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The increasing recognition of the health impacts from human exposure to per- and polyfluorinated alkyl substances (PFAS) has surged the need for sophisticated analytical techniques and advanced data analyses, especially for assessing exposure by food of animal origin. Despite the existence of nearly 15,000 PFAS listed in the CompTox chemicals dashboard by the US Environmental Protection Agency, conventional monitoring and suspect screening methods often fall short, covering only a fraction of these substances. This study introduces an innovative automated data processing workflow, named PFlow, for identifying PFAS in environmental samples using direct infusion Fourier transform ion cyclotron resonance mass spectrometry (DI-FT-ICR MS). PFlow's validation on a bream liver sample, representative of low-concentration biota, involves data pre-processing, annotation of PFAS based on their precursor masses, and verification through isotopologues. Notably, PFlow annotated 17 PFAS absent in the comprehensive targeted approach and tentatively identified an additional 53 compounds, thereby demonstrating its efficiency in enhancing PFAS detection coverage. From an initial dataset of 30,332 distinct m/z values, PFlow thoroughly narrowed down the candidates to 84 potential PFAS compounds, utilizing precise mass measurements and chemical logic criteria, underscoring its potential in advancing our understanding of PFAS prevalence and of human exposure. [ABSTRACT FROM AUTHOR]

With over 450 genes, solute carriers (SLCs) constitute the largest transporter superfamily responsible for the uptake and efflux of nutrients, metabolites, and xenobiotics in human cells. SLCs are associated with a wide variety of human diseases, including cancer, diabetes, and metabolic and neurological disorders. They represent an important therapeutic target class that remains only partly exploited as therapeutics that target SLCs are scarce. Additionally, many small molecules reported in the literature to target SLCs are poorly characterized. Both features may be due to the difficulty of developing SLC transport assays that fulfill the quality criteria for high-throughput screening. Here, we report one of the main limitations hampering assay development within the RESOLUTE consortium: the lack of a resource providing high-quality information on SLC tool compounds. To address this, we provide a systematic annotation of tool compounds targeting SLCs. We first provide an overview on RESOLUTE assays. Next, we present a list of SLC-targeting compounds collected from the literature and public databases; we found that most data sources lacked specificity data. Finally, we report on experimental tests of 19 selected compounds against a panel of 13 SLCs from seven different families. Except for a few inhibitors, which were active on unrelated SLCs, the tested inhibitors demonstrated high selectivity for their reported targets. To make this knowledge easily accessible to the scientific community, we created an interactive dashboard displaying the collected data in the RESOLUTE web portal (https://re-solute.eu). We anticipate that our openaccess resources on assays and compounds will support the development of future drug discovery campaigns for SLCs. [ABSTRACT FROM AUTHOR]

• Using the FDA adverse event reporting system database, ETI and DILI reports were found to be significantly associated (p < 0.05) for all disproportionality measures (PRR, ROR, IC, EGBM, chi-squared). • The ROR for ETI-DILI is greater than that of ∼10 % of the "Most-DILI concern" drugs in the FDA DILIRank dataset. • ETI-DILI reports for people with cystic fibrosis were predominately male, in contrast to patient reports for other drugs and DILI. • "Hospitalization" was the second most common patient outcome for ETI-DILI after "other serious outcomes". • Most people with cystic fibrosis experienced onset times within 3 months of initiation; however, several occurred prior to 3 months or beyond 1 year, indicating additional monitoring may be prudent. The efficacy and safety of elexacaftor/tezacaftor/ivacaftor (ETI) have been established in prospective clinical trials. Liver function test elevations were observed in a greater proportion of patients receiving ETI compared with placebo; however, the relatively small number of patients and short duration of study preclude detection of rare but clinically significant associations with drug-induced liver injury (DILI). To address this gap, we assessed the real-world risk of DILI associated with ETI through data mining of the FDA adverse event reporting system (FAERS). Disproportionality analyses were conducted on FAERS data from the fourth quarter of 2019 through the third quarter of 2022. Comparative patient demographics, onset time and outcomes for ETI-DILI were also obtained. 452 reports of DILI associated with ETI were found, representing 2.1 % of all adverse event reports for ETI. All disproportionality measures were significant for ETI-DILI at p < 0.05; the reporting odds ratio (ROR) (2.82) was comparable to that of drugs classified by FDA as "Most-DILI concern". The most notable demographic finding was a male majority (5:4 male to female ratio) for ETI-DILI compared to a female majority (4:5 male to female ratio) for non ETI-DILI. Median ETI-DILI onset time was 50.5 days, and hospitalization was the second most common complication. Using FAERS data, ETI was found to be disproportionately associated with DILI. Future research is needed to investigate the hepatotoxic mechanisms and assess potential mitigation strategies for ETI-induced hepatotoxicity. [ABSTRACT FROM AUTHOR]

