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Biocomputing 2019 - 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, 2018.
Edition:1st ed.
Subjects:
Electronic books.
Online Access:Click to View
Table of Contents:
  • Intro
  • Preface
  • PATTERN RECOGNITION IN BIOMEDICAL DATA: CHALLENGES IN PUTTING BIG DATA TO WORK
  • Session introduction
  • Introduction
  • References
  • Learning Contextual Hierarchical Structure of Medical Concepts with Poincairé Embeddings to Clarify Phenotypes
  • 1. Introduction
  • 2. Methods
  • 2.1. Source Code
  • 2.2. Data Source
  • 2.3. Data Selection and Preprocessing
  • 2.3.1. Reference ICD9 Example
  • 2.3.2. Real Member Analyses
  • 2.4. Poincaré Embeddings
  • 2.5. Processing and Evaluating Embeddings
  • 3. Results
  • 3.1. ICD9 Hierarchy Evaluation
  • 3.2. Poincaré Embeddings on 10 Million Members
  • 3.3. Comparison with Euclidean Embeddings
  • 3.4. Cohort Specific Embeddings
  • 4. Discussion and Conclusion
  • 5. Acknowledgments
  • References
  • The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data
  • 1. Introduction
  • 2. Background
  • 2.1. Multitask nets
  • 3. Methods
  • 3.1. Dataset Construction and Design
  • 3.2. Experimental Design
  • 4. Experiments and Results
  • 4.1. When Does Multitask Learning Improve Performance?
  • 4.2. Relationship Between Performance and Number of Tasks
  • 4.3. Comparison with Logistic Regression Baseline
  • 4.4. Interaction between Phenotype Prevalence and Complexity
  • 5. Limitations
  • 6. Conclusion
  • Acknowledgments
  • References
  • ODAL: A one-shot distributed algorithm to perform logistic regressions on electronic health records data from multiple clinical sites
  • 1. Introduction
  • 1.1. Integrate evidence from multiple clinical sites
  • 1.2. Distributed Computing
  • 2. Material and Method
  • 2.1. Clinical Cohort and Motivating Problem
  • 2.2. Algorithm
  • 2.3. Simulation Design
  • 3. Results
  • 3.1. Simulation Results
  • 3.2. Fetal Loss Prediction via ODAL
  • 4. Discussion
  • References.
  • PVC Detection Using a Convolutional Autoencoder and Random Forest Classifier
  • 1. Introduction
  • 2. Methods
  • 2.1. Data Set and Implementation
  • 2.2. Proposed PVC Detection Method
  • 2.2.1. Feature Extraction
  • 2.2.2. Classification
  • 3. Results
  • 3.1. Full Database Evaluation
  • 3.2. Timing Disturbance Evaluation
  • 3.3. Cross-Patient Training Evaluation
  • 3.4. Estimated Parameters and Convergence
  • 4. Discussion
  • References
  • Removing Confounding Factors Associated Weights in Deep Neural Networks Improves the Prediction Accuracy for Healthcare Applications
  • 1. Introduction
  • 2. Related Work
  • 3. Confounder Filtering (CF) Method
  • 3.1. Overview
  • 3.2. Method
  • 3.3. Availability
  • 4. Experiments
  • 4.1. lung adenocarcinoma prediction
  • 4.1.1. Data
  • 4.1.2. Results
  • 4.2. Segmentation on right ventricle(RV) of Heart
  • 4.2.1. Data
  • 4.2.2. Results
  • 4.3. Students' confusion status prediction
  • 4.3.1. Data
  • 4.3.2. Results
  • 4.4. Brain tumor prediction
  • 4.4.1. Data
  • 4.4.2. Results
  • 4.5. Analyses of the method behaviors
  • 5. Conclusion
  • 6. Acknowledgement
  • References
  • DeepDom: Predicting protein domain boundary from sequence alone using stacked bidirectional LSTM
  • 1. Introduction
  • 2. METHODS
  • 2.1 Data Set Preparation
  • 2.2 Input Encoding
  • 2.3 Model Architecture
  • 2.4 Evaluation criteria
  • 3. RESULTS AND DISCUSSION
  • 3.1 Parameter configuration experiments on test data
  • 3.2 Comparison with Other Domain Boundary Predictors
  • 3.2.1 Free modeling targets from CASP 9
  • 3.2.2 Multi-domain targets from CASP 9
  • 3.2.3 Discontinuous domain target from CASP 8
  • 4. CONCLUSION
  • 5. ACKNOWLEDGEMENTS
  • REFERENCES
  • Res2s2aM: Deep residual network-based model for identifying functional noncoding SNPs in trait-associated regions
  • 1. Introduction
  • 2. Background theory.
