運用圖變換器和深度表示學習預測基於序列的蛋白質功能(1/3)

研究計畫: A - 政府部門b - 科技部

專案詳細資料

說明

This study establishes a novel framework that can improve the interpretation of protein sequences and the prediction of their functions using AlphaFold2-predicted structures, deep representative learning, and graph transformer. The developed framework will be applied to solve different problems of protein function problems (i.e., protein subcellular localization, gene ontology-based functions, or general functions) that have been proven to be the key mechanisms in molecular biology. This is the first study that observes this combination to protein function prediction in particular and bioinformatics sequences in general. For biological insights, we suggest that our method could provide useful information for biologists studying protein sequence patterns or pathogenic mechanisms of mutations, and chemists interested in targeted drug design.
狀態進行中
有效的開始/結束日期8/1/227/31/23

Keywords

  • 計算生物學
  • 自然語言處理
  • 深度學習
  • 圖變換器
  • 蛋白質功能預測
  • 序列分析
  • 神經網絡
  • AlphaFold2
  • 蛋白質結構
  • 運輸蛋白
  • Gene Ontology
  • 分子功能
  • 精準醫學
  • 藥物設計

指紋

探索此專案觸及的研究主題。這些標籤是根據基礎獎勵/補助款而產生。共同形成了獨特的指紋。