Inhibitors of DNA methyltransferase (DNMTs) are now a major family of epigenetic targets with therapeutic interest. However, only two cytosine analogues 5-azacytosine (azacytidine) and 20-deoxy-5-azacytidine (decitabine), have been approved as the most cutting-edge medications for treating epigenetic cancer with some restrictions. In this context, computational methods that rely on quantitative structure-activity relationship (QSAR) play a crucial role allowing us to predict the biological activity of potential molecules based on the theoretically calculated physicochemical properties of these compounds. When coupled with machine learning (ML), QSAR approaches create an ideal platform for discovering potential drug candidates. In this study, three Machine Learning (ML) models; Random Forest, Support Vector Machine, and Artificial Neural Network, were trained using modified TeachOpenCADD KNIME workflows and applied it to the identification of plant molecules that are structurally similar to the active pharmaceuticals of current DNMT inhibitors. Then molecular docking simulations were performed using AutoDock Vina, employing two human DNMT structures (PDB codes: 4WXX and 2QRV) as target proteins and the predicted phytochemicals as ligands. Additionally, we focused on the R882H mutation hotspot in the catalytic domain of DNMT3A, which is associated with aberrant DNA methylation in acute myeloid leukemia (AML). Consequently, the structure of R882H DNMT3A (PDB code: 6W8J) was docked with the identified novel ligands. As a result of our computational analysis, eight phytochemicals were predicted as potential DNMT inhibitors through the ML approaches from KNIME. Subsequently, three of these phytochemicals, namely Herbacetin, Kaempferide, and Morin were identified as virtual hits against DNMTs following the molecular docking simulations. Overall, our study demonstrates the effectiveness of this computational strategy in identifying DNMT inhibitors. These findings hold promise for the discovery of potent and selective anticancer drugs targeting DNMTs. [ABSTRACT FROM AUTHOR]

The rapid growth of the Internet of Things (IoT) and digital industrial devices has significantly impacted various aspects of life, underscoring the importance of the Industrial Internet of Things (IIoT). Given its importance in industrial contexts that affect human life, the IIoT represents a key subset of the broader IoT landscape. Due to the proliferation of sensors in smart devices, which are viewed as points of contact, as the gathering of data and information regarding the IIoT systems and devices operating on the IoT, there is an urgent requirement for developing effective security methods to counter such threats as well as protecting IIoT systems. In this study, we develop and evaluate a well-optimized intrusion detection system (IDS) based on deep learning (DL) and machine learning (ML) techniques to enhance IIoT security. Leveraging the Edge-IIoTset dataset, specifically designed for IIoT cybersecurity evaluations, we focus on detecting and mitigating 14 distinct attack types targeting IIoT and IoT protocols. These attacks are categorized into five threat groups: information collection, malware, DDoS, manin-the-middle attacks, and injection attacks. We conducted experiments using machine learning algorithms (knearest neighbors, decision tree) and a neural network on the KNIME platform, achieving a remarkable 100% accuracy with the decision tree model. This high accuracy demonstrates the effectiveness of our approach in protecting industrial IoT networks. [ABSTRACT FROM AUTHOR]

Molecular features play an important role in different bio‐chem‐informatics tasks, such as the Quantitative Structure–Activity Relationships (QSAR) modeling. Several pre‐trained models have been recently created to be used in downstream tasks, either by fine‐tuning a specific model or by extracting features to feed traditional classifiers. In this regard, a new family of Evolutionary Scale Modeling models (termed as ESM‐2 models) was recently introduced, demonstrating outstanding results in protein structure prediction benchmarks. Herein, we studied the usefulness of the different‐dimensional embeddings derived from the ESM‐2 models to classify antimicrobial peptides (AMPs). To this end, we built a KNIME workflow to use the same modeling methodology across experiments in order to guarantee fair analyses. As a result, the 640‐ and 1280‐dimensional embeddings derived from the 30‐ and 33‐layer ESM‐2 models, respectively, are the most valuable since statistically better performances were achieved by the QSAR models built from them. We also fused features of the different ESM‐2 models, and it was concluded that the fusion contributes to getting better QSAR models than using features of a single ESM‐2 model. Frequency studies revealed that only a portion of the ESM‐2 embeddings is valuable for modeling tasks since between 43% and 66% of the features were never used. Comparisons regarding state‐of‐the‐art deep learning (DL) models confirm that when performing methodologically principled studies in the prediction of AMPs, non‐DL based QSAR models yield comparable‐to‐superior performances to DL‐based QSAR models. The developed KNIME workflow is available‐freely at https://github.com/cicese-biocom/classification-QSAR-bioKom. This workflow can be valuable to avoid unfair comparisons regarding new computational methods, as well as to propose new non‐DL based QSAR models. [ABSTRACT FROM AUTHOR]

Plant-derived natural compounds are significant resources for the discovery of potential anticancer drugs. While research in the plant-based anticancer field has surged in recent years, systematic bibliometric analyses covering a longer period and containing up-to-date publications remain scarce. Here, we conducted a bibliometric analysis of literature on the anticancer properties of plant natural compounds over the past three decades, leveraging the bibliometric framework and open-access platform, KNIME. Our findings showed that the number of plant anticancer-related publications underwent an accelerating growth from 1992 to 2023. The country and institution analyses revealed that countries with traditional medical systems contributed a large portion of publications in the plant anticancer field, such as India, China, and South Korea. This study also highlighted the top ten eminent researchers and publications, assisting researchers in identifying pivotal literature. The primary publications were domains of chemistry and biology-related fields, such as Pharmacology & Pharmacy, Plant Sciences, and Biochemistry & Molecular Biology. Additionally, we noted that flavonoids have been focal plant compounds in anticancer, with strong anticancer potential. Our study provides new insights into the progress and trends in the plant anticancer field and will assist researchers in grasping the future research direction.

