Retrieval and visualization of biological data are essential for understanding complex systems. With the increasing volume of data generated from high-throughput sequencing technologies, effective and optimized data visualization tools have become indispensable. This is particularly relevant in the COVID-19 postpandemic period, where understanding the diversity and interactions of microbial communities (i.e., viral and bacterial) constitutes an important asset to develop and plan suitable interventions.In this chapter, we show the usage and the potentials of ExTaxsI (Exploring Taxonomy Information) tool to retrieve viral biodiversity data stored in National Center for Biotechnology Information (NCBI) databases and create the related visualization. In addition, by integrating different functions and modules, the tool generates relevant types of visualization plots to facilitate the exploration of microbial biodiversity communities useful to deep dive into ecological and taxonomic relationships among different species and identify potential significant targets.Using the Monkeypox virus as a case study, this work points out significant perspectives on biological data visualization, which can be used to gain insights into the ecology, evolution, and pathogenesis of viruses. Accordingly, we show the potentiality of ExTaxsI to organize and describe the available/downloaded data in an easy, simple, and interpretable way allowing the user to interact dynamically with the visualization plots through specific filters, zoom, and explore functions.

Glioblastoma (GBM) is the most aggressive and common malignant primary brain tumor; however, treatment remains a significant challenge. This study aims to identify drug repurposing or repositioning candidates for GBM by developing an integrative rare disease profile network containing heterogeneous types of biomedical data.
We developed a Glioblastoma-based Biomedical Profile Network (GBPN) by extracting and integrating biomedical information pertinent to GBM-related diseases from the NCATS GARD Knowledge Graph (NGKG). We further clustered the GBPN based on modularity classes which resulted in multiple focused subgraphs, named mc_GBPN. We then identified high-influence nodes by performing network analysis over the mc_GBPN and validated those nodes that could be potential drug repurposing or repositioning candidates for GBM.
We developed the GBPN with 1,466 nodes and 107,423 edges and consequently the mc_GBPN with forty-one modularity classes. A list of the ten most influential nodes were identified from the mc_GBPN. These notably include Riluzole, stem cell therapy, cannabidiol, and VK-0214, with proven evidence for treating GBM.
Our GBM-targeted network analysis allowed us to effectively identify potential candidates for drug repurposing or repositioning. Further validation will be conducted by using other different types of biomedical and clinical data and biological experiments. The findings could lead to less invasive treatments for glioblastoma while significantly reducing research costs by shortening the drug development timeline. Furthermore, this workflow can be extended to other disease areas.

The pharmaceutical industry continuously looks for ways to improve its development and manufacturing efficiency. In recent years, such efforts have been driven by the transition from batch to continuous manufacturing and digitalization in process development. To facilitate this transition, integrated data management and informatics tools need to be developed and implemented within the framework of Industry 4.0 technology. In this regard, the work aims to guide the data integration development of continuous pharmaceutical manufacturing processes under the Industry 4.0 framework, improving digital maturity and enabling the development of digital twins. This paper demonstrates two instances where a data integration framework has been successfully employed in academic continuous pharmaceutical manufacturing pilot plants. Details of the integration structure and information flows are comprehensively showcased. Approaches to mitigate concerns in incorporating complex data streams, including integrating multiple process analytical technology tools and legacy equipment, connecting cloud data and simulation models, and safeguarding cyber-physical security, are discussed. Critical challenges and opportunities for practical considerations are highlighted.

Integration of data from multiple domains can greatly enhance the quality and applicability of knowledge generated in analysis workflows. However, working with health data is challenging, requiring careful preparation in order to support meaningful interpretation and robust results. Ontologies encapsulate relationships between variables that can enrich the semantic content of health datasets to enhance interpretability and inform downstream analyses.
We developed an R package for electronic health data preparation, \"eHDPrep,\" demonstrated upon a multimodal colorectal cancer dataset (661 patients, 155 variables; Colo-661); a further demonstrator is taken from The Cancer Genome Atlas (459 patients, 94 variables; TCGA-COAD). eHDPrep offers user-friendly methods for quality control, including internal consistency checking and redundancy removal with information-theoretic variable merging. Semantic enrichment functionality is provided, enabling generation of new informative \"meta-variables\" according to ontological common ancestry between variables, demonstrated with SNOMED CT and the Gene Ontology in the current study. eHDPrep also facilitates numerical encoding, variable extraction from free text, completeness analysis, and user review of modifications to the dataset.
eHDPrep provides effective tools to assess and enhance data quality, laying the foundation for robust performance and interpretability in downstream analyses. Application to multimodal colorectal cancer datasets resulted in improved data quality, structuring, and robust encoding, as well as enhanced semantic information. We make eHDPrep available as an R package from CRAN (https://cran.r-project.org/package = eHDPrep) and GitHub (https://github.com/overton-group/eHDPrep).

