Non-local modeling of enhancer-promoter interactions, a correspondence on “LOCO-EPI: Leave-one-chromosome-out (LOCO) as a benchmarking paradigm for deep learning based prediction of enhancer-promoter interactions”

IF 3.4 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Michael A. Beer
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引用次数: 0

Abstract

A recent paper by Tahir et al. (Appl Intell 55:71, 2024) in Applied Intelligence reported a computational model of enhancer promoter interactions without realizing that many of their conclusions were previously published in 2018. In addition to correcting this record, the authors appear to be unaware of an additional body of previous work on enhancer-promoter interactions, which can explain why their computational model performs poorly. We describe how the weak predictive power of their model is consistent with new insights gained from substantial recent progress in the area of detecting and modeling enhancer promoter interactions constrained by DNA looping, extrusion by cohesin, and CTCF.

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来源期刊
Applied Intelligence
Applied Intelligence 工程技术-计算机:人工智能
CiteScore
6.60
自引率
20.80%
发文量
1361
审稿时长
5.9 months
期刊介绍: With a focus on research in artificial intelligence and neural networks, this journal addresses issues involving solutions of real-life manufacturing, defense, management, government and industrial problems which are too complex to be solved through conventional approaches and require the simulation of intelligent thought processes, heuristics, applications of knowledge, and distributed and parallel processing. The integration of these multiple approaches in solving complex problems is of particular importance. The journal presents new and original research and technological developments, addressing real and complex issues applicable to difficult problems. It provides a medium for exchanging scientific research and technological achievements accomplished by the international community.
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