With over 450 genes, solute carriers (SLCs) constitute the largest transporter superfamily responsible for the uptake and efflux of nutrients, metabolites, and xenobiotics in human cells. SLCs are associated with a wide variety of human diseases, including cancer, diabetes, and metabolic and neurological disorders. They represent an important therapeutic target class that remains only partly exploited as therapeutics that target SLCs are scarce. Additionally, many small molecules reported in the literature to target SLCs are poorly characterized. Both features may be due to the difficulty of developing SLC transport assays that fulfill the quality criteria for high-throughput screening. Here, we report one of the main limitations hampering assay development within the RESOLUTE consortium: the lack of a resource providing high-quality information on SLC tool compounds. To address this, we provide a systematic annotation of tool compounds targeting SLCs. We first provide an overview on RESOLUTE assays. Next, we present a list of SLC-targeting compounds collected from the literature and public databases; we found that most data sources lacked specificity data. Finally, we report on experimental tests of 19 selected compounds against a panel of 13 SLCs from seven different families. Except for a few inhibitors, which were active on unrelated SLCs, the tested inhibitors demonstrated high selectivity for their reported targets. To make this knowledge easily accessible to the scientific community, we created an interactive dashboard displaying the collected data in the RESOLUTE web portal (https://re-solute.eu). We anticipate that our open-access resources on assays and compounds will support the development of future drug discovery campaigns for SLCs.

Abstract The rapid increase of publicly available chemical structures and associated experimental data presents a valuable opportunity to build robust QSAR models for applications in different fields. However, the common concern is the quality of both the chemical structure information and associated experimental data. This is especially true when those data are collected from multiple sources as chemical substance mappings can contain many duplicate structures and molecular inconsistencies. Such issues can impact the resulting molecular descriptors and their mappings to experimental data and, subsequently, the quality of the derived models in terms of accuracy, repeatability, and reliability. Herein we describe the development of an automated workflow to standardize chemical structures according to a set of standard rules and generate two and/or three-dimensional “QSAR-ready” forms prior to the calculation of molecular descriptors. The workflow was designed in the KNIME workflow environment and consists of three high-level steps. First, a structure encoding is read, and then the resulting in-memory representation is cross-referenced with any existing identifiers for consistency. Finally, the structure is standardized using a series of operations including desalting, stripping of stereochemistry (for two-dimensional structures), standardization of tautomers and nitro groups, valence correction, neutralization when possible, and then removal of duplicates. This workflow was initially developed to support collaborative modeling QSAR projects to ensure consistency of the results from the different participants. It was then updated and generalized for other modeling applications. This included modification of the “QSAR-ready” workflow to generate “MS-ready structures” to support the generation of substance mappings and searches for software applications related to non-targeted analysis mass spectrometry. Both QSAR and MS-ready workflows are freely available in KNIME, via standalone versions on GitHub, and as docker container resources for the scientific community. Scientific contribution: This work pioneers an automated workflow in KNIME, systematically standardizing chemical structures to ensure their readiness for QSAR modeling and broader scientific applications. By addressing data quality concerns through desalting, stereochemistry stripping, and normalization, it optimizes molecular descriptors' accuracy and reliability. The freely available resources in KNIME, GitHub, and docker containers democratize access, benefiting collaborative research and advancing diverse modeling endeavors in chemistry and mass spectrometry.

The rapid increase of publicly available chemical structures and associated experimental data presents a valuable opportunity to build robust QSAR models for applications in different fields. However, the common concern is the quality of both the chemical structure information and associated experimental data. This is especially true when those data are collected from multiple sources as chemical substance mappings can contain many duplicate structures and molecular inconsistencies. Such issues can impact the resulting molecular descriptors and their mappings to experimental data and, subsequently, the quality of the derived models in terms of accuracy, repeatability, and reliability. Herein we describe the development of an automated workflow to standardize chemical structures according to a set of standard rules and generate two and/or three-dimensional "QSAR-ready" forms prior to the calculation of molecular descriptors. The workflow was designed in the KNIME workflow environment and consists of three high-level steps. First, a structure encoding is read, and then the resulting in-memory representation is cross-referenced with any existing identifiers for consistency. Finally, the structure is standardized using a series of operations including desalting, stripping of stereochemistry (for two-dimensional structures), standardization of tautomers and nitro groups, valence correction, neutralization when possible, and then removal of duplicates. This workflow was initially developed to support collaborative modeling QSAR projects to ensure consistency of the results from the different participants. It was then updated and generalized for other modeling applications. This included modification of the "QSAR-ready" workflow to generate "MS-ready structures" to support the generation of substance mappings and searches for software applications related to non-targeted analysis mass spectrometry. Both QSAR and MS-ready workflows are freely available in KNIME, via standalone versions on GitHub, and as docker container resources for the scientific community. Scientific contribution: This work pioneers an automated workflow in KNIME, systematically standardizing chemical structures to ensure their readiness for QSAR modeling and broader scientific applications. By addressing data quality concerns through desalting, stereochemistry stripping, and normalization, it optimizes molecular descriptors' accuracy and reliability. The freely available resources in KNIME, GitHub, and docker containers democratize access, benefiting collaborative research and advancing diverse modeling endeavors in chemistry and mass spectrometry. [ABSTRACT FROM AUTHOR]

