The Bioinformatics Track (BINF) at the ACM Symposium on Applied Computing (SAC) aims to bring together leading researchers, bioinformaticians, and early-career scientists to share recent advances in bioinformatics and explore their applications in cancer genomics, structural biology, transcriptomics, personalized medicine, and computational biology.

Topics of interest include, but are not limited to:

• Genomics: Methods and tools for genome assembly, annotation, comparative genomics, and identification of genetic variation.

• Transcriptomics: Analysis of RNA sequencing data for gene expression profiling, alternative splicing, RNA processing, and transcriptional regulation.

• Proteomics: Computational approaches for mass spectrometry data analysis, protein identification, protein–protein interaction networks, and functional annotation.

• Metabolomics: Computational methods for metabolic profiling, biomarker discovery, and metabolic pathway analysis.

• Cancer Genomics: Computational approaches for identifying somatic mutations, structural variants, and molecular mechanisms underlying cancer, enabling precision oncology.

• Structural Biology: Modeling, prediction, and analysis of biomolecular structures using computational methods and experimental data, including cryo-electron microscopy and X-ray crystallography.

• Systems Biology: Integration of multi-omics datasets to model biological networks, pathways, and cellular processes.

• Personalized Medicine: Computational analysis of genomic and molecular data to predict disease risk, guide diagnosis, and optimize individualized therapies.

• Biological Data Visualization: Interactive and scalable methods for visualizing complex biological datasets and analytical results.

• High-Throughput Sequencing: Algorithms, pipelines, and software for the analysis of next-generation and third-generation sequencing data.

• Big Data and Cloud Computing: Scalable methods for storing, managing, processing, and analyzing large-scale biological and biomedical datasets.

• Artificial Intelligence in Bioinformatics: Machine learning, deep learning, and generative AI approaches for biological data analysis, biomarker discovery, functional prediction, and knowledge extraction.

• Showcase innovative bioinformatics methods, software, and computational pipelines that advance biological and biomedical research.

• Foster discussions on the integration and analysis of large-scale multi-omics datasets to uncover novel biological insights.

• Highlight emerging advances in artificial intelligence, machine learning, and data-driven approaches for bioinformatics applications.

• Promote interdisciplinary collaboration among researchers from biology, medicine, computer science, statistics, and related fields.

• Address challenges and opportunities in the storage, management, processing, visualization, and reproducible analysis of large-scale biological data.

• Originality and significance of the research.

• Innovation and applicability of the bioinformatics methods.

• Clarity and conciseness of the presentation.

• Potential for sparking discussion and collaboration.

The BINF Track will follow the deadlines suggested by the ACM SAC 2027 conference.

• October 2, 2026 - Submission of regular papers and SRC research abstracts

• November 13, 2026 - Notification of paper and SRC acceptance/rejection

• Jae Yeon Hwang (University of Louisville, USA)

• Zhenhua Shang (University of Louisville, USA)