Learn Beyond Boundaries
Start, Learn, Switch, and Enhance your research and discovery with advanced AI training and courses in Bioinformatics and Computational Biology for Precision Medicine.
Bioinformatics
Delivers integrated wet lab and computational workflows for advanced multiomics analysis
AI Computational Biology
Applies artificial intelligence to model and predict biological systems
Specialized Training
We customize strategies to meet your unique biological research needs, driving innovation in precision medicine
Bioinformatics Training
Pathogen Genomics: From NGS Data to Biological Complexity in Pathogen Variant Analysis
This workshop aims to empower bioinformaticians with the skills needed to implement comprehensive Next-Generation Sequencing workflows for pathogen genomics. Participants will learn to process sequencing reads, identify genetic variations, filter data, and analyze pathogen variants using state-of-the-art computational tools on real medical problems.
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Time: 5 days ( 25 hours )
Level: Beginners
Format: Online, hands-on workshops
Module: Genomics
Target: Scientists, researchers, bioinformaticians, and students
Main outcomes: Implementing Computational Workflows for Pathogen analysis ​
Bioinformatics for All - Bioinformatics Models, Methods and Algorithms
This workshop provides a comprehensive introduction to bioinformatics, covering key models, methods, and algorithms. Participants will explore sequence alignment, phylogenetics, and data analysis while applying bioinformatics techniques to real-world biological data. Hands-on training and virtual office hours ensure personalized guidance and skill development.
Time: 5 days ( 25 hours )
Level: Beginners
Format: In-person, hands-on workshops
Module: Bioinformaticians tools
Target: Scientists, researchers, and students
Main outcomes: Understand and apply bioinformatics techniques to biological data analysis​
Introduction of Multiomics Workflow and Bioinformatics Pipelines for Beginners
This workshop focuses on introducing bioinformatics pipelines to researchers and practitioners who are new to computational biology. The session covers fundamental concepts, tools, and frameworks used in pipeline development for multiomics data analysis. Participants will gain hands-on experience in constructing and executing bioinformatics workflows using popular tools such as Nextflow, Snakemake, and Galaxy. The goal is to equip attendees with the knowledge to automate repetitive tasks and enhance the reproducibility of bioinformatics analyses.
Time: 5 days ( 25 hours )
Level: Intermediate
Format: In-person, hands-on workshops
Module: Multiomics
Target: Scientists, researchers, bioinformaticians, and students
Main outcomes: Bioinformatic workflows Implementation and integration ​
Introduction of NextGen Omics and Spatial Biology Analysis
This workshop focuses on the integration of next-generation sequencing (NGS) technologies with spatial biology to provide comprehensive insights into tissue-specific gene expression and cellular interactions. Participants will explore various multiomics techniques, including transcriptomics, proteomics, and metabolomics, and learn how spatial omics enhances biological interpretations. The workshop includes hands-on sessions with state-of-the-art bioinformatics tools for data analysis, visualization, and interpretation.
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Time: 5 days ( 25 hours )
Level: Beginners
Format: Online, hands-on workshops
Module: Genomics
Target: Scientists, researchers, bioinformaticians, and clinicians
Main outcomes: Analyze and interpret spatial transcriptomics, proteomics data
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Computational Biology
Biology Meets Programming: Bioinformatics for Beginners
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.​
Time: 5 days ( 25 hours )
Level: Beginners
Format: Online, hands-on workshops
Module: Biomedical data
Target: Data scientists, bioinformaticians, and students
Main outcomes: Implementing python and R scripts for biomedical analysis​
Machine Learning in Computational Genomics Analysis
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.
Time: 5 days ( 25 hours )
Level: Beginners
Format: In-person, hands-on workshops
Module: Genomics and biomedical data
Target: Bioinformaticians, computational biologists, and students
Main outcomes: Building and optimizing ML models for genomics applications​
Biostatistical and AI Models for Biomedical Data Interpretation and Analysis
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.​
Time: 5 days ( 25 hours )
Level: Beginners
Format: Online, hands-on workshops
Module: Biomedical data
Target: Biomedical researchers, clinicians, and students
Main outcomes: Implementing biostatistical and AI models in Python and R on real-world biomedical datasets​
Deep Learning in Multimodal data and Biomedical Imaging Analysis
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.​
Time: 5 days ( 25 hours )
Level: Beginners
Format: Online, hands-on workshops
Module: Multimodal data
Target: AI practitioners in healthcare, imaging scientists, and students
Main outcomes: Implementing deep learning pipelines in Python using PyTorch for Biomedical assessment ​
Introduction to Semantic Analysis of Biomedical Knowledge Graphs and Its Applications in Precision Medicine
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.