Background: Epidemiological outbreak investigations often prove to be lengthy and inconclusive due to the time-consuming nature of the currently-used approaches. An alternative approach to address these challenges could involve the application of algorithms to support authorities and food business operators by providing timely, relevant and reliable information. Algorithms, such as gravity models, could be applied as commodity trade models, but they require a large amount of reliable and consistent data on trade for generating projections at international, national or even regional level. Several trade databases, such as UN COMTRADE, EUROSTAT, BACI, CHELEM and GTAP, provide information on trace, albeit with variations in the provided information and in the structure. However, it is worth noting that not all of these databases are freely accessible and data management can pose challenges, hampering the access to the trade data. WarenstromInfo (WI) was created as a software solution that allows easy trade data extraction and visualisation for application in different areas, such as outbreak investigations. New information: WarenstromInfo (WI) is an application tool that automatically extracts, decodes and visually displays trade flow data from EUROSTAT "EU trade since 2002 by statistical procedure, by HS2-4-6 and CN8 (DS-059322)" (hereinafter referred to EUROSTAT) and the BACI databases, based on user input. New information WarenstromInfo (WI) is an application tool that automatically extracts, decodes and visually displays trade flow data from EUROSTAT "EU trade since 2002 by statistical procedure, by HS2-4-6 and CN8 (DS-059322)" (hereinafter referred to EUROSTAT) and the BACI databases, based on user input. WI was developed by using the open-source desktop software KNIME Analytics Platform. WI offers the flexibility to be executed either as a web service on a KNIME Web Server infrastructure or as a local resource. To integrate the BACI database into WI, the database is annually downloaded as csv files, rebuilt as a SQLite database and hosted locally into the KNIME Web Server Infrastructure. In order to optimise storage space on the KNIME server, this SQLite database specifically includes only agrifood data, reflecting the tool's focus. However, if new objectives are established, this database can be expanded. Further, data of the SQLite database can be customised by executing the WI workflow locally, enabling the user to expand the database at any time. In contrast to BACI, trade data extraction from the EUROSTAT database is performed via the EUROSTAT's API (Application Programming Interface) applying GET requests and XML data management. WI displays four User-friendly Interfaces (UIs) designed with interactive KNIME nodes that facilitate the input of variables. The extracted trade flow data are shown through interactive tables directly within the UIs. This feature enables users to easily explore the data in a structured and user-friendly manner. Additionally, WI incorporates the extracted trade flow data into maps. These maps provide a visual representation of the data, allowing users to gain insights and identify patterns and trends geographically. Both, the data table and the maps, can be downloaded as a single Excel file (containing multiple preformatted tables) and as png files, respectively. New information WarenstromInfo (WI) is an application tool that automatically extracts, decodes and visually displays trade flow data from EUROSTAT "EU trade since 2002 by statistical procedure, by HS2-4-6 and CN8 (DS-059322)" (hereinafter referred to EUROSTAT) and the BACI databases, based on user input. WI was developed by using the open-source desktop software KNIME Analytics Platform. WI offers the flexibility to be executed either as a web service on a KNIME Web Server infrastructure or as a local resource. To integrate the BACI database into WI, the database is annually downloaded as csv files, rebuilt as a SQLite database and hosted locally into the KNIME Web Server Infrastructure. In order to optimise storage space on the KNIME server, this SQLite database specifically includes only agrifood data, reflecting the tool's focus. However, if new objectives are established, this database can be expanded. Further, data of the SQLite database can be customised by executing the WI workflow locally, enabling the user to expand the database at any time. In contrast to BACI, trade data extraction from the EUROSTAT database is performed via the EUROSTAT's API (Application Programming Interface) applying GET requests and XML data management. WI displays four User-friendly Interfaces (UIs) designed with interactive KNIME nodes that facilitate the input of variables. The extracted trade flow data are shown through interactive tables directly within the UIs. This feature enables users to easily explore the data in a structured and user-friendly manner. Additionally, WI incorporates the extracted trade flow data into maps. These maps provide a visual representation of the data, allowing users to gain insights and identify patterns and trends geographically. Both, the data table and the maps, can be downloaded as a single Excel file (containing multiple preformatted tables) and as png files, respectively. WI was developed by using the open-source desktop software KNIME Analytics Platform. WI offers the flexibility to be executed either as a web service on a KNIME Web Server infrastructure or as a local resource. To integrate the BACI database into WI, the database is annually downloaded as csv files, rebuilt as a SQLite database and hosted locally into the KNIME Web Server Infrastructure. In order to optimise storage space on the KNIME server, this SQLite database specifically includes only agrifood data, reflecting the tool's focus. However, if new objectives are established, this database can be expanded. Further, data of the SQLite database can be customised by executing the WI workflow locally, enabling the user to expand the database at any time. In contrast to BACI, trade data extraction from the EUROSTAT database is performed via the EUROSTAT's API (Application Programming Interface) applying GET requests and XML data management. WI displays four User-friendly Interfaces (UIs) designed with interactive KNIME nodes that facilitate the input of variables. The extracted trade flow data are shown through interactive tables directly within the UIs. This feature enables users to easily explore the data in a structured and user-friendly manner. Additionally, WI incorporates the extracted trade flow data into maps. These maps provide a visual representation of the data, allowing users to gain insights and identify patterns and trends geographically. Both, the data table and the maps, can be downloaded as a single Excel file (containing multiple preformatted tables) and as png files, respectively. [ABSTRACT FROM AUTHOR]

