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Biocomputing 2018 - Proceedings Of The Pacific Symposium.

Bibliographic Details
Main Author: Altman, Russ B.
Other Authors: Dunker, A Keith., Hunter, Lawrence., Ritchie, Marylyn D., Murray, Tiffany A., Klein, Teri E.
Format: eBook
Language:English
Published: Singapore : World Scientific Publishing Company, 2017.
Edition:1st ed.
Subjects:
Electronic books.
Online Access:Click to View
Table of Contents:
  • 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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