Artificial Intelligence and Computation for Biology

Our Artificial Intelligence (AI) and Computational Biology research integrates advanced computational techniques with biological sciences to accelerate discoveries and innovation. By developing new models, algorithms, databases, and software, we aim to address complex biological challenges and expand the frontiers of biomedical research.

Key Areas of Research:

Development of New Computational Models

  • Creating mathematical and AI-driven models to simulate and predict biological phenomena, such as protein folding, gene regulation, and cellular behavior.
  • Designing systems biology models to understand interactions across molecular, cellular, and organismal levels.
  • Incorporating machine learning (ML) and deep learning (DL) methods to interpret large-scale biological data with improved accuracy and scalability.

Innovative Algorithm Design

  • Developing novel algorithms for analyzing high-throughput omics data, including genomics, transcriptomics, proteomics, and metabolomics.
  • Enhancing algorithms for sequence alignment, structural prediction, and functional annotation of biomolecules.
  • Creating AI-driven optimization methods for drug discovery, biomarker identification, and personalized medicine.

Creation of New Databases

  • Building comprehensive, user-friendly databases to house biological data, including genetic, proteomic, structural, and interaction datasets.
  • Designing platforms that integrate multi-omics data for holistic analysis and visualization.
  • Ensuring databases are scalable, interoperable, and equipped with robust search and retrieval functionalities to support researchers worldwide.

Development of Software Tools

  • Designing intuitive and high-performance software for analyzing, visualizing, and interpreting complex biological data.
  • Creating open-source tools and pipelines for reproducible and collaborative research.
  • Integrating computational tools with laboratory workflows for seamless experimental design and data interpretation.

Applications in Biology and Medicine

  • Applying computational tools to study diseases at the molecular level, enabling better diagnostics and treatment strategies.
  • Supporting the design of synthetic biology constructs, drug candidates, and novel biotechnological applications.
  • Bridging computational and experimental biology to accelerate translational research in health and disease.

Collaborative and Multidisciplinary Focus

  • Partnering with biologists, clinicians, and computational scientists to solve real-world biological problems.
  • Sharing tools, data, and resources to foster an open and collaborative research ecosystem.
  • Our research in AI and Computational Biology is dedicated to developing cutting-edge tools and methods that empower the scientific community, drive innovation, and transform biological understanding for real-world impact.