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Biocomputing 2021 - 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, 2020.
Edition:1st ed.
Subjects:
Electronic books.
Online Access:Click to View
Table of Contents:
  • Intro
  • Contents
  • Preface
  • ACHIEVING TRUSTWORTHY BIOMEDICAL DATA
  • Session Introduction: Achieving Trustworthy Biomedical Data Solutions
  • 1. Introduction
  • 2. Preserving Privacy and Explaining Decisions of Artificial Intelligence
  • 3. Sharing Genomic and Health Records
  • 4. Deploying Digital Health Solutions
  • 5. Crowdsourcing Healthcare
  • 6. Considering the Bioethics
  • 7. Anticipating the Future
  • References
  • Selection of Trustworthy Crowd Workers for Telemedical Diagnosis of Pediatric Autism Spectrum Disorder
  • 1. Introduction
  • 2. Methods
  • 2.1. Clinically representative videos
  • 2.2. Crowdsourcing task for Microworkers
  • 2.3. Classifier to evaluate performance
  • 2.4. Metrics evaluated
  • 2.5. Prediction of crowd worker performance from metrics
  • 3. Results
  • 3.1. Correlation between metrics and probability of the correct class
  • 3.2. Regression prediction of the mean probability of the correct class
  • 4. Discussion and Future Work
  • 5. Conclusion
  • 6. Acknowledgments
  • References
  • Differential Privacy Protection Against Membership Inference Attack on Machine Learning for Genomic Data
  • 1. Introduction
  • 2. Related Work
  • 3. Methods
  • 3.1. Membership inference attack (MIA)
  • 3.2. Di erential privacy (DP)
  • 4. Experimental Setup
  • 4.1. Dataset
  • 4.2. Implementation of target models
  • 4.3. Implementation of DP
  • 4.4. Implementation of MIA
  • 4.5. Evaluation metrics
  • 5. Results
  • 5.1. Vulnerability of target model against MIA without DP protection
  • 5.2. Impact of privacy budget on the target model accuracy
  • 5.3. E ectiveness of DP against MIA
  • 5.4. E ect of model sparsity
  • 6. Conclusion
  • References
  • Making Compassionate Use More Useful: Using Real-World Data, Real-World Evidence and Digital Twins to Supplement or Supplant Randomized Controlled Trials
  • 1. Introduction.
  • 1.1 Compassionate use
  • 1.2 Compassionate use during the pandemic
  • 1.3 What is an RCT?
  • 1.3 EA data and NDAs
  • 2. Real-World Information
  • 2.1 Real-world data in trials
  • 2.2 Real-world data and real-world evidence
  • 2.2 Real-world limitations
  • 3.0 Making RWD Work
  • 3.1 Digital twins
  • 4.0 Conclusions
  • References
  • ADVANCED METHODS FOR BIG DATA ANALYTICS IN WOMEN'S HEALTH
  • Session Introduction: Advanced Methods for Big Data Analytics in Women's Health
  • 1. Introduction
  • 2. Session Summary
  • 2.1. Full-length papers
  • 3. Discussion
  • References
  • Intimate Partner Violence and Injury Prediction from Radiology Reports
  • 1. Introduction
  • 2. Related Work
  • 2.1. Intimate partner violence
  • 2.2. Clinical prediction
  • 2.3. Natural language processing
  • 3. Dataset
  • 3.1. IPV patient selection
  • 3.2. Control group selection
  • 3.3. Injury labels
  • 3.4. Data cleaning
  • 3.5. Demographic data
  • 4. Methodology
  • 4.1. Experiment setup
  • 4.2. Models
  • 4.3. Evaluation
  • 4.3.1. Prediction and predictive features
  • 4.3.2. Error analysis
  • 4.3.3. Report-program date gap
  • 5. Results
  • 5.1. IPV and injury prediction and predictive features
  • 5.2. Error analysis
  • 5.3. Report-program date gap
  • 6. Discussion and conclusion
  • References
  • Not All C-sections Are the Same: Investigating Emergency vs. Elective C-section deliveries as an Adverse Pregnancy Outcome
  • 1. Background and Significance
  • 2. Methods
  • 2.1. Dataset characteristics
  • 2.2. Identification of delivery outcomes
  • 2.2.1. Cesarean section deliveries
  • 2.2.2. Preterm birth, stillbirth, and multiple birth deliveries
  • 2.3. Integration of data from encounter records
  • 2.4. Generalized regression models
  • 3. Results
  • 3.1. Utilization of cesarean section codes
  • 3.2. Admission types recorded in encounter records.
