated with primary radio(chemo)therapy or surgery, were related to LRC for patients treated with PORT-C, where mesenchymal tumours presented with worse prognosis. After prospective validation, subtype-based patient stratification, potentially in combination with other molecular classifiers

Purpose: The aim of this study was to develop and validate a novel gene signature from full-transcriptome data using machine-learning approaches to predict loco-regional control (LRC) of patients with human papilloma virus (HPV)-negative locally advanced head and neck squamous cell carcinoma (HNSCC), who received postoperative radio(chemo)therapy (PORT-C). Materials and methods: Gene expression analysis was performed using Affymetrix GeneChip Human Transcriptome Array 2.0 on a multicentre retrospective training cohort of 128 patients and an independent validation cohort of 114 patients from the German Cancer Consortium - Radiation Oncology Group (DKTK-ROG). Genes were filtered based on differential gene expression analyses and Cox regression. The identified gene signature was combined with clinical parameters and with previously identified genes related to stem cells and hypoxia. Technical validation was performed using nanoString technology. Results: We identified a 6-gene signature consisting of four individual genes CAV1, GPX8, IGLV3-25, TGFBI, and one metagene combining the highly correlated genes INHBA and SERPINE1. This signature was prognostic for LRC on the training data (ci = 0.84) and in validation (ci = 0.63) with a significant patient stratification into two risk groups (p = 0.005). Combining the 6-gene signature with the clinical parameters T stage and tumour localisation as well as the cancer stem cell marker CD44 and the 15-gene hypoxia-associated signature improved the validation performance (ci = 0.69, p = 0.001). Conclusion: We have developed and validated a novel prognostic 6-gene signature for LRC of HNSCC patients with HPV-negative tumours treated by PORT-C. After successful prospective validation the signature can be part of clinical trials on the individualization of radiotherapy.

RDS data of models developed and reported for the article entitled "Integrated radiogenomics analyses allow for subtype classification and improved outcome prognosis of patients with locally advanced HNSCC". Software package version is also available in repository

As developments in artificial intelligence and machine learning become more widespread in healthcare, their potential to transform clinical outcomes also increases. Peripartum cardiomyopathy is a rare and poorly-characterised condition that presents as heart failure in the last trimester prior to delivery or within 5-6 months postpartum. The lack of a definitive understanding of the molecular causes and clinical progress of this condition suggests that bibliometrics will be well-suited to creating new insights into this serious clinical problem. We examine similarities and differences between peripartum and its closely related familial dilated cardiomyopathy and idiopathic dilated cardiomyopathy. Using PubMed as the source of bibliometric data, we apply artificial intelligence-supported natural language processing to compare extracted data and genes association with these cardiomyopathies. Gene data were enhanced with additional metadata from third-party datasets and then analysed for their impact and specificity for peripartum cardiomyopathy. Artificial intelligence identified 14 genes that distinguished peripartum from both dilated and familial dilated cardiomyopathy. They are as follows: CTSD, RLN2, MMP23B*, SLC17A5, ST2*, PTHLH, CFH*, CFI, GPT, MR1, Rln1, SRI, STAT5A* and THBD. We then used the Human Protein Atlas website that uses affinity-purified rabbit polyclonal antibodies to identify genes that are expressed at the protein level (bold), or as RNA transcripts (*) in healthy human left ventricles. Additional analysis focussed on the full set of peripartum genes on linkage and specificity to cardiomyopathy yielded a different set of thirteen genes (bold font indicates those expressed in cardiomyocytes: PRL, RLN2, PLN, ST2, CTSD, F2, ACE, STAT3, TTN, SPP1, LGALS3, miR-146a, GNB3, SRI). This type of analysis can highlight new avenues for research, aimed at improving genomics-driven peripartum cardiomyopathy diagnosis as well as potential pathological and clinic

The COVID-19 pandemic constitutes an ongoing worldwide threat to human society and has caused massive impacts on global public health, the economy and the political landscape. The key to gaining control of the disease lies in understanding the genetics of SARS-CoV-2 and the disease spectrum that follows infection. This study leverages traditional and intelligent bibliometric methods to conduct a multi-dimensional analysis on 5,632 COVID-19 genetic research papers, revealing that 1) the key players include research institutions from the United States, China, Britain and Canada; 2) research topics predominantly focus on virus infection mechanisms, virus testing, gene expression related to the immune reactions and patient clinical manifestation; 3) studies originated from the comparison of SARS-CoV-2 to previous human coronaviruses, following which research directions diverge into the analysis of virus molecular structure and genetics, the human immune response, vaccine development and gene expression related to immune responses; and 4) genes that are frequently highlighted include ACE2, IL6, TMPRSS2, and TNF. Emerging genes to the COVID-19 consist of FURIN, CXCL10, OAS1, OAS2, OAS3, and ISG15. This study demonstrates that our suite of novel bibliometric tools could help biomedical researchers follow this rapidly growing field and provide substantial evidence for policymakers' decision-making on science policy and public health administration.