  • 3. Dataset for training and testing
  • 3.1. Source databases
  • 3.2. Dataset generation
  • 4. Methods
  • 4.1. ResNet architecture in our model
  • 4.2. Tandem inputs of forward- and reverse-strand sequences
  • 4.3. Biallelic high-level network structure
  • 4.4. Incorporating HaploReg SNP annotation features
  • 4.5. Training of models
  • 5. Results
  • 6. Conclusions and discussion
  • Acknowledgements
  • References
  • DNA Steganalysis Using Deep Recurrent Neural Networks
  • 1. Introduction
  • 2. Background
  • 2.1. Notations
  • 2.2. Hiding Messages
  • 2.3. Determination of Message-Hiding Regions
  • 3. Methods
  • 3.1. Proposed DNA Steganalysis Principle
  • 3.2. Proposed Steganalysis RNN Model
  • 4. Results
  • 4.1. Dataset
  • 4.2. Input Representation
  • 4.3. Model Training
  • 4.4. Evaluation Procedure
  • 4.5. Performance Comparison
  • 5. Discussion
  • Acknowledgments
  • References
  • Bi-directional Recurrent Neural Network Models for Geographic Location Extraction in Biomedical Literature
  • 1. Introduction
  • 2. Related Work
  • 3. Methods
  • 3.1. Toponym Detection
  • 3.1.1. Recurrent Neural Networks
  • 3.1.2. LSTM
  • 3.1.3. Other Gated RNN Architectures
  • 3.1.4. Hyperparameter search and optimization
  • 3.2. Toponym Disambiguation
  • 3.2.1. Building Geonames Index
  • 3.2.2. Searching Geonames Index
  • 4. Results and Discussion
  • 4.1. Toponym Disambiguation
  • 4.2. Toponym Resolution
  • 5. Limitations and Future Work
  • 6. Conclusion
  • Acknowledgments
  • Funding
  • References
  • Automatic Human-like Mining and Constructing Reliable Genetic Association Database with Deep Reinforcement Learning
  • 1. Introduction
  • 2. Related Work
  • 3. Method
  • 3.1. Model Framework
  • 3.2. Deep Reinforcement Learning for Organizing Actions
  • 3.3. Preprocessing and Name Entity Recognition with UMLS
  • 3.4. Bidirectional LSTM for Relation Classification.
