Scholarlink

<p>We develop GeneRAIN, a suite of Transformer-based models that learn gene expression relationships from 410&#xa0;K human bulk RNA-seq samples. Featuring a novel Binning-By-Gene normalization technique, our models capture diverse biological information beyond expression. We introduce GeneRAIN-vec, a multifaceted vectorized gene representation that outperforms those from existing models. We demonstrate knowledge transfer from protein-coding genes to Make 62.5 million biological attribute predictions for 13,030 long noncoding RNAs. This work advances Transformer and self-supervised deep learning applications to expression data, enhancing biological exploration.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

GeneRAIN: multifaceted representation of genes via deep learning of gene expression networks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月22日

查看全文
  • Zheng Su,
  • Mingyan Fang,
  • Andrei Smolnikov,
  • Marcel E. Dinger,
  • Emily C. Oates,
  • Fatemeh Vafaee

摘要

We develop GeneRAIN, a suite of Transformer-based models that learn gene expression relationships from 410 K human bulk RNA-seq samples. Featuring a novel Binning-By-Gene normalization technique, our models capture diverse biological information beyond expression. We introduce GeneRAIN-vec, a multifaceted vectorized gene representation that outperforms those from existing models. We demonstrate knowledge transfer from protein-coding genes to Make 62.5 million biological attribute predictions for 13,030 long noncoding RNAs. This work advances Transformer and self-supervised deep learning applications to expression data, enhancing biological exploration.