GroupPost20241029-Maple & AlphaFold2

MAPLE can be used to predict methylation age and disease risk. It achieves stable and precise results by eliminating batch effects through contrastive learning methods.

AF2, the champion of the 2021 CASP competition, is also the work that won the 2024 Nobel Prize and holds significant importance for protein engineering.

The following is an overview of the presentations by Yu Zhang & Bohao Lv:

MAPLE：

• Predicting an individual’s age and disease probability through methylation data
• Using contrastive learning methods to eliminate batch effects between methylation data from different sources
• Capturing biological factors related to disease risk using MAPLE
• Analyzing MAPLE results under the framework of aging biology

AF2

• Input feature construction: Multiple Sequence Alignment (MSA) + Pair representation
• Encoding part Evoformer:
  • MSA representation update: seq-based pair-bias self-attention + residues-based self-attention
  • Pair representation update: Triangular multiplicative + Triangular self-attention

The specific files can be found here: MAPLE.pdf & AF2.pdf