  • 3.3. Age distribution by delivery admit type
  • 3.4. Number of deliveries by weekday and admit type
  • 4. Generalized regression model
  • 4.1. Surgical Incision Type for C-section and Effect on Emergency Admission
  • 5. Discussion
  • References
  • Co-occurrence Patterns of Intimate Partner Violence
  • 1. Introduction
  • 2. Materials and Methods
  • 2.1. Description of Data and Pre-Processing
  • 2.2. Co-Occurrence of Violence Types
  • 2.3. Co-Occurrence Network of Individual Violence Items
  • 2.4. Radial Visualization
  • 2.5. Clustering of Survivors and Identification of Subgroups
  • 2.6. Health Problems and Trauma Symptoms
  • 3. Results
  • 4. Discussion
  • 5. Acknowledgments
  • References
  • BIOCOMPUTING AND AI FOR INFECTIOUS DISEASE MODELLING AND THERAPEUTICS
  • Session Introduction: AI for Infectious Disease Modelling and Therapeutics
  • 1. Background
  • 2. Introduction
  • 3. Social Media and COVID-19
  • 4. Biomedical literature and COVID-19 plus neglected tropical diseases
  • 5. Genomics and HCV
  • 6. Protein intrinsically disordered regions and SARS-CoV-2
  • 7. Protein-protein interactions and SARS-CoV-2
  • References
  • Characterization of Anonymous Physician Perspectives on COVID-19 Using Social Media Data
  • 1. Introduction
  • 2. Methods
  • 2.1. Data Collection
  • 2.2. N-gram Frequency Measures
  • 2.3. Sentiment Analysis
  • 3. Results
  • 3.1. Frequency of terms and n-grams
  • 3.2. Sentiment analysis
  • 3.3. Sentiments of tweets containing specific terms
  • 4. Discussion and Conclusion
  • 5. Acknowledgments
  • References
  • Semantic Changepoint Detection for Finding Potentially Novel Research Publications
  • 1. Introduction
  • 2. Methods
  • 2.1. Data collection and general procedures
  • 2.2. Title and abstract entropies
  • 2.3. Bayesian changepoint analysis
  • 2.4. Differential word clouds
  • 2.5. Title and abstract embeddings.
  • 2.6. Semantic novelty
  • 2.6.1. Strategy T1: Novel paper detection based on semantic distance
  • 2.6.2. Strategy T2: Detection of novel papers that may constitute a trend
  • 2.6.3. Strategy Y1: Detection of a group of novel papers based on their mean vector
  • 2.6.4. Strategy Y2: Proportion of novel papers
  • 3. Results and Discussion
  • 4. Conclusions
  • 5. Supplementary Information
  • 6. Acknowledgements
  • References
  • TreeFix-TP: Phylogenetic Error-Correction for Infectious Disease Transmission Network Inference
  • 1. Background
  • 2. Methods
  • 2.1. Minimizing inter-host transmissions
  • 2.2. Description of TreeFix-TP
  • 2.3. Evaluation using simulated data sets
  • 2.3.1. Data set generation
  • 2.3.2. Evaluating reconstruction accuracy
  • 3. Results
  • 3.1. Phylogenetic error correction results
  • 3.2. Source recovery in HCV outbreaks
  • 3.3. Running time and scalability
  • 4. Discussion and Conclusions
  • Acknowledgments
  • Authors' Contributions
  • Supplementary Material
  • References
  • SARS-CoV-2 Drug Discovery based on Intrinsically Disordered Regions
  • 1. Introduction
  • 2. Methods
  • 2.1. Molecular docking
  • 2.1.1. Data collection
  • 2.1.2. Data preprocessing
  • 2.1.3. Target file generation
  • 2.1.4. Flexible docking
  • 2.1.5. Ensemble docking
  • 2.2. Statistical model
  • 2.2.1. Chemprop
  • 2.2.2. Data and training
  • 3. Results
  • 3.1. Interaction modelling
  • 3.2. Activity prediction
  • 4. Conclusion
  • 5. Acknowledgements
  • References
  • Feasibility of the Vaccine Development for SARS-CoV-2 and Other Viruses Using the Shell Disorder Analysis
  • 1. Introduction
  • 1.1. SARS-COV-2 Vaccine
  • 1.2. Shell disorder analysis of HIV and other viruses
  • 1.3. Spinoff projects including coronaviruses: Shell disorder and modes of transmission
  • 1.4. Yet another spinoff: Correlations between the inner shell disorder and virulence.
  • 2. Results
  • 2.1. Clustering of CoV based mainly on NPID
  • 2.2 Outer shell disorder is an indicator for the presence or absence of effective vaccines
  • 2.3. A disordered outer shell provides an immune evasion tactic: Viral shapeshifting
  • 2.4. SARS-CoV-2: Exceptionally hard shell (low MPID) associated with burrowing animals and buried feces
  • 2.5. Behavior of the animal hosts matters in the evolutions of the viruses: EIAV vs. HIV
  • 2.6. Feasibility of developing attenuated vaccine strains for SARS-CoV-2
  • 3. Discussion
  • 3.1. Links between respiratory transmission, N (Inner shell) disorder, and virulence: Viral load in body fluids vs. vital organs
  • 3.2. Greater disorder in the inner shell proteins provide means for the more efficient replication of viral particles
  • 3.3 Two modes of immune evasion: "Trojan Horse" (inner shell disorder) and "viral shapeshifting" (outer shell disorder)
  • 3.4. FIV, HIV-1 and HIV-2: Similarities and differences
  • 3.5. FIV vaccine enigma: Questionable efficacy
  • 4. Conclusions
  • 4.1. Development of the SARS-CoV-2 vaccine is feasible and vaccine strains can be found in nature
  • 5. Materials and Methods
  • References
  • Protein Sequence Models for Prediction and Comparative Analysis of the SARS-CoV-2−Human Interactome
  • 1. Introduction
  • 2. Methods
  • 2.1. Generalized Additive Models with interactions (GA2M)
  • 3. Gold Standard Interaction Datasets
  • 3.1. Dealing with the lack of negative examples
  • 3.2. Features
  • 4. Experiments
  • 4.1. TAPE: Transformer based model for protein sequences
  • 5. Results
  • 5.1. Prediction performance and validation of predicted interactions
  • 5.2. Enrichment analysis of predicted human binding partners
  • 6. Discussion
  • 6.1. Visualizing the virus-human interactions
  • 6.2. Highly ranked sequence features
  • 6.3. Structural analysis
  • 7. Prior Work
  • 8. Conclusion.
  • 9. Acknowledgements.

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