The increasing sophistication of mobile and sensing technology has enabled the collection of intensive longitudinal data (ILD) concerning dynamic changes in an individual\'s state and context. ILD can be used to develop dynamic theories of behavior change which, in turn, can be used to provide a conceptual framework for the development of just-in-time adaptive interventions (JITAIs) that leverage advances in mobile and sensing technology to determine when and how to intervene. As such, JITAIs hold tremendous potential in addressing major public health concerns such as cigarette smoking, which can recur and arise unexpectedly. In tandem, a growing number of studies have utilized multiple methods to collect data on a particular dynamic construct of interest from the same individual. This approach holds promise in providing investigators with a significantly more detailed view of how a behavior change processes unfold within the same individual than ever before. However, nuanced challenges relating to coarse data, noisy data, and incoherence among data sources are introduced. In this manuscript, we use a mobile health (mHealth) study on smokers motivated to quit (Break Free; R01MD010362) to illustrate these challenges. Practical approaches to integrate multiple data sources are discussed within the greater scientific context of developing dynamic theories of behavior change and JITAIs.

BACKGROUND: The amount of data in health care is rapidly rising, leading to multiple datasets generated for any given individual. Data integration involves mapping variables in different datasets together to form a combined dataset which can then be used to conduct different types of analyses. However, with increasing numbers of variables, manual mapping of a dataset can become inefficient. Another approach is to use text classification through machine learning to classify the variables to a schema.
OBJECTIVE: Our aim was to create and evaluate the use of machine learning methods for the integration of data from datasets across health information-seeking behavior (HISB) databases.
METHODS: Four online databases relevant to the research field were selected for integration. Two experiments were designed for dataset mapping: intra-database mapping using the one data source, and inter-database mapping to map datasets between the four databases. We compared logistic regression (LR), a random forest classifier (RFC), and neural network (NN) models by F1-score for two methods of integration. A third experiment was an ablation study that used all the available data to create a model for classifying HISB variables in a dataset.
RESULTS: In intra-database mapping, the mean F1 score for an LR classifier (0.787) was better than the RFC score (0.767) and fully connected NN (0.735). In inter-database mapping, the LR (0.245) scored best, however, this was dependent on which database was used as a training source. Using all the databases, these top three models were able to correctly classify 90-91% of the variables. Removing one dataset improved scores and resulted in a model able to correctly classify 95-96% of the HISB variables.
CONCLUSIONS: As part of data integration, a neural network can be used as an approach to map the variables of a dataset. The developed models can be used to classify the HISB terms in a database.

Knowledge of demography is essential for understanding wildlife population dynamics and developing appropriate conservation plans. However, population survey and demographic data (e.g., capture-recapture) are not always aligned in space and time, hindering our ability to robustly estimate population size and demographic processes. Integrated population models (IPMs) can provide inference for population dynamics with poorly aligned but jointly analysed population and demographic data. In this study, we used an IPM to analyse partially aligned population and demographic data of a migratory shorebird species, the snowy plover (Charadrius nivosus). Snowy plover populations have declined dramatically during the last two decades, yet the demographic mechanisms and environmental drivers of these declines remain poorly understood, hindering development of appropriate conservation strategies. We analysed 21 years (1998-2018) of partially aligned population survey, nest survey, and capture-recapture-resight data in three snowy plover populations (i.e., Texas, New Mexico, Oklahoma) in the Southern Great Plains of the US. By using IPMs we aimed to achieve better precision while evaluating the effects of wetland habitat and climatic factors (minimum temperature, wind speed) on snowy plover demography. Our IPM provided reasonable precision for productivity measures even with missing data, but population and survival estimates had greater uncertainty in years without corresponding data. Our model also uncovered the complex relationships between wetland habitat, climate, and demography with reasonable precision. Wetland habitat had positive effects on snowy plover productivity (i.e., clutch size and clutch fate), indicating the importance of protecting wetland habitat under climate change and other human stressors for the conservation of this species. We also found a positive effect of minimum temperature on snowy plover productivity, indicating potential benefits of warmth during night on their population. Based on our results, we suggest prioritizing population and capture-recapture surveys for understanding population dynamics and underlying demographic processes when data collection is limited by time and/or financial resources. Our modelling approach can be used to allocate limited conservation resources for evidence-based decision-making.