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Artificial intelligence (AI) has huge potential to accelerate drug discovery, but challenges remain in implementing AI algorithms that can be used by the broad scientific community. Identification of molecular features and their subsequent use in training of machine learning models may permit prediction of new molecules with enhanced properties. Predictive modeling is particularly applicable to analysis of structure-activity relationships (SARs) and would be a useful tool in the hands of laboratory medicinal chemists. This requires a software platform that is chemically intuitive while providing the user with access to AI methods. The KNIME platform provides such an environment through inclusion of broad chemical toolsets and a user-friendly approach for utilization of machine learning for analysis of SAR data. Here, we illustrate use of KNIME for this purpose, with a focus on discovery of features of highly potent tau inhibitors from a series of structurally diverse polyphenols. Workflows are described that enable implementation of AI tools in KNIME for diverse SAR projects.

Frauds accounted for significant losses in the financial sector and emerged as the industry's biggest challenge. Companies invest significant amounts to prevent such fraud. It has been reported that 63.6% of the financial institutions that use Automated Fraud prevention methods successfully prevented frauds before their occurrence. Some estimations suggest that 80% of specialists are confident in cutting down fraud using Artificial Intelligence (AI)-based platforms. Several research studies have also administered AI-based techniques for fraud prevention. This study takes a systematic literature review approach to uncover the emerging areas of fraud detection using AI. The authors have analyzed 241 research articles published in the last 20 years. The Scopus database was the source of the articles in the literature review. The meta-analysis and network analysis were carried out, and the output shows the up trend of this research domain. Author-coauthor network collaboration is analyzed using the VOSviewer tool. K-means clustering was performed to identify the critical research domain, and future research areas were also identified. This research will act as a reference for future scholars who want to perform analysis on the application of AI techniques in financial fraud detection and prevention. We finally conclude the study by identifying the scope of future research and will be a value addition for financial fraud researchers. [ABSTRACT FROM AUTHOR]

This paper portrays the topic modeling approach in identifying critical facets in new Library and Information Science (LIS) research. Using machine language, topic modeling in statistics recognizes the "topics" in a document pool. Latent Dirichlet Allocation (LDA) is one such model for topic analysis and thus is used to classify text in the literary warrant in LIS in various locations worldwide. The study analyzed 734 articles published in 4 different LIS journals, i.e., Annals of Library and Information Studies (Indian), DESIDOC Journal of Library and Information Technology (DJLIT-Indian), College and Research Libraries (International), Liber Quarterly (International) from 2016 to 2020. LDA is used to analyze and manage the dataset. The study pivoted on the foundation of extracting 'topics' from these four journals using Mallet and Knime software to determine what research topics are essential at the national and global levels. The paper also visualizes a relationship map of distributed issues at the national and international levels to identify the sublime contexture of keyed-in tags. Furthermore, the study took a socio-cultural approach to LIS research with cross-country comparisons. The study found that the compared topics retrieved from both journals published in India and abroad where non-Indian journals varied significantly from those published in Indian journals. The study concluded that cross-cultural adoption is visible in the research activities over time. Moreover, this study identified the social parameters that describe the changes in the discipline.

The key hallmark of a digitally minded organisation today is seen in their rapid advancement, globalisation, innovation and resilience to change. Companies that wish to thrive must be prepared to adapt to the new digital reality. Being digitally minded does not mean implementing new technology, investing in tools and upgrading current systems. These stages are critical, but they are not the entire picture. If a company wants to remain competitive, it must not just be able to adapt to changes, but also anticipate and drive innovation. Companies must plan ahead and be proactive architects of their future in order to achieve this vision. This is where a digital transformation strategy is crucial. A digital transformation strategy assists organisational leadership in addressing challenges about their business, such as the present level of digitisation and a digital maturity roadmap. Although diverse data capturing technologies and data-generating assets exist, material/chemical science domains, such as R&D and Manufacturing groups, struggle to harness the full power of their data. A typical industry will have significant data sources generating large amounts of data stored in siloed databases with minimal to non-existent cross-talk. This in part creates scenarios for researchers to be able to perform a deep dive in one set of data, but unable to co-populate and harness the interdependences or relationships amongst the different datasets. This paper seeks to define, distinguish, aggregate and propose an integrative approach to utilising the various types of disparate data sources commonly encountered by researchers in the field of their material science research. The main focus here is defining strategies to harness insights across integrative data to aid in efficient research in R&D organisations as these industries seek to embrace the power of digital transformation. Although the principles described here relate to industries in the applied science domain, the general strategies proposed can be applied to other industries on a case-by-case basis. [ABSTRACT FROM AUTHOR]

