Learning genotype–phenotype associations from gaps in multi-species sequence alignments DOI Creative Commons

Uwaise Ibna Islam,

André Luiz Campelo dos Santos,

Ria Kanjilal

и другие.

Briefings in Bioinformatics, Год журнала: 2024, Номер 26(1)

Опубликована: Ноя. 22, 2024

Abstract Understanding the genetic basis of phenotypic variation is fundamental to biology. Here we introduce GAP, a novel machine learning framework for predicting binary phenotypes from gaps in multi-species sequence alignments. GAP employs neural network predict presence or absence solely alignment gaps, contrasting with existing tools that require additional and often inaccessible input data. can be applied three distinct problems: species known associated genomic regions, pinpointing positions within such regions are important phenotypes, extracting sets candidate phenotypes. We showcase utility by exploiting well-known association between L-gulonolactone oxidase (Gulo) gene vitamin C synthesis, demonstrating its perfect prediction accuracy 34 vertebrates. This exceptional performance also applies more generally, achieving high power on large simulated dataset. Moreover, predictions synthesis unknown status mirror their phylogenetic relationships, predictive importance consistent those identified previous studies. Last, genome-wide application identifies many genes may analysis these candidates uncovers functional enrichment immunity, widely recognized role C. Hence, represents simple yet useful tool genotype–phenotype associations addressing diverse evolutionary questions data available broad range study systems.

Язык: Английский

DTI-MHAPR: optimized drug-target interaction prediction via PCA-enhanced features and heterogeneous graph attention networks DOI Creative Commons
Guang Yang, Yinbo Liu,

SiJian Wen

и другие.

BMC Bioinformatics, Год журнала: 2025, Номер 26(1)

Опубликована: Янв. 13, 2025

Drug-target interactions (DTIs) are pivotal in drug discovery and development, their accurate identification can significantly expedite the process. Numerous DTI prediction methods have emerged, yet many fail to fully harness feature information of drugs targets or address issue redundancy. We aim refine accuracy by eliminating redundant features capitalizing on node topological structure enhance extraction. To achieve this, we introduce a PCA-augmented multi-layer heterogeneous graph-based network that concentrates key throughout encoding-decoding phase. Our approach initiates with construction graph from various similarity metrics, which is then encoded via neural network. concatenate integrate resultant representation vectors merge multi-level information. Subsequently, principal component analysis applied distill most informative features, random forest algorithm employed for final decoding integrated data. method outperforms six baseline models terms accuracy, as demonstrated extensive experimentation. Comprehensive ablation studies, visualization results, in-depth case analyses further validate our framework's efficacy interpretability, providing novel tool integrates multimodal features.

Язык: Английский

Процитировано

1
RNA structure prediction using deep learning — A comprehensive review DOI Creative Commons
Mayank Chaturvedi, Mahmood A. Rashid, Kuldip K. Paliwal

и другие.

Computers in Biology and Medicine, Год журнала: 2025, Номер 188, С. 109845 - 109845

Опубликована: Фев. 20, 2025

In computational biology, accurate RNA structure prediction offers several benefits, including facilitating a better understanding of functions and RNA-based drug design. Implementing deep learning techniques for has led tremendous progress in this field, resulting significant improvements accuracy. This comprehensive review aims to provide an overview the diverse strategies employed predicting secondary structures, emphasizing methods. The article categorizes discussion into three main dimensions: feature extraction methods, existing state-of-the-art model architectures, approaches. We present comparative analysis various models highlighting their strengths weaknesses. Finally, we identify gaps literature, discuss current challenges, suggest future approaches enhance performance applicability tasks. provides deeper insight subject paves way further dynamic intersection life sciences artificial intelligence.

Язык: Английский

Процитировано

1
Editorial: Machine Learning in Bio-cheminformatics DOI Open Access
Kenneth M. Merz, Guo‐Wei Wei, Feng Zhu

и другие.

Journal of Chemical Information and Modeling, Год журнала: 2024, Номер 64(7), С. 2125 - 2128

Опубликована: Апрель 8, 2024

ADVERTISEMENT RETURN TO ISSUEEditorialNEXTEditorial: Machine Learning in Bio-cheminformaticsKenneth M. Merz*Kenneth MerzDepartment of Chemistry, Michigan State University, Lansing 48824, Michigan, United States*(Email: [email protected]).More by Kenneth Merzhttps://orcid.org/0000-0001-9139-5893, Guo-Wei Wei*Guo-Wei WeiDepartment Mathematics, Weihttps://orcid.org/0000-0002-5781-2937, and Feng Zhu*Feng ZhuCollege Pharmaceutical Sciences, Zhejiang Hangzhou 310058, Zhejiang, China*(Email: Zhuhttps://orcid.org/0000-0001-8069-0053Cite this: J. Chem. Inf. Model. 2024, 64, 7, 2125–2128Publication Date (Web):April 8, 2024Publication History Received13 March 2024Published online8 April inissue 8 2024https://pubs.acs.org/doi/10.1021/acs.jcim.4c00444https://doi.org/10.1021/acs.jcim.4c00444editorialACS PublicationsCopyright © 2024 American Chemical Society. This publication is available under these Terms Use. Request reuse permissions free to access through this site. Learn MoreArticle Views1288Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum full text article downloads since November 2008 (both PDF HTML) across all institutions individuals. These metrics regularly updated reflect usage leading up last few days.Citations number other articles citing article, calculated Crossref daily. Find more information about citation counts.The Altmetric Attention Score a quantitative measure attention that research has received online. Clicking on donut icon will load page at altmetric.com with additional details score social media presence for given article. how calculated. Share Add toView InAdd Full Text ReferenceAdd Description ExportRISCitationCitation abstractCitation referencesMore Options onFacebookTwitterWechatLinked InRedditEmail (1 MB) Get e-AlertscloseSUBJECTS:Bioinformatics computational biology,Chemoinformatics,Genetics,Peptides proteins,Protein structure e-Alerts

Язык: Английский

Процитировано

5
Drug–target interaction prediction based on improved heterogeneous graph representation learning and feature projection classification DOI
Donghua Yu, Huawen Liu, Shuang Yao

и другие.

