This workshop will introduce the terminology and concepts of machine learning, including random forests, gradient boosting classifiers, and neural networks, and how they can be applied to research. In addition to theory, participants will be able to work with a sample dataset to have a more practical understanding of how to implement simple machine learning strategies and apply them to their own datasets. This workshop will focus on implementation in Python using sci-kit.

Molecular modeling is a popular technique used commonly for research in the fields of biology, chemistry, polymer sciences, and more. In particular, physical and chemical aspects of biomolecules are regularly investigated using molecular dynamics simulations. Molecular docking is used for investigating protein-protein, protein-DNA, protein-RNA, and other biomolecular docking. Further, molecular docking is routinely used in drug discovery.

This workshop will provide hands on introduction to molecular modeling. The topics covered will include using biomolecular visualization software such as PyMOL and Chimera, using molecular dynamics and molecular docking software. Quantum chemistry (electronic structure calculations) will also be covered briefly. Different software used for these calculations will also be discussed.

No prior experience is required, and especially recommended for researchers who would like to use molecular dynamics simulations and docking in their research.

The advancement of sequencing technology and its affordability have enabled the exploration of gene expression in different organisms and under different conditions.  Delving into the transcriptomics via RNA-seq technique allowed us to explore genes regulation and relations with higher resolution than ever before.

This advanced hands-on workshop aims to cover the Bioinformatics aspects of transcriptomics research using the Linux platform.
It is expected that you have familiarity in Linux basic commands and in “R”.