This article compares the expression and applicability of biomarkers, from single genes and gene signatures, identified in patients with locally advanced head and neck squamous cell carcinoma using the GeneChip Human Transcriptome Array 2.0, nCounter, and real-time PCR analyses. Two multicenter, retrospective cohorts of patients with head and neck squamous cell carcinoma from the German Cancer Consortium Radiation Oncology Group who received postoperative radiochemotherapy or primary radiochemotherapy were considered. Real-time PCR was performed for a limited number of 38 genes of the cohort who received postoperative radiochemotherapy only. Correlations between the methods were evaluated by the Spearman rank correlation coefficient. Patients were stratified based on the expression of putative cancer stem cell markers, hypoxia-associated gene signatures, and a previously developed seven-gene signature. Locoregional tumor control was compared between these patient subgroups using log-rank tests. Gene expressions obtained from nCounter analyses were moderately correlated to GeneChip analyses (median r Z approximately 0.68). A higher correlation was obtained between nCounter analyses and real-time PCR (median r Z 0.84). Significant associations with locoregional tumor control were observed for most of the considered biomarkers evaluated by GeneChip and nCounter analyses. In general, all applied biomarkers (single genes and gene signatures) classified approximately 70% to 85% of the patients similarly. Overall, gene signatures seem to be more robust and had a better transferability among different measurement methods.

Purpose or Objective: To personalize radiotherapy of locally advanced head and neck squamous cell carcinoma (HNSCC), gene signatures have been developed that are related to processes involved in radioresistance of HNSCC. To date, most of these signatures were developed for patients who have received primary radiotherapy (pRCTx). The aim of our study was (I) to apply existing gene signatures related to different radio-biological processes for patients treated with postoperative radiochemotherapy (PORT-C) and (II) to validate these signatures in xenograft models. Material and Methods: This study is based on two cohorts: (i) 128 patients with locally advanced HVP16 DNA-negative HNSCC who received PORT-C in a multicentre retrospective study of the German Cancer Consortium Radiation Oncology Group (DKTK-ROG) between 2005 and 2011 and (ii) 60 mice bearing xenografts of 10 cell lines of human squamous cell carcinoma. Gene signatures related to cancer stem cell (CSC) markers [1], DNA repair [2], hypoxia [3], radiosensitivity [4] and epithelial mesenchymal transition (EMT) [5] were selected. Whole transcriptome analysis was performed using the HTA 2.0 Array (Affymetrix). Based on the gene signatures, risk groups were generated as described in the original publications (i) for the PORT-C cohort and (ii) for the xenograft data. The primary endpoint for the PORT-C cohort was loco-regional control (LRC), while the endpoint for the xenograft data was the dose to control 50% of the tumours (TCD50). The endpoints were evaluated byCox regression and the Mann-Whitney-U test, respectively. Results: All signatures were able to stratify patients treated with PORT-C into risk groups with a significant difference in LRC (figure A) or showed a statistical trend (radiosensitivity signature). This was confirmed in multivariable Cox regression including age and T stage. In the xenograft models, the gene signatures based on cancer stem cell markers, hypoxia and radiosensitivity showed a signif

Knowledge base construction (KBC) aims to populate knowledge bases with high-quality information from unstructured data but how to effectively conduct KBC from scientific documents with limited preknowledge is still elusive. This paper proposes a KBC framework by applying computational intelligent techniques through the integration of intelligent bibliometrics—e.g., co-occurrence analysis is used for profiling research topics/domains and identifying key players, and recommending potential collaborators based on the incorporation of a link prediction approach; an approach of scientific evolutionary pathways is exploited to trace the evolution of research topics; and a search engine incorporating with fuzzy logics, word embedding, and genetic algorithm is developed for knowledge searching and ranking. Aiming to examine and demonstrate the reliability of the proposed framework, a case of gene-related cardiovascular diseases is selected, and a knowledge base is constructed, with the validation of domain experts.