This workshop is designed for beginners interested in the intersection of biology and programming. Participants will be introduced to fundamental bioinformatics concepts and computational techniques used for analyzing biological data. The workshop will cover key programming skills, data processing, and real-world applications in genomics, transcriptomics, and proteomics.​

 This workshop focuses on the application of machine learning techniques in computational genomics. Participants will explore supervised and unsupervised learning approaches, deep learning architectures, and their applications in genomic data analysis. The workshop includes hands-on training with real-world datasets and bioinformatics pipelines to empower attendees with the skills to apply machine learning in genomics.

 This workshop focuses on the integration of biostatistical methods and artificial intelligence (AI) models for interpreting and analyzing biomedical data. Participants will explore key statistical techniques, casual models, machine learning algorithms, and deep learning frameworks that are widely used in clinical and genomic research. The hands-on training will enable attendees to apply these techniques to real-world datasets, improving their ability to extract insights and make data-driven decisions.​

This workshop explores the application of deep learning techniques in multimodal and biomedical imaging analysis. Participants will gain a comprehensive understanding of convolutional neural networks (CNNs), transformers, and generative models used in medical image processing. Hands-on sessions will provide experience in analyzing multimodal datasets, including MRI, CT, histopathology, and genomic imaging, using deep learning frameworks.​

This workshop introduces participants to the principles of semantic analysis in biomedical knowledge graphs (KGs) and their applications in precision medicine. Attendees will explore how semantic relationships between biomedical entities can enhance data integration, reasoning, and decision-making in clinical and genomic research. Hands-on sessions will cover knowledge graph construction, semantic enrichment, and reasoning techniques to extract meaningful insights from complex biomedical data.

This workshop explores the integration of large language models (LLMs) with knowledge graph (KG) generation techniques to extract meaningful insights from biomedical reports and clinical notes. Participants will gain hands-on experience in natural language processing (NLP), entity extraction, and graph-based representation of medical knowledge. The workshop emphasizes AI-driven automation for structured biomedical data representation and decision-making

This workshop explores the integration of artificial intelligence (AI) models in omics data analysis, covering genomics, transcriptomics, proteomics, and metabolomics. Participants will learn how AI-driven approaches enhance the interpretation of complex biological data, enabling new insights in precision medicine and systems biology. Hands-on sessions will provide experience in AI-based feature selection, predictive modeling, and knowledge discovery in multi-omics datasets.

This workshop focuses on the application of advanced AI models in causal network analysis and reasoning with biomedical knowledge graphs (KGs). Participants will explore cutting-edge techniques for causal inference, knowledge representation, and AI-driven reasoning in biomedical research. Hands-on sessions will provide practical experience in constructing, analyzing, and interpreting biomedical knowledge graphs for causal discovery and decision-making.