The goal of Bioinformatics rack (BINF) of the ACM SAC is to bring together leading researchers, bioinformaticians, and early-career scientists to explore the latest advancements in bioinformatics and their applications in cancer genomics, structural biology, personalized medicine, transcriptomics, and computational biology research

• Genomics: Tools and methods for identifying genetic variations, genome assembly, annotation, and comparative genomics.

• Transcriptomics: RNA sequencing data analysis for gene expression, alternative splicing, and transcription regulation.

• Proteomics: Mass spectrometry data analysis, protein-protein interaction mapping, and functional annotation of proteins.

• Metabolomics: Analyzing metabolic profiles, identifying biomarkers, and understanding metabolic pathways.

• Cancer Genomics: Identifying somatic mutations and structural alterations to understand tumorigenesis and develop personalized treatments.

• Structural Biology: Modeling and predicting macromolecular structures using techniques like X-ray crystallography and cryo-electron microscopy.

• Systems Biology: Integrating multi-omics data to construct biological networks and pathways.

• Personalized Medicine: Analyzing genetic and molecular profiles to predict disease risk and tailor treatments.

• Visualization: Advanced tools for representing complex biological data.

• High-Throughput Sequencing: Algorithms and software for next-generation sequencing data.

• Big Data Management: Solutions for storing, processing, and analyzing vast biological datasets.

• AI in Bioinformatics: Using AI and machine learning to enhance data analysis and discover new biological insights.

• Showcase novel bioinformatics tools and pipelines.

• Foster discussion on large-scale omics data integration.

• Highlight emerging fields like machine learning and AI.

• Promote interdisciplinary collaboration.

• Discuss and share big data management challenges.

• 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.

Papers: TBA

• Jae Yeon Hwang (University of Louisville, USA)

• Zhenhua Shang (University of Louisville, USA)