Expert Systems with Applications, Год журнала: 2024, Номер 252, С. 124289 - 124289

Опубликована: Май 27, 2024

Язык: Английский

Процитировано

5
Deep learning for predicting synergistic drug combinations: State‐of‐the‐arts and future directions DOI Creative Commons
Yu Wang, Junjie Wang, Yun Liu

и другие.

Clinical and Translational Discovery, Год журнала: 2024, Номер 4(3)

Опубликована: Июнь 1, 2024

Abstract Combination therapy has emerged as an efficacy strategy for treating complex diseases. Its potential to overcome drug resistance and minimize toxicity makes it highly desirable. However, the vast number of pairs presents a significant challenge, rendering exhaustive clinical testing impractical. In recent years, deep learning‐based methods have promising tools predicting synergistic combinations. This review aims provide comprehensive overview applying diverse deep‐learning architectures combination prediction. commences by elucidating quantitative measures employed assess synergy. Subsequently, we delve into various currently Finally, concludes outlining key challenges facing learning approaches proposes future research.

Язык: Английский

Процитировано

4
Variable multi-scale attention fusion network and adaptive correcting gradient optimization for multi-task learning DOI

Naihua Ji,

Yongqiang Sun,

Fanyun Meng

и другие.

Pattern Recognition, Год журнала: 2025, Номер unknown, С. 111423 - 111423

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0
GS-DTA: integrating graph and sequence models for predicting drug-target binding affinity DOI Creative Commons
Junwei Luo, Zheng Zhu, Zhiyu Xu

и другие.

BMC Genomics, Год журнала: 2025, Номер 26(1)

Опубликована: Фев. 4, 2025

Drug-target binding affinity (DTA) prediction is vital in drug discovery and repositioning, more researchers are beginning to focus on this. Many effective methods have been proposed. However, some current certain shortcomings focusing important nodes molecular graphs dealing with complex structural molecules. In particular, when considering substructures molecules, they may not be able fully explore the potential relationships between different parts. addition, protein structures, ignore connections amino acid fragments that far apart sequence but work synergistically function. this paper, we propose a new method, called GS-DTA, for predicting DTA based graph models. GS-DTA takes simplified input line system (SMILES) of as input. First, each modeled graph, which vertex an atom edge represents interaction atoms. Then GATv2-GCN three-layer GCN networks used extract features drug. enhances model's ability by assigning dynamic attention scores, improves learning structure's intricate patterns. Besides, The can captures hierarchical through deeper propagation feature transformation. Meanwhile, protein, framework combining CNN, Bi-LSTM, Transformer contextual information sequences, combination help understand comprehensive detailed protein. Finally, obtained vectors combined predict connected layer. source code downloaded from https://github.com/zhuziguang/GS-DTA . results show achieves good performance terms MSE, CI, r2m Davis KIBA datasets, improving accuracy prediction.

Язык: Английский

Процитировано

0
MGTNSyn: Molecular structure-aware graph transformer network with relational attention for drug synergy prediction DOI
Yunjiong Liu, Peiliang Zhang, Dongyang Li

и другие.

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 127699 - 127699

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
Characterizing Clinical Toxicity in Cancer Combination Therapies DOI Creative Commons
Alexandra M. Wong, Lorin Crawford

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Апрель 19, 2025

A bstract Predicting synergistic cancer drug combinations through computational methods offers a scalable approach to creating therapies that are more effective and less toxic. However, most algorithms focus solely on synergy without considering toxicity when selecting optimal combinations. In the absence of combinatorial assays, few models use penalties balance high with lower toxicity. these have not been explicitly validated against known drug-drug interactions. this study, we examine whether scores metrics correlate adverse While some show trends levels, our results reveal significant limitations in using them as penalties. These findings highlight challenges incorporating into prediction frameworks suggest advancing field requires comprehensive combination data.

Язык: Английский

Процитировано

0
Image-based molecular representation learning for drug development: a survey DOI Creative Commons
Yue Li, Bingyan Liu,

Jinyan Deng

и другие.

Briefings in Bioinformatics, Год журнала: 2024, Номер 25(4)

Опубликована: Май 23, 2024

Artificial intelligence (AI) powered drug development has received remarkable attention in recent years. It addresses the limitations of traditional experimental methods that are costly and time-consuming. While there have been many surveys attempting to summarize related research, they only focus on general AI or specific aspects such as natural language processing graph neural network. Considering rapid advance computer vision, using molecular image enable appears be a more intuitive effective approach since each chemical substance unique visual representation. In this paper, we provide first survey image-based representation for development. The proposes taxonomy based learning paradigms vision reviews large number corresponding papers, highlighting contributions Besides, discuss applications, future directions field. We hope could offer valuable insight into use context

Язык: Английский

Процитировано

2