Motivation: Drug-induced liver injury (DILI) is one of the primary problems in drug development. Early prediction of DILI, based on the chemical properties of substances and experiments performed on cell lines, would bring a significant reduction in the cost of clinical trials and faster development of drugs. The current study aims to build predictive models of risk of DILI for chemical compounds using multiple sources of information. Methods: Using several supervised machine learning algorithms, we built predictive models for several alternative splits of compounds between DILI and non-DILI classes. To this end, we used chemical properties of the given compounds, their effects on gene expression levels in six human cell lines treated with them, as well as their toxicological profiles. First, we identified the most informative variables in all data sets. Then, these variables were used to build machine learning models. Finally, composite models were built with the Super Learner approach. All modeling was performed using multiple repeats of cross-validation for unbiased and precise estimates of performance. Results: With one exception, gene expression profiles of human cell lines were non-informative and resulted in random models. Toxicological reports were not useful for prediction of DILI. The best results were obtained for models discerning between harmless compounds and those for which any level of DILI was observed (AUC = 0.75). These models were built with Random Forest algorithm that used molecular descriptors.

Biomedical information mining is increasingly recognized as a promising technique to accelerate drug discovery and development. Especially, integrative approaches which mine data from several (open) data sources have become more attractive with the increasing possibilities to programmatically access data through Application Programming Interfaces (APIs). The use of open data in conjunction with free, platform-independent analytic tools provides the additional advantage of flexibility, re-usability, and transparency. Here, we present a strategy for performing ligand-based in silico drug repurposing with the analytics platform KNIME. We demonstrate the usefulness of the developed workflow on the basis of two different use cases: a rare disease (here: Glucose Transporter Type 1 (GLUT-1) deficiency), and a new disease (here: COVID 19). The workflow includes a targeted download of data through web services, data curation, detection of enriched structural patterns, as well as substructure searches in DrugBank and a recently deposited data set of antiviral drugs provided by Chemical Abstracts Service. Developed workflows, tutorials with detailed step-by-step instructions, and the information gained by the analysis of data for GLUT-1 deficiency syndrome and COVID-19 are made freely available to the scientific community. The provided framework can be reused by researchers for other in silico drug repurposing projects, and it should serve as a valuable teaching resource for conveying integrative data mining strategies.

Advances in technology within biomedical sciences have led to an inundation of data across many fields, raising new challenges in how best to integrate and analyze these resources. For example, rapid chemical screening programs like the US Environmental Protection Agency\'s ToxCast and the collaborative effort, Tox21, have produced massive amounts of information on putative chemical mechanisms where assay targets are identified as genes; however, systematically linking these hypothesized mechanisms with in vivo toxicity endpoints like disease outcomes remains problematic. Herein we present a novel use of normalized pointwise mutual information (NPMI) to mine biomedical literature for gene associations with biological concepts as represented by Medical Subject Headings (MeSH terms) in PubMed. Resources that tag genes to articles were integrated, then cross-species orthologs were identified using UniRef50 clusters. MeSH term frequency was normalized to reflect the MeSH tree structure, and then the resulting GeneID-MeSH associations were ranked using NPMI. The resulting network, called Entity MeSH Co-occurrence Network (EMCON), is a scalable resource for the identification and ranking of genes for a given topic of interest. The utility of EMCON was evaluated with the use case of breast carcinogenesis. Topics relevant to breast carcinogenesis were used to query EMCON and retrieve genes important to each topic. A breast cancer gene set was compiled through expert literature review (ELR) to assess performance of the search results. We found that the results from EMCON ranked the breast cancer genes from ELR higher than randomly selected genes with a recall of 0.98. Precision of the top five genes for selected topics was calculated as 0.87. This work demonstrates that EMCON can be used to link in vitro results to possible biological outcomes, thus aiding in generation of testable hypotheses for furthering understanding of biological function and the contribution of chemical exposures to disease.