The Abstracts track is a perfect place for bioinformatics researchers and medical informaticians to introduce their preliminary research outcomes to increase research impact and to nurture collaborations. We emphasize research effectiveness on real-world datasets. Submitted abstracts will be peer-reviewed by the program committee to make sure that the content would be of interest to the KDD audience including researchers, practitioners and trainees.
Authors of accepted abstracts will be asked to give a short oral presentation. Quality ones will be invited for journal paper extension to publish in IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB). We invite the submission of abstracts about research contributions in (but not limited to) the areas listed below:
Protein foundational models
AI-driven therapeutics development
Large language models for bioinformatics and computational biology
Generative AI for bioinformatics and computational biology
Semantic web, biomedical ontologies and ontology-driven data integration methods
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
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
Bioimage analysis, single-cell analysis
Submission Guidelines
Abstracts should be at most 1 page in length, single-spaced, in font size 10 or larger with one-inch margins on all sides. Using the ACM Proceedings Format is highly recommended. An abstract should be submitted in PDF format through Microsoft CMT at the following link: https://cmt3.research.microsoft.com/BIOKDD2025. The submissions will be judged for quality and relevance through single-blind reviewing.
Acknowledgment
The Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.