Time: 5 days ( 25 hours )
Level: Intermediate
Format: In-person, hands-on workshops
Module: Biomedical graph
Target: AI practitioners in healthcare, ihealthcare professionals, and students
Main outcomes: Constructing and analyzing semantic biomedical knowledge graph ​
GraphMedAI: Building Knowledge Graphs from Clinical Reports Using LLMs
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
Time: 5 days ( 25 hours )
Level: Advanced
Format: Online, hands-on workshops
Module: Multimodal data
Target: AI practitioners in healthcare, imaging scientists, and students
Main outcomes: I ​ Implementing, constructing and visualizing knowledge graphs from real-world datasets
AI-Powered Bioinformatics: AI Model Integration for Omics Analysis
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.
Time: 5 days ( 25 hours )
Level: Intermediate
Format: Online, hands-on workshops
Module: Biomedical data
Target: Bioinformaticians and computational biologists
Main outcomes: Implement best practices for reproducibility and interpretability in AI-based bioinformatics
Advanced AI Models for Causal Network and Reasoning with Biomedical Knowledge Graphs: Interpretation and Analysis
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.
Time: 5 days ( 25 hours )
Level: Advanced
Format: Online, hands-on workshops
Module: Causal Knowledge graph
Target: Scientists, researchers, bioinformaticians, and students
Main outcomes: Implementing Building causal knowledge graphs from real-world biomedical data ​
Specialized Training
GenAI4DigitalHealth: Application of Generative AI for Digital Health and Personalized Medicine
This workshop explores the role of generative artificial intelligence (GenAI) in digital health and personalized medicine. Participants will learn how AI-driven generative models enhance biomedical data interpretation, predictive analytics, and personalized treatment strategies. Hands-on sessions will cover generative AI techniques for multi-omics data integration, digital twin modeling, and AI-assisted diagnostics.​
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Time: 5 days ( 25 hours )
Level: Advanced
Format: Online, hands-on workshops
Module: Genomics
Target: Clinicians, data scientists, AI practitioners in digital health and personalized medicine, and students
Main outcomes: Implement generative models for predictive diagnostics and treatment optimization.
Causal AI for Disease Diagnosis: Risk Assessment and Proactive Healthcare
This workshop explores the development and application of AI-driven risk assessment systems for disease diagnosis and proactive healthcare using causal knowledge graphs. Participants will learn how AI models integrate structured biomedical knowledge and causal reasoning to enhance disease risk prediction and personalized treatment planning.
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Time: 5 days ( 25 hours )
Level: Beginners
Format: In-person, hands-on workshops
Module: Bioinformaticians tools
Target: Healthcare professionals, biomedical researchers, and AI scientists
Main outcomes: Understand and apply bioinformatics techniques to biological data analysis​
Advanced AI Models and Systems in Biomedicine and Digital Twin Systems and Applications
This workshop explores the role of artificial intelligence (AI) in biomedicine and its integration with digital twin systems. Participants will learn how AI-driven models enhance biomedical research, personalized medicine, and disease modeling through digital twins. Hands-on sessions will cover AI applications in genomics, real-time patient monitoring, and simulation-based precision medicine.
Time: 5 days ( 25 hours )
Level: Intermediate
Format: Online, hands-on workshops
Module: Genomics
Target: AI practitioners in healthcare and personalized medicine
Main outcomes: Building a simple AI-driven digital twin model and visualizing and interpreting biomedical simulations​
AI in Cheminformatics: Techniques, Artificial Intelligence, and Machine Learning
This workshop explores the application of artificial intelligence (AI) and machine learning (ML) in cheminformatics. Participants will learn how AI-driven approaches enhance molecular property prediction, drug discovery, and chemical data analysis. Hands-on sessions will provide experience in AI-based feature extraction, predictive modeling, and molecular simulations.
Time: 5 days ( 25 hours )
Level: Intermediate
Format: In-person, hands-on workshops
Module: Genomics
Target: Computational chemists, cheminformaticians, and students
Main outcomes: Implementing AI models on real chemical datasets and visualising of AI-based cheminformatics results​
Computational Bootcamp
Introduction to Programming for Bioinformatics Bootcamp
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This bootcamp provides an intensive introduction to programming for bioinformatics applications. Participants will learn fundamental programming skills with a focus on analyzing biological data using Python and R. The bootcamp includes hands-on coding exercises, practical bioinformatics workflows, and best practices for reproducible research.​​
Time: 10 Days (wet lab + dry lab)
Level: Beginners
Format: Online, hands-on workshops
Module: Genomics
Target: Doctors, researchers, bioinformaticians, Biologists, and students
Main outcomes: Understand the basics of programming in Python and R and implementing computational workflows for biomedical data analysis ​
Pathogen Genomics Bootcamp: From Wet Lab to Pathogen Analysis
This 10-day intensive training program is designed to equip participants with hands-on wet-lab techniques and advanced computational workflows for pathogen genomics. The workshop covers DNA extraction, sequencing, and analysis of bacterial, viral, fungal, and parasitic genomes, focusing on public health, epidemiology, and clinical applications.
Time: 10 Days (wet lab + dry lab)
Level: Beginners
Format: Hybrid, hands-on workshops
Module: Genomics
Target: Scientists, Microbiologists, bioinformaticians, and students
Main outcomes: Understand the basics of shell command and Implementing workflows for Pathogen analysis ​