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Biocomputing 2020 - 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, 2019.
Edition:1st ed.
Subjects:
Electronic books.
Online Access:Click to View
Table of Contents:
  • Intro
  • Content
  • Preface
  • ARTIFICIAL INTELLIGENCE FOR ENHANCING CLINICAL MEDICINE
  • Session Introduction: Artificial Intelligence for Enhancing Clinical Medicine
  • 1. Introduction
  • 2. Novel Research Applying Artificial Intelligence to Clinical Medicine
  • 2.1. Artificial intelligence for predicting patient outcomes
  • 2.2. Artificial intelligence for improved insight into disease pathogenesis and features
  • 3. Artificial intelligence for advancing medical workflows
  • 4. Artificial intelligence for improving imaging
  • 5. Conclusion
  • References
  • Predicting Longitudinal Outcomes of Alzheimer's Disease via a Tensor-Based Joint Classification and Regression Model
  • 1. Introduction
  • 2. Methods
  • 2.1. The Longitudinal Joint Learning Model
  • 2.2. The Solution Algorithm Using the Multi-Block ADMM
  • 3. Experiments
  • 3.1. Performance
  • 3.2. Empirical Convergence
  • 3.3. Biomarker Identification
  • 4. Conclusion
  • Acknowledgements
  • References
  • Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph
  • 1. Introduction
  • 2. Related Work
  • 3. Methods
  • 3.1. Data collection and preparation
  • 3.2. Evaluation with GHKG
  • 3.3. Disease predictability analysis
  • 3.4. Demographic analysis
  • 3.5. Non-linear methods
  • 4. Results
  • 5. Discussion
  • 5.1. Data size does not always matter.
  • 5.2. Confounders may explain errors
  • 5.3. Increased model complexity does not necessarily help
  • 5.4. Limitations remain as an opportunity for future work
  • 6. Conclusion
  • Acknowledgements
  • References
  • Increasing Clinical Trial Accrual via Automated Matching of Biomarker Criteria
  • 1. INTRODUCTION
  • 2. MATERIALS AND METHODS
  • 2.1. Specimens and Retrospective Analysis
  • 2.2. Real-time Analysis
  • 2.3. Source of Biomarker-based Clinical Trial Data
  • 3. RESULTS.
  • 3.1. STAMP assay identifies somatic mutations
  • 3.2. Algorithmic pipeline flags eligible patients for precision medicine clinical trials
  • 3.2.1. Automation of Feature Matching
  • 3.2.2. Manual Review of Matching Output
  • 3.3. Validation of algorithmic pipeline
  • 3.4. Match rate analysis of STAMP-identified mutations
  • 4. DISCUSSION
  • 4.1. Incorporation of informatics into clinical workflows
  • 4.2. Limitations of algorithmic pipelines
  • 5. CONCLUSION
  • 6. AUTHOR CONTRIBUTIONS
  • 7. ACKNOWLEDGEMENTS
  • 8. REFERENCES
  • 9. FIGURES
  • 10. SUPPLEMENTARY TABLES AND FIGURES
  • Addressing the Credit Assignment Problem in Treatment Outcome Prediction Using Temporal Difference Learning
  • 1. Introduction
  • 2. Dataset
  • 3. Methods
  • 3.1. Feature Extraction
  • 3.2. Temporal Difference Learning
  • 3.2.1. State-Estimation
  • 3.2.2. Value Iteration
  • 3.2.3. Optimization
  • 3.3. Baselines and Performance Measure
  • 4. Results
  • 5. Discussion and Conclusion
  • References
  • Multiclass Disease Classification from Microbial Whole-Community Metagenomes
  • 1. Introduction
  • 2. Previous Work
  • 3. Problem Setup
  • 3.1. Dataset Construction
  • 3.2. Graph Convolutional Neural Networks
  • 3.3. Models
  • 3.4. Training
  • 4. Results
  • 5. Conclusion
  • 6. Acknowledgments
  • 7. External Links
  • References
  • LitGen: Genetic Literature Recommendation Guided by Human Explanations
  • 1. Introduction
  • 2. Clinical Variant Curation Data
  • 2.1. ClinGen's Variant Curation Interface (VCI)
  • 2.2. Labeled papers
  • 2.3. Unlabeled papers
  • 3. Method
  • 3.1. BiLSTM baseline
  • 3.2. Leveraging unlabeled data
  • 3.3. Explanations in multitask learning
  • 3.4. Explanations as feature selection for proxy labeling
  • 4. Experimental results
  • 4.1. Evaluation metrics
  • 4.2. Performance comparison
  • 4.3. Performance of Proxy Labeling Model.
