模型不可知半监督的超级学习阐明了研究不足的分布外分子相互作用
1Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, New York, USA.
bioRxiv : the preprint server for biology
|June 9, 2023
概括
一个新的半监督的元学习框架,MMAPLE,用有限的数据解决了研究不足的生物学问题. 它有效地使用未标记的数据来预测相互作用,即使有分布转移,加速科学发现.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
背景情况:
- 生物学研究面临着来自实验约束和人类偏见的局限性,导致研究不足的领域.
- 深度学习模型与稀缺的标记数据和数据分布转移作斗争,阻碍了科学发现.
结论:
- MMAPLE是一个多功能框架,用于探索以前无法通过实验和计算方法访问的新生物领域.
- 它增强了与训练数据不相似的涉及化学物质或蛋白质的相互作用的预测.
- 这种方法通过有效利用未标记的数据,加速了对研究不足的生物学问题的发现.
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