18 February 2023
  • Title:Manifold Learning and Artificial Intelligence - A Path to Protein Design and Disease Mechanism (1) Protein Language Model
  • Date:09:00pm US East time, 02/18/2023
  • Date:10:00am Beijing time, 02/19/2023
  • Zoom ID:933 1613 9423
  • Zoom PWD:416262

  • Momiao Xiong, Ph. D, Professor in Department of Biostatistics snd Data Science , University of Texas, School of Public Health. Dr. Xiong graduated from the Department of Statistics at the University of Georgia in 1993. From 1993 to 1995, Dr. Xiong was postdoctoral fellow at the University of Southern California working with Michael Waterman.

  • Research Interest: Causal Inference, Artificial Intelligence , Manifold Learning, Statistic Genetics and Bioinformatics .

  • https://theaisummer.com/diffusion-models/#:~:text=Diffusion%20models%20are%20a%20new,to%20train%20large%2Dscale%20models.

  • https://towardsdatascience.com/understanding-graph-convolutional-networks-for-node-classification-a2bfdb7aba7b

  • https://medium.com/red-buffer/implementation-and-understanding-of-graph-neural-networks-gnn-54084c8a0e24


Proteins are the workhorses of living organisms and are involved in virtually every biological process. They carry out essential functions such as catalyzing reactions, transporting molecules, and transmitting signals. Understanding the structure and function of proteins is crucial to developing new therapies for diseases and improving our understanding of the mechanisms underlying them.

One approach to gaining insight into protein structure and function is through the development of protein language models. These models are built using machine learning algorithms that analyze large datasets of protein sequences and structures. The models can then be used to predict the structures and functions of novel proteins based on their amino acid sequences.

One example of a protein language model is AlphaFold, developed by researchers at the University of Washington and the European Molecular Biology Laboratory. AlphaFold uses deep neural networks to predict the three-dimensional structures of proteins, which can be used to understand their functions and develop new drugs. In 2020, AlphaFold made headlines when it accurately predicted the structure of a protein related to COVID-19, paving the way for the development of new treatments.

Protein language models can also be used to understand the mechanisms underlying diseases. For example, many diseases are caused by mutations in proteins that disrupt their function. By analyzing the effects of mutations on protein structure and function, researchers can gain insight into the mechanisms underlying disease and develop new treatments.

In summary, protein language models are a powerful tool for understanding protein structure and function, as well as disease mechanisms. They have the potential to revolutionize the way we develop new therapies for diseases and improve our understanding of the biological processes that underlie them.

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