  • 4.4. Performance by Evidence Types
  • 5. Discussion
  • References
  • From Genome to Phenome: Predicting Multiple Cancer Phenotypes Based on Somatic Genomic Alterations via the Genomic Impact Transformer
  • 1. Introduction
  • 2. Materials and methods
  • 2.1. SGAs and DEGs pre-processing
  • 2.2. The GIT neural network
  • 2.2.1. GIT network structure: encoder-decoder architecture
  • 2.2.2. Pre-training gene embeddings using Gene2Vec algorithm
  • 2.2.3. Encoder: multi-head self-attention mechanism
  • 2.2.4. Decoder: multi-layer perceptron (MLP)
  • 2.3. Training and evaluation
  • 3. Results
  • 3.1. GIT statistically detects real biological signals
  • 3.2. Gene embeddings compactly represent the functional impact of SGAs
  • 3.4. Personalized tumor embeddings reveal distinct survival profiles
  • 3.5. Tumor embeddings are predictive of drug responses of cancer cell lines
  • 4. Conclusion and Future Work
  • Acknowledgments
  • Funding
  • References
  • Automated Phenotyping of Patients with Non-Alcoholic Fatty Liver Disease Reveals Clinically Relevant Disease Subtypes
  • 1. Introduction
  • 2. Methods
  • 2.1. NAFLD definition
  • 2.2. Natural language processing
  • 2.3. Data collection
  • 2.4. Clinical feature standardization and quality control
  • 2.4.1. Demographic data
  • 2.4.2. Diagnoses, procedures, medications
  • 2.4.3. Laboratory tests
  • 2.4.4. Vital signs
  • 2.5. Patient pairwise distance and clustering
  • 2.6. Statistical analysis
  • 2.6.1. Descriptive statistics
  • 2.6.2. Survival analysis
  • 3. Results
  • 3.1. Descriptive statistics for the cohort
  • 3.2. Identification of NAFLD subtypes
  • 3.3. Identification of distinct outcomes by NAFLD subtype
  • 3.4. Internal cross-validation of the subtypes discovered
  • 4. Conclusion
  • 5. References
  • References
  • Monitoring ICU Mortality Risk with a Long Short-Term Memory Recurrent Neural Network.
  • 1. Introduction
  • 2. Background and Related Work
  • 3. Data and Preprocessing
  • 3.1. Data Source and Cohort Selection
  • 3.2. Data Extraction and Preprocessing
  • 4. Methodology
  • 4.1. Mortality Monitoring Task
  • 4.2. Average Pooling and Attention Mechanism
  • 4.3. Recurrent Neural Network (RNN)
  • 5. Experimental Design
  • 5.1. Sampling Strategy
  • 5.2. Baseline Model
  • 5.3. Experimental Settings
  • 6. Results and Analysis
  • 6.1. Dimensionality Analysis
  • 6.2. Prediction with Different Feature Representations
  • 6.3. Interpreting Mortality of Learned Representation
  • 7. Discussion and Conclusions
  • References
  • Multilevel Self-Attention Model and Its Use on Medical Risk Prediction
  • 1. Introduction
  • 2. Related Work
  • 2.1. Future disease prediction
  • 3. Methods
  • 3.1. Terminology and Notation
  • 3.2. Model Architecture
  • 3.3. Self-attention Encoder Unit
  • 3.4. Loss Function
  • 4. Experiments
  • 4.1. Source of Data
  • 4.2. Dataset preprocessing
  • 4.3. Implementation details
  • 5. Results
  • 5.1. Future disease prediction
  • 5.2. Future cost prediction
  • 5.3. Case study for the self-attention mechanism
  • 6. Conclusion
  • 7. Bibliography
  • Identifying Transitional High Cost Users from Unstructured Patient Profiles Written by Primary Care Physicians
  • 1. Introduction
  • 2. Data
  • 2.1. EMRPC
  • 2.2. Total Healthcare Costs
  • 2.3. Encoding of Ordinal Variables
  • 2.4. Word Embeddings
  • 3. Methods
  • 3.1. Bag of Words
  • 3.2. EmbEncode
  • 3.3. Historical Baseline
  • 3.4. Varying the Training Set
  • 3.5. Varying the Evaluation Set
  • 4. Results
  • 5. Discussion
  • Acknowledgments
  • References
  • Obtaining Dual-Energy Computed Tomography (CT) Information from a Single-Energy CT Image for Quantitative Imaging Analysis of Living Subjects by Using Deep Learning
  • 1. Introduction
  • 2. Methods
  • 3. Results.
  • 4. Discussion and Conclusion
  • 5. Acknowledge
  • References
  • INTRINSICALLY DISORDERED PROTEINS (IDPS) AND THEIR FUNCTIONS
  • Session Introduction: On the Importance of Computational Biology and Bioinformatics to the Origins and Rapid Progression of the Intrinsically Disordered Proteins Field
  • 1. Introduction
  • 2. Computational prediction of IDPs and IDRs and their functions
  • 3. Popularization of research on IDPs and IDRs
  • 4. A Collection of Recent Papers on IDPs and IDRs
  • References
  • Many-to-One Binding by Intrinsically Disordered Protein Regions
  • 1. Introduction
  • 2. Results
  • 2.1. Many-to-one binding datasets
  • 2.2. Many-to-one binding profiles: independent and overlapping
  • 2.3 Comparing VOR (with backbone only) and RMSΔASA Values
  • 2.4. Selected many-to-one case studies
  • 3. Discussion
  • 4. Materials and Methods
  • 4.1. Dataset preparation
  • 4.2. MoRF sequence similarity
  • 4.3. MoRF interface similarity
  • References
  • Disordered Function Conjunction: On the In-Silico Function Annotation of Intrinsically Disordered Regions
  • 1. Introduction
  • 2. Materials and Methods
  • 2.1. Data collection
  • 2.2. Computational workflow
  • 2.2.1. Feature-based representation of protein regions
  • 2.2.2. Prediction of protein region functions
  • 2.2.3. Assessment of the function prediction and clustering
  • 3. Results and Discussion
  • 3.1. Prediction of individual functions of IDRs
  • 3.2. IDRs described in multidimensional space form function-related clusters
  • 3.3. Case studies
  • 4. Conclusions
  • Acknowledgments
  • References
  • De novo Ensemble Modeling Suggests that AP2-Binding to Disordered Regions Can Increase Steric Volume of Epsin but Not Eps15
  • 1. Introduction
  • 2. Methods
  • 2.1. Generation of structural ensembles
  • 2.2. Filtering Epsin conformers to mimic the effect of Plasma membrane.
  • 2.3. Docking AP2α to the IDRs by superposition.

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