Biocomputing 2018 - Proceedings Of The Pacific Symposium.
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Place / Publishing House: | Singapore : : World Scientific Publishing Company,, 2017. Ã2018. |
Year of Publication: | 2017 |
Edition: | 1st ed. |
Language: | English |
Online Access: | |
Physical Description: | 1 online resource (649 pages) |
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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.