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BIOKDD 2020 : 19th International Workshop on Data Mining in Bioinformatics

San Diego, CA - USA
Event Date: Aug 24, 2020 - Aug 24, 2020
Submission Deadline: May 20, 2020
Notification of Acceptance: Jun 15, 2020
Camera Ready Version Due : Aug 24, 2020

About

The goal of the 19th International Workshop on Data Mining in Bioinformatics (BIOKDD'20) is to encourage KDD researchers to tackle the numerous challenges of mining and learning in Bioinformatics, Biomedical and Health Informatics. We are soliciting papers and abstracts that presenting research that utilizes Big Data technology, data visualization, and effective data mining and machine learning approaches to analyze large amounts of heterogeneous complex biological and medical data, together with those that describe innovative applications in Bioinformatics and Health Informatics. The key goal is to accelerate the convergence between Data Mining and Biomedical Informatics communities to expedite discoveries in basic biology, medicine and healthcare. Research that battles the COVID-19 pandemic is especially encouraged.

Call For Paper

We invite the submission of original research contributions in (but not limited to) the areas listed below. Quality papers accepted will be invited to publish in IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB).

  • Research that battles COVID-19
  • Bioimage analysis, single-cell analysis
  • Biological network visualization
  • Information visualization and visual analytics for biomedical data
  • Development of deep learning methods for biological and clinical data
  • Novel methods and frameworks for mining and integrating big biological data
  • Discovering biological networks and pathways underlying biological processes and diseases
  • Analysis, discovery of biomarkers and mutations, and disease risk assessment
  • Comparative genomics
  • Metagenome analysis using sequencing data
  • RNA-seq and microarray-based gene expression analysis
  • Genome-wide analysis of non-coding RNAs
  • Genome-wide regulatory motif discovery
  • Structural bioinformatics
  • Automated annotation of genes and proteins
  • Discovery of structural variations from next-generation sequencing (NGS) data
  • Correlating NGS with proteomics data analysis
  • Discovery of genotype-phenotype associations
  • Building predictive models for complex phenotypes
  • Functional annotation of genes and proteins
  • Cheminformatics
  • Special biological data management techniques
  • Privacy and security issues in mining genomic databases
  • Predictive modeling for personalized treatment
  • Semantic web and ontology-driven data integration methods
  • Text mining for biomedical literature and clinical notes
  • Information retrieval for healthcare and Biomedical applications
  • Biomedical signal analysis and processing
  • Intelligent medical data management
  • Collaboration technologies for biomedicine
  • Social networks for biomedicine

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