Artificial intelligence in the life sciences最新文献_第10页

Learning functional group chemistry from molecular images leads to accurate prediction of activity cliffs 从分子图像中学习官能团化学可以准确预测活性悬崖

Artificial intelligence in the life sciences Pub Date : 2021-12-01 DOI: 10.1016/j.ailsci.2021.100022

Javed Iqbal, Martin Vogt, Jürgen Bajorath

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引用次数: 4

AutoGenome: An AutoML tool for genomic research AutoGenome:一个用于基因组研究的AutoML工具

Artificial intelligence in the life sciences Pub Date : 2021-12-01 DOI: 10.1016/j.ailsci.2021.100017

Denghui Liu , Chi Xu , Wenjun He , Zhimeng Xu , Wenqi Fu , Lei Zhang , Jie Yang , Zhihao Wang , Bing Liu , Guangdun Peng , Dali Han , Xiaolong Bai , Nan Qiao

{"title":"AutoGenome: An AutoML tool for genomic research","authors":"Denghui Liu , Chi Xu , Wenjun He , Zhimeng Xu , Wenqi Fu , Lei Zhang , Jie Yang , Zhihao Wang , Bing Liu , Guangdun Peng , Dali Han , Xiaolong Bai , Nan Qiao","doi":"10.1016/j.ailsci.2021.100017","DOIUrl":"https://doi.org/10.1016/j.ailsci.2021.100017","url":null,"abstract":"<div><p>Deep learning has achieved great successes in traditional fields like computer vision (CV), natural language processing (NLP), speech processing, and more. These advancements have greatly inspired researchers in genomics and made deep learning in genomics an exciting and popular topic. The convolutional neural network (CNN) and recurrent neural network (RNN) are frequently used to solve genomic sequencing and prediction problems, and multiple layer perception (MLP) and auto-encoders (AE) are frequently used for genomic profiling data like RNA expression data and gene mutation data. Here, we introduce a new neural network architecture-the residual fully-connected neural network (RFCN)-and describe its advantage in modeling genomic profiling data. We also incorporate AutoML algorithms and implement AutoGenome, an end-to-end, automated deep learning framework for genomic studies. By utilizing the proposed RFCN architecture, automatic hyper-parameter search, and neural architecture search algorithms, AutoGenome can automatically train high-performance deep learning models for various kinds of genomic profiling data. To help researchers better understand the trained models, AutoGenome can assess the importance of different features and export the most critical features for supervised learning tasks and the representative latent vectors for unsupervised learning tasks. We expect AutoGenome will become a popular tool in genomic studies.</p></div>","PeriodicalId":72304,"journal":{"name":"Artificial intelligence in the life sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667318521000179/pdfft?md5=cf6b23a0b87a53ab56b10caf93790902&pid=1-s2.0-S2667318521000179-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136694939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GPCR Dock 2021: a blind docking competition in the post AlphaFold2 era GPCR对接2021:后AlphaFold2时代的盲对接竞赛

Artificial intelligence in the life sciences Pub Date : 2021-11-01 DOI: 10.1016/j.ailsci.2021.100024

Suwen Zhao

引用次数: 0

Erratum regarding missing Conflict of Interest Statement & Ethical Statement in previously published articles 关于先前发表的文章中缺少利益冲突声明和道德声明的勘误表

Artificial intelligence in the life sciences Pub Date : 1900-01-01 DOI: 10.1016/j.ailsci.2023.100076

引用次数: 0

Corrigendum to “Optimizing active learning for free energy Calculations” [Artificial Intelligence in the Life Sciences, 2 (2022) 100050] “优化自由能计算的主动学习”的勘误表[生命科学中的人工智能，2 (2022)100050]

Artificial intelligence in the life sciences Pub Date : 1900-01-01 DOI: 10.1016/j.ailsci.2023.100074

James Thompson, W. Walters, Jianwen A. Feng, Nicolas A. Pabon, Hongcheng Xu, Brian B. Goldman, D. Moustakas, M. Schmidt, Forrest York

引用次数: 0

An industrial evaluation of proteochemometric modelling: Predicting drug-target affinities for kinases 蛋白质化学模型的工业评价:预测激酶的药物靶标亲和力

Artificial intelligence in the life sciences Pub Date : 1900-01-01 DOI: 10.1016/j.ailsci.2023.100079

Astrid Stroobants, Lewis H. Mervin, O. Engkvist, G. Robb

引用次数: 0

A natural language processing system for the efficient updating of highly curated pathophysiology mechanism knowledge graphs 一个自然语言处理系统，用于高效地更新高度策划的病理生理机制知识图谱

Artificial intelligence in the life sciences Pub Date : 1900-01-01 DOI: 10.1016/j.ailsci.2023.100078

Negin Sadat Babaiha, H. Elsayed, Bide Zhang, Abish Kaladharan, Priya Sethumadhavan, Bruce Schultz, Jürgen Klein, Bruno Freudensprung, V. Lage-Rupprecht, A. Kodamullil, M. Jacobs, Stefan Geißler, S. Madan, M. Hofmann-Apitius

引用次数: 0