Vascular endothelial growth factors (VEGFs) are specific cytokines involved in angiogenesis and do so via binding to vascular endothelial growth factor receptors (VEGFRs), a type of receptor tyrosine kinase. VEGFs are reported to facilitate angiogenesis in physiological (embryogenesis) and pathological (tumor) conditions. The overexpression of VEGFs and consequently VEGFRs is reported in tumorigenic conditions. Several VEGFR inhibitors currently used as anticancer drugs to prevent angiogenesis are sunitinib, sorafenib, etc. To identify new potential candidates as VEGFR inhibitors, a classification study using a large and diverse dataset of VEGFR inhibitors from the BindingDB database has been conducted. The KNIME platform was used to calculate molecular and fingerprint‐based descriptors and several classification algorithms viz. linear regression (LR), k‐nearest neighbor (kNN), decision tree (DT), random forest (RF), and gradient boosted tree (GBT) were employed to build the classification model. The model performance was evaluated by accuracy, precision, recall, and F1 score of the test set. The best LR, kNN, DT, RF, and GBT classifiers had the F1 score of 0.81, 0.87, 0.82, 0.87, and 0.87, respectively. The assorted 5120 VEGFR inhibitors were clustered into 10 subsets, and the structural features of each subset were assessed along with the identification of significant fragments in active and inactive compounds. The automated classifier model developed using the KNIME platform could serve as an important platform for screening and designing molecules as VEGFR inhibitors. [ABSTRACT FROM AUTHOR]

This paper portrays the topic modeling approach in identifying critical facets in new Library and Information Science (LIS) research. Using machine language, topic modeling in statistics recognizes the "topics" in a document pool. Latent Dirichlet Allocation (LDA) is one such model for topic analysis and thus is used to classify text in the literary warrant in LIS in various locations worldwide. The study analyzed 734 articles published in 4 different LIS journals, i.e., Annals of Library and Information Studies (Indian), DESIDOC Journal of Library and Information Technology (DJLIT-Indian), College and Research Libraries (International), Liber Quarterly (International) from 2016 to 2020. LDA is used to analyze and manage the dataset. The study pivoted on the foundation of extracting 'topics' from these four journals using Mallet and Knime software to determine what research topics are essential at the national and global levels. The paper also visualizes a relationship map of distributed issues at the national and international levels to identify the sublime contexture of keyed-in tags. Furthermore, the study took a socio-cultural approach to LIS research with cross-country comparisons. The study found that the compared topics retrieved from both journals published in India and abroad where non-Indian journals varied significantly from those published in Indian journals. The study concluded that cross-cultural adoption is visible in the research activities over time. Moreover, this study identified the social parameters that describe the changes in the discipline. [ABSTRACT FROM AUTHOR]

Exposure to air pollutants of fine particulate matter (PM2.5) is a major threat to human health. Research on PM2.5 exposure has been on the rise in recent years, but there has been a lack of systematic literature analysis in this field. To address this research gap, we conducted a bibliometric analysis of the existing publications on PM2.5 exposure over the past three decades and analyzed the current state and historical trends of research using the open-access tool, KNIME. Our results show that the number of publications on PM2.5 exposure has been increasing at an accelerating rate over the last 30 years. The authors of the publications are primarily from developed nations, such as countries in North America, Europe, East Asia, and Oceania. Published research is mainly from the fields of Environmental Sciences and Ecology, Environmental Sciences, and Public, Environmental and Occupational Health. The main research hotspots are exposure, air pollution, and PM2.5. Research is trending toward the intersection and integration of multiple subjects. Our results highlight the rapid growth of PM2.5-related research and provide insights into the structure, impact, and trends of this interdisciplinary field. [ABSTRACT FROM AUTHOR]

Dissemination of novel research methods, especially in the form of chemoinformatics software, depends heavily on their ease of applicability for non‐expert users with only a little or no programming skills and knowledge in computer science. Visual programming has become widely popular over the last few years, also enabling researchers without in‐depth programming skills to develop tailored data processing pipelines using elements from a repository of predefined standard procedures. In this work, we present the development of a set of nodes for the KNIME platform implementing the QPhAR algorithm. We show how the developed KNIME nodes can be included in a typical workflow for biological activity prediction. Furthermore, we present best‐practice guidelines that should be followed to obtain high‐quality QPhAR models. Finally, we show a typical workflow to train and optimise a QPhAR model in KNIME for a set of given input compounds, applying the discussed best practices. [ABSTRACT FROM AUTHOR]

The combination of the quantitative turn in linguistics and the emergence of text analytics has created a demand for new methodological skills among linguists and data scientists. This paper introduces KNIME as a low-code programming platform for linguists interested in learning text analytic methods, while highlighting the considerations necessary from a linguistics standpoint for data scientists. Examples from an Open Educational Resource created for the DiMPAH project are used to demonstrate KNIME's value as a low-code option for text analysis, using sentiment analysis and topic modelling as examples. The paper provides detailed step-by-step descriptions of the workflows for both methods, showcasing how these methods can be applied without writing code. The results suggest that visual or low-code programming tools are useful as an introduction for linguists and humanities scholars who wish to gain an understanding of text analytic workflows and computational thinking. However, as with more traditional programming, caution must be exercised when using methods without fully understanding them. In conclusion, KNIME is a potential avenue for innovative research and teaching computational methods to humanities scholars. [ABSTRACT FROM AUTHOR]

