Bayesian Methods in Bioinformatics
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".
Deadline for manuscript submissions: closed (15 November 2022) | Viewed by 2627
Special Issue Editor
Interests: artificial intelligence; machine learning; Bayesian approaches; probabilistic graphical models; causality research; chemoinformatics; bioinformatics
Special Issue Information
Dear Colleagues,
Bayesian methods and bioinformatics have a long-standing, mutually inspiring relationship. On one hand, the Bayesian approach has allowed for the development of cutting-edge bioinformatics solutions, including precision sequencing, genotypic data imputation, phylogenetic analysis, information fusion for drug, gene, and variant prioritization, genome-wide association studies (GWASes), distillation of multimorbidity dependency maps, and de novo molecule generation. On the other hand, novel bioinformatics data sets and problems motivate further developments of Bayesian methods, such as federated biobanks with hierarchical clinical data and heterogeneous omics data; temporal electronic health records with multiple resolutions, irregularly sampled, self-quantified data; heterogeneous, large-scale drug-target interaction data, increasingly including natural products beyond small synthetic compounds, and protein/nucleotide drug candidates; omics-wide summary statistics for associations and causal effects for the entire phenome; and cross-domain, cross-species semantic linked open data (LOD) with more and more quantitative uncertainty information.
In addition to the rich variety of Bayesian bioinformatics results, the Bayesian approach also provides a consistent and overarching framework for the whole scientific lifecycle: quantifying the value of scientific data; designing experiments; coping with noisy and incomplete data; combining multiple data sets, partial statistics and knowledge fragments; model averaging; and reporting full posteriors. Significantly, the Bayesian approach allows for the construction of a novel, intermediate layer of scientific knowledge between data and interpreted scientific conclusions via the systematic reporting of posteriors. Large-scale inference using such kinds of omics-wide statistics has already led to significant discoveries, e.g., causal inference using GWAS summary statistics.
This Special Issue invites papers that advance computational developments of Bayesian bioinformatics with particular emphasis on Bayesian publishing, i.e., reporting posteriors systematically and semantically as an intermediate quantitative layer of scientific knowledge (see semantic publishing, linked data). Submissions that discuss the generation, sharing, and combination of posteriors linking multiple phases of data analysis, different domains, species, and abstraction hierarchy levels are especially welcome. Papers that concern artificial intelligence and active learning methods autonomously using Bayesian linked data for automated scientific discovery are also encouraged.
Dr. Peter Antal
Guest Editor
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Keywords
- data and knowledge fusion
- approximate Bayesian computation
- probabilistic graphical models
- summary statistics
- linked open data
- federated learning
- active learning
- causal inference
- design of experiments
- machine science
- discovery systems
- drug discovery
- bioinformatics
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