Intro -- Preface -- APPLICATIONS OF GENETICS, GENOMICS AND BIOINFORMATICS IN DRUG DISCOVERY -- Session introduction -- 1. Introduction -- 2. Session Contributions -- 2.1. Drug mechanisms of action and drug combinations -- 2.2. Drug metabolism and in silico drug screening -- 2.3. Disease genes and pathways -- 3. Acknowledgments -- References -- Characterization of drug-induced splicing complexity in prostate cancer cell line using long read technology -- Introduction -- Results -- Discussion -- Methods -- Supplementary -- Acknowledgements -- References -- Prediction of protein-ligand interactions from paired protein sequence motifs and ligand substructures -- 1. Introduction -- 1.1. Decreasing returns in drug discovery pipelines -- 1.2. Existing methods for prediction of protein-ligand interactions -- 2. Methods -- 2.1. Data set -- 2.2. Protein Featurization -- 2.3. Ligand Featurization -- 2.4. Boosting Model -- 2.5. Cross Validation Approaches -- 3. Results -- 3.1. Model Performance -- 3.2. Most predictive motif features -- 3.3. Known positive examples -- 3.3.1. Uricase - Uric acid -- 3.3.2. Chloramphenicol O-acetyltransferase - Chloramphenicol -- 3.3.3. Transthyretin -T4 -- 3.4. Interpreting ADT Paths -- 3.4.1. Path lengths -- 3.4.2. Protein kinase C - Phosphatidylserine -- 4. Discussion -- Acknowledgments -- References -- Cell-specific prediction and application of drug-induced gene expression profiles -- 1. Introduction -- 2. Methods -- 2.1. Notation and terminology -- 2.2. Data processing -- 2.3. The Drug Neighbor Profile Prediction algorithm -- 2.4. The Fast, Low-Rank Tensor Completion algorithm -- 2.5. Baseline averaging schemes -- 2.6. Cross-validation for predicting gene expression profiles -- 2.7. Predicting drug targets and ATC codes -- 3. Results -- 3.1. Overall accuracy -- 3.2. Tradeoffs in accuracy across drug-cell space.

3.3. Effects of varying observation density -- 3.4. Accuracy of differentially expressed genes -- 3.5. Analysis of cell-specificity -- 3.6. Utility of completed data for downstream prediction of drug properties -- 4. Discussion -- Supplementary Information -- Funding -- References -- Large-scale integration of heterogeneous pharmacogenomic data for identifying drug mechanism of action -- 1. Introduction -- 2. Materials and Methods -- 2.1. Construction of heterogeneous drug-drug similarity networks -- 2.2. Integration of multi-omics data -- 2.3. Prediction of MoAs and drug targets -- 3. Results -- 3.1. Mania improves the quanti cation of drug-drug similarity -- 3.2. Mania achieves accurate prediction of drug MoAs and targets -- 3.3. Identification of functionally-enriched drug communities -- 3.4. Predictions of drugs for significantly mutated genes -- 4. Discussion -- References -- Chemical reaction vector embeddings: towards predicting drug metabolism in the human gut microbiome -- 1. Introduction -- 2. Methods -- 2.1. Data sources and processing -- 2.2. Constructing molecular vector space -- 2.3. Characterizing vector spaces -- 2.3.1. Molecule-level Analysis -- 2.3.2. Reaction-Level Analysis -- 2.4. Querying drug-metabolite pairs against reaction vectors -- 3. Results -- 3.1. Molecule-level analysis -- 3.2. Reaction-level analysis -- 3.3. Querying reaction vectors against drug-metabolite pairs -- 4. Discussion -- 5. Conclusion -- 6. Acknowledgments -- References -- Loss-of-function of neuroplasticity-related genes confers risk for human neurodevelopmental disorders -- 1. Introduction -- 2. Methods -- 2.1 Neuroplasticity signatures -- 2.2 Hospital and biobank cohort -- 2.3 Variant annotation -- 2.4 Neurodevelopmental disease phenotyping -- 2.5 LOF gene and disease association analysis -- 3. Results -- 3.1 Identifying putative neuroplasticity genes.

