Briefings in bioinformatics最新文献_第2页

Enhancing RNA-seq analysis by addressing all co-existing biases using a self-benchmarking approach with 2D structural insights. 利用具有二维结构洞察力的自我基准方法解决所有并存的偏差，从而加强 RNA-seq 分析。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-09-23 DOI: 10.1093/bib/bbae532

Qiang Su, Yi Long, Deming Gou, Junmin Quan, Qizhou Lian

{"title":"Enhancing RNA-seq analysis by addressing all co-existing biases using a self-benchmarking approach with 2D structural insights.","authors":"Qiang Su, Yi Long, Deming Gou, Junmin Quan, Qizhou Lian","doi":"10.1093/bib/bbae532","DOIUrl":"10.1093/bib/bbae532","url":null,"abstract":"We introduce a groundbreaking approach: the minimum free energy-based Gaussian Self-Benchmarking (MFE-GSB) framework, designed to combat the myriad of biases inherent in RNA-seq data. Central to our methodology is the MFE concept, facilitating the adoption of a Gaussian distribution model tailored to effectively mitigate all co-existing biases within a k-mer counting scheme. The MFE-GSB framework operates on a sophisticated dual-model system, juxtaposing modeling data of uniform k-mer distribution against the real, observed sequencing data characterized by nonuniform k-mer distributions. The framework applies a Gaussian function, guided by the predetermined parameters-mean and SD-derived from modeling data, to fit unknown sequencing data. This dual comparison allows for the accurate prediction of k-mer abundances across MFE categories, enabling simultaneous correction of biases at the single k-mer level. Through validation with both engineered RNA constructs and human tissue RNA samples, its wide-ranging efficacy and applicability are demonstrated.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"25 6","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11491153/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142458372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SpaDiT: diffusion transformer for spatial gene expression prediction using scRNA-seq. SpaDiT：利用 scRNA-seq 进行空间基因表达预测的扩散变换器。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-09-23 DOI: 10.1093/bib/bbae571

Xiaoyu Li, Fangfang Zhu, Wenwen Min

{"title":"SpaDiT: diffusion transformer for spatial gene expression prediction using scRNA-seq.","authors":"Xiaoyu Li, Fangfang Zhu, Wenwen Min","doi":"10.1093/bib/bbae571","DOIUrl":"10.1093/bib/bbae571","url":null,"abstract":"The rapid development of spatially resolved transcriptomics (SRT) technologies has provided unprecedented opportunities for exploring the structure