Format:
1 Online-Ressource (xii, 359 Seiten)
ISBN:
9789811232701
Content:
The Pacific Symposium on Biocomputing (PSB) 2021 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2021 will be held on a virtual platform at psb.stanford.edu/ on January 5-7, 2021. Tutorials and workshops will be offered prior to the start of the conference.PSB 2021 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field.
Content:
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.
Note:
Description based on publisher supplied metadata and other sources
Additional Edition:
ISBN 9789811232695
Additional Edition:
Erscheint auch als Druck-Ausgabe ISBN 9789811232695
Language:
English
Keywords:
Biocomputer
;
Bioinformatik
;
Konferenzschrift