3.2 LOF variants in putative plasticity genes confer risk for neurodevelopmental and nervous system related disorders -- 4. Discussion -- 5. Conclusions and Future Directions -- 6. Acknowledgments -- References -- Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders -- 1. Introduction -- 2. Methods -- 2.1. Model Summary -- 2.2. Model Implementation -- 2.3. Parameter Selection -- 2.4. Input Data -- 2.5. Interpretation of Gene Weights -- 2.6. The Latent Space of Ovarian Cancer Subtypes -- 2.7. Enabling Exploration through Visualization -- 3. Results -- 3.1. Tumors were encoded in a lower dimensional space -- 3.2. Features represent biological signal -- 3.3. Interpolating the lower dimensional manifold of HGSC subtypes -- 4. Conclusion -- 5. Reproducibility -- Acknowledgments -- References -- Diffusion mapping of drug targets on disease signaling network elements reveals drug combination strategies -- 1. Introduction -- 2. Methods -- 3. Results -- 4. Discussion -- References -- CHALLENGES OF PATTERN RECOGNITION IN BIOMEDICAL DATA -- Session introduction -- 1. Introduction -- 2. Session Contributions -- 2.1 Network-based approaches -- 2.2 Machine learning approaches -- 2.2 Application of methods to identify patterns in EHR data -- 2.3 Applications in transcriptome and next-generation sequencing data -- 3. References -- Large-scale analysis of disease pathways in the human interactome -- 1. Introduction -- 2. Background and related work -- 3. Data -- 4. Connectivity of disease proteins in the PPI network -- 4.1. Proximity of disease proteins in the PPI network -- 4.2. Connections between PPI network structure and disease protein discovery -- 5. Higher-order connectivity of disease proteins in the PPI network -- 6. Prediction of disease proteins using higher-order PPI network structure -- 7. Conclusion.

Acknowledgments -- References -- Mapping patient trajectories using longitudinal extraction and deep learning in the MIMIC-III Critical Care Database -- 1. Introduction -- 2. Methods -- 2.1. Source Code and Analysis Availability -- 2.2. Care Event Extraction -- 2.3. Unsupervised learning to learn embeddings of extracted Care Events -- 2.4. Predicting Survival Using Care Events -- 3. Results -- 3.1. Treatment and Outcome Comparison -- 3.2. Unsupervised modeling of patient care events -- 3.3. Supervised prediction of patient survival -- 4. Discussion and Conclusions -- 5. Acknowledgments -- References -- OWL-NETS: Transforming OWL representations for improved network inference -- 1. Introduction -- 2. Methods -- 2.1. Biomedical Use Cases -- 2.2. Link Prediction Procedures -- 2.2.1. Evaluation of Link Prediction Algorithm Performance -- 2.2.2. Evaluation of Inferred Edges -- 3. Results -- 3.1. Comparison of Network Properties -- 3.2. Link Prediction Algorithm Performance -- 3.2.1. Inferred Edges -- 4. Discussion -- 5. Conclusions -- 6. Acknowledgments -- 7. Funding -- References -- Automated disease cohort selection using word embeddings from Electronic Health Records -- 1. Introduction -- 2. Methods and Materials -- 2.1. Research Cohort and Resource -- 2.2. Disease Phenotyping Algorithms -- 2.3. Phenotype and Patient Embedding -- 2.4. Evaluation Design -- 3. Results -- 3.1. Evaluating Performance of Embeddings -- 4. Discussion -- 4.1. Limitations and Future Directions -- 5. Acknowledgments -- References -- Functional network community detection can disaggregate and filter multiple underlying pathways in enrichment analyses -- 1. Introduction -- 2. Methods -- 2.1. General Approach -- 2.2. Control Arm -- 2.3. Experimental Arm -- 3. Results and Discussion -- 3.1. Simulation Study -- 3.2. HGSC Results -- 4. Conclusion -- 5. Acknowledgments.

6. Supplementary Material -- References -- An ultra-fast and scalable quantification pipeline for transposable elements from next generation sequencing data -- 1. Introduction -- 2. Methods -- 2.1. Transposable Element Library Preparation -- 2.2. Salmon quanti cation algorithm -- 2.3. Statistical tests -- 3. Results -- 3.1. Datasets -- 3.2. Computational experiment setup -- 3.3. SalmonTE guarantees a reliable TE expression estimation -- 3.4. SalmonTE shows a better scalability in the speed benchmark dataset -- 3.5. Discover differentially expressed TEs in ALS cell line -- 4. Conclusion -- Acknowledgments -- References -- Causal inference on electronic health records to assess blood pressure treatment targets: An application of the parametric g formula -- 1. Introduction -- 1.1. Global Burden of Hypertension -- 1.2. Challenges in Previous Efforts to Discover Optimal Target Blood Pressures -- 1.3. Causal Inference from Electronic Health Records As a Tool to Answer Difficult Clinical Questions -- 2. Methods -- 2.1. Data Acquisition from the Mount Sinai Hospital EHR -- 2.2. Problem setup -- 2.3. Parametric g formula -- 3. Results -- 3.1. Electronic Health Records Data -- 3.2. Survival time by goal blood pressure target -- 4. Conclusion -- References -- Data-driven advice for applying machine learning to bioinformatics problems -- 1. Introduction -- 2. Methods -- 3. Results -- 3.1. Algorithm Performance -- 3.2. Effect of Tuning and Model Selection -- 3.3. Algorithm Coverage -- 4. Discussion and Conclusions -- 5. Acknowledgments -- References -- Improving the explainability of Random Forest classifier - user centered approach -- 1. Introduction, Background and Motivation -- 1.1 Random Forest (RF) Classifiers -- 1.2 Related work on Explainability for Random Forest Classifiers -- 1.3 User-Centered Approach in Enhancing Random Forest Explainability - RFEX.

2. Case Study: RFEX Applied to Stanford FEATURE data.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2022. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.