Due to the rapid economic growth of the real estate sector, the prediction of housing prices is important for future planning. The aim of this study is to predict the potential price of a house using machine learning methods. As machine learning methods, artificial neural networks and polynomial regression were used and their prediction performances were compared. KNIME data analysis platform was used to apply machine learning methods. R Squared performance metric was used to measure the success of the methods. The application results show that the artificial neural network method predicts house prices with higher accuracy than the Polynomial regression method. It is thought that the study will contribute to the development of applications used for home evaluation and to scientific studies in this field. In future studies, it is aimed to expand the study by using different methods or data sets with home attributes. [ABSTRACT FROM AUTHOR]

SNOMED CT has an enormous number of clinical concepts and mapping to SNOMED CT is considered as the foundation to achieve semantic interoperability in healthcare. Manual mapping is time-consuming and error-prone thus making this crucial step challenging. In addition, hierarchy retrieval of clinical concepts increases the challenges for the user. Terminology Servers provide an interface, which can be used to automate the process of retrieving data. In this work, it is shown that Snowstorm can significantly improve the efficiency of retrieval process if used with semi-automated workflows. [ABSTRACT FROM AUTHOR]

Abstract Background Integration of medicinal chemistry data from numerous public resources is an increasingly important part of academic drug discovery and translational research because it can bring a wealth of important knowledge related to compounds in one place. However, different data sources can report the same or related compounds in various forms (e.g., tautomers, racemates, etc.), thus highlighting the need of organising related compounds in hierarchies that alert the user on important bioactivity data that may be relevant. To generate these compound hierarchies, we have developed and implemented canSARchem, a new compound registration and standardization pipeline as part of the canSAR public knowledgebase. canSARchem builds on previously developed ChEMBL and PubChem pipelines and is developed using KNIME. We describe the pipeline which we make publicly available, and we provide examples on the strengths and limitations of the use of hierarchies for bioactivity data exploration. Finally, we identify canonicalization enrichment in FDA-approved drugs, illustrating the benefits of our approach. Results We created a chemical registration and standardization pipeline in KNIME and made it freely available to the research community. The pipeline consists of five steps to register the compounds and create the compounds’ hierarchy: 1. Structure checker, 2. Standardization, 3. Generation of canonical tautomers and representative structures, 4. Salt strip, and 5. Generation of abstract structure to generate the compound hierarchy. Unlike ChEMBL’s RDKit pipeline, we carry out compound canonicalization ahead of getting the parent structure, similar to PubChem’s OpenEye pipeline. canSARchem has a lower rejection rate compared to both PubChem and ChEMBL. We use our pipeline to assess the impact of grouping the compounds in hierarchies for bioactivity data exploration. We find that FDA-approved drugs show statistically significant sensitivity to canonicalization compared to the majority of bioactive compounds which demonstrates the importance of this step. Conclusions We use canSARchem to standardize all the compounds uploaded in canSAR (> 3 million) enabling efficient data integration and the rapid identification of alternative compound forms with useful bioactivity data. Comparison with PubChem and ChEMBL pipelines evidenced comparable performances in compound standardization, but only PubChem and canSAR canonicalize tautomers and canSAR has a slightly lower rejection rate. Our results highlight the importance of compound hierarchies for bioactivity data exploration. We make canSARchem available under a Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0) at https://gitlab.icr.ac.uk/cansar-public/compound-registration-pipeline .

This paper describes the design, development, and testing of a general-purpose scientific-workflows tool for spatial analytics. Spatial analytics processes are frequently complex, both conceptually and computationally. Adaptation, documention, and reproduction of bespoke spatial analytics procedures represents a growing challenge today, particularly in this era of big spatial data. Scientific workflow systems hold the promise of increased openness and transparency with improved automation of spatial analytics processes. In this work, we built and implemented a KNIME spatial analytics (“K-span”) software tool, an extension to the general-purpose open-source KNIME scientific workflow platform. The tool augments KNIME with new spatial analytics nodes by linking to and integrating a range of existing open-source spatial software and libraries. The implementation of the K-span system is demonstrated and evaluated with a case study associated with the original process of construction of the Australian national DEM (Digital Elevation Model) in the Greater Brisbane area of Queensland, Australia by Geoscience Australia (GA). The outcomes of translating example spatial analytics process into a an open, transparent, documented, automated, and reproducible scientific workflow highlights the benefits of using our system and our general approach. These benefits may help in increasing users’ assurance and confidence in spatial data products and in understanding of the provenance of foundational spatial data sets across diverse uses and user groups.

Issues of the environmental crisis are being addressed by researchers, government, and organizations alike. GHRM is one such field that is receiving lots of research focus since it is targeted at greening the firms and making them eco-friendly. This research reviews 317 articles from the Scopus database published on green human resource management (GHRM) from 2008 to 2021. The study applies text mining, latent semantic analysis (LSA), and network analysis to explore the trends in the research field in GHRM and establish the relationship between the quantitative and qualitative literature of GHRM. The study has been carried out using KNIME and VOSviewer tools. As a result, the research identifies five recent research trends in GHRM using K-mean clustering. Future researchers can work upon these identified trends to solve environmental issues, make the environment eco-friendly, and motivate firms to implement GHRM in their practices. [ABSTRACT FROM AUTHOR]

The heterogeneity of the Caco-2 cell line and differences in experimental protocols for permeability assessment using this cell-based method have resulted in the high variability of Caco-2 permeability measurements. These problems have limited the generation of large datasets to develop accurate and applicable regression models. This study presents a QSPR approach developed on the KNIME analytical platform and based on a structurally diverse dataset of over 4900 molecules. Interpretable models were obtained using random forest supervised recursive algorithms for data cleaning and feature selection. The development of a conditional consensus model based on regional and global regression random forest produced models with RMSE values between 0.43–0.51 for all validation sets. The potential applicability of the model as a surrogate for the in vitro Caco-2 assay was demonstrated through blind prediction of 32 drugs recommended by the International Council for the Harmonization of Technical Requirements for Pharmaceuticals (ICH) for validation of in vitro permeability methods. The model was validated for the preliminary estimation of the BCS/BDDCS class. The KNIME workflow developed to automate new drug prediction is freely available. The results suggest that this automated prediction platform is a reliable tool for identifying the most promising compounds with high intestinal permeability during the early stages of drug discovery. [ABSTRACT FROM AUTHOR]

Abstract The thermal shift assay (TSA)—also known as differential scanning fluorimetry (DSF), thermofluor, and Tm shift—is one of the most popular biophysical screening techniques used in fragment-based ligand discovery (FBLD) to detect protein–ligand interactions. By comparing the thermal stability of a target protein in the presence and absence of a ligand, potential binders can be identified. The technique is easy to set up, has low protein consumption, and can be run on most real-time polymerase chain reaction (PCR) instruments. While data analysis is straightforward in principle, it becomes cumbersome and time-consuming when the screens involve multiple 96- or 384-well plates. There are several approaches that aim to streamline this process, but most involve proprietary software, programming knowledge, or are designed for specific instrument output files. We therefore developed an analysis workflow implemented in the Konstanz Information Miner (KNIME), a free and open-source data analytics platform, which greatly streamlined our data processing timeline for 384-well plates. The implementation is code-free and freely available to the community for improvement and customization to accommodate a wide range of instrument input files and workflows. Graphical Abstract

Plant-derived natural compounds are significant resources for the discovery of potential anticancer drugs. While research in the plant-based anticancer field has surged in recent years, systematic bibliometric analyses covering a longer period and containing up-to-date publications remain scarce. Here, we conducted a bibliometric analysis of literature on the anticancer properties of plant natural compounds over the past three decades, leveraging the bibliometric framework and open-access platform, KNIME. Our findings showed that the number of plant anticancer-related publications underwent an accelerating growth from 1992 to 2023. The country and institution analyses revealed that countries with traditional medical systems contributed a large portion of publications in the plant anticancer field, such as India, China, and South Korea. This study also highlighted the top ten eminent researchers and publications, assisting researchers in identifying pivotal literature. The primary publications were domains of chemistry and biology-related fields, such as Pharmacology & Pharmacy, Plant Sciences, and Biochemistry & Molecular Biology. Additionally, we noted that flavonoids have been focal plant compounds in anticancer, with strong anticancer potential. Our study provides new insights into the progress and trends in the plant anticancer field and will assist researchers in grasping the future research direction., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

In many interdisciplinary research domains, the creation of a shared understanding of relevant terms is considered the foundation for efficient cross-sector communication and interpretation of data and information. This is also true for the domain of One Health (OH) where many One Health Surveillance (OHS) documents rarely contain glossaries with a list of terms for which their specific meaning in the context of the given document is defined Cornelia et al. 2018, Buschhardt et al. 2021. The absence of glossaries within these documents may lead to misinterpretation of surveillance results due to the wrong interpretation of terminology specifically when term definitions differ across OH sectors. Under the One Health EJP project ORION, the OHEJP Glossary was recently created. The OHEJP Glossary is a tool to improve communication and collaboration among OH sectors by providing an easy-to-use online resource that lists relevant OH terms and sector-specific definitions. To improve the accessibility of content from the OHEJP Glossary and support the creation of integrative glossaries in future OHS-related documents, the OHEJP Glossaryfication Web Service was created. This service can support the practical use of the OHEJP Glossary and other relevant online glossaries by OH professionals.The Glossaryfication Web Service (GWS) is an application that automatically identifies terms in any uploaded text-based document and creates a document-specific list of matching definitions in selected online glossaries. This auto-generated document-specific glossary can easily be adjusted by the user, e.g. by selecting the desired definition in case multiple definitions were found for a specific term. The document-specific glossary could then be downloaded, manually adjusted, and finally included into the original document where it supports the correct interpretation of terminology used within the document. Especially in sector-specific reports such as from animal health or public health authorities, this can be beneficial to ensure the correct interpretation by other OH sectors in the future. The GWS developed with the open-source desktop software KNIME Analytics Platform and runs as a web service on a KNIME Web Server infrastructure. The core data processing functionality in the GWS is based on KNIME’s Textprocessing extension. KNIME's JavaScript nodes provided the basis for an interactive user interface where users can easily upload their files and select between different reference glossaries such as the OHEJP Glossary, the CDC Glossary, the WHO Glossary, or the EFSA Glossary. After retrieval of the user input settings, the GWS tags words within the provided document and maps these tagged words with matching entries in the selected glossaries. As the main output, the user receives a downloadable list of matching terms with their corresponding definitions, sectorial assignments, and references, which can then be added by the user to the original document. The GWS is freely accessible via this link as well as the underlying KNIME workflow.