  • 3.5. Algorithm
  • 3.6. Implementation Specification
  • 4. Experiments
  • 4.1. Data
  • 4.2. Evaluation
  • 4.3. Results
  • 4.3.1. Improved Reliability
  • 4.3.2. Robustness in Real-world Situations
  • 4.3.3. Number of Articles Read
  • 5. Conclusions and Future Work
  • 6. Acknowledgement
  • References
  • Estimating classification accuracy in positive-unlabeled learning: characterization and correction strategies
  • 1. Introduction
  • 2. Methods
  • 2.1. Performance measures: definitions and estimation
  • 2.2. Positive-unlabeled setting
  • 2.3. Performance measure correction
  • 3. Experiments and Results
  • 3.1. A case study
  • 3.2. Data sets
  • 3.3. Experimental protocols
  • 3.4. Results
  • 4. Conclusions
  • Acknowledgements
  • References
  • PLATYPUS: A Multiple-View Learning Predictive Framework for Cancer Drug Sensitivity Prediction
  • 1. Introduction
  • 2. System and methods
  • 2.1. Data
  • 2.2. Single views and co-training
  • 2.3. Maximizing agreement across views through label assignment
  • 3. Results
  • 3.1. Preliminary experiments to optimize PLATYPUS performance
  • 3.2. Predicting drug sensitivity in cell lines
  • 3.3. Key features from PLATYPUS models
  • 4. Conclusions
  • Acknowledgments
  • References
  • Computational KIR copy number discovery reveals interaction between inhibitory receptor burden and survival
  • 1. Introduction
  • 2. Materials and Methods
  • 2.1 Data collection
  • 2.2 K-mer selection
  • 2.3 NGS pipeline and k-mer extraction
  • 2.4 Data cleaning
  • 2.5 Normalization of k-mer frequencies
  • 2.6 Copy number segregation and cutoff selection
  • 2.7 Validation of copy number
  • 2.8 Survival analysis
  • 2.9 Additional immune analysis
  • 3. Results and Discussions
  • 3.1 Establishing unique k-mers
  • 3.2 Varying coverage of KIR region by exome capture kit
  • 3.3 Inference of KIR copy number
  • 3.4 Population variation of the KIR region.
  • 3.5 KIR inhibitory gene burden correlates with survival in cervical and uterine cancer
  • 5. Conclusions
  • 6. Acknowledgements
  • 7. Supplementary Material
  • References
  • Exploring microRNA Regulation of Cancer with Context-Aware Deep Cancer Classifier
  • 1. Introduction
  • 2. Data
  • 2.1. Preprocessing
  • 3. Deep Cancer Classifier
  • 3.1. Training &amp
  • testing
  • 3.2. Parameter tuning
  • 3.3. Feature importance
  • 4. Results and Discussion
  • 4.1. Model selection
  • 4.2. Classifier performance
  • 4.3. Comparison with other methods
  • 4.4. Feature importance
  • 5. Conclusion
  • References
  • Implementing and Evaluating A Gaussian Mixture Framework for Identifying Gene Function from TnSeq Data
  • 1. Introduction
  • 1.1. TnSeq Motivation and Background
  • 1.2. Motivation and New Methods
  • 2. Methods
  • 2.1. TnSeq Experimental Data
  • 2.2. Mixture framework
  • 2.3. Classification methods
  • 2.3.1. Novel method - EM
  • 2.3.2. Current method - t-statistic
  • 2.3.3. Bayesian hierarchical model
  • 2.3.4. Data partitioning for the Bayesian model
  • 2.4. Simulation
  • 2.5. Real data
  • 3. Results
  • 3.1.1. Classification rate
  • 3.1.2. False positive rate
  • 3.1.3. Positive classification rate
  • 3.1.4. Cross entropy
  • 3.2. Simulation Results
  • 3.3. Comparisons on real data
  • 3.4. Software
  • 4. Discussion
  • References
  • SNPs2ChIP: Latent Factors of ChIP-seq to infer functions of non-coding SNPs
  • 1. Introduction
  • 2. Results
  • 2.1. SNPs2ChIP analysis framework overview
  • 2.2. Batch normalization of heterogeneous epigenetic features
  • 2.3. Latent factor discovery and their biological characterization
  • 2.4. SNPs2ChIP identifies relevant functions of the non-coding genome
  • 2.4.1. Genome-wide SNPs coverage of the reference datasets
  • 2.4.2. Non-coding GWAS SNPs of systemic lupus erythematosus
  • 2.4.3. ChIP-seq peaks for vitamin D receptors.
  • 2.5. Robustness Analysis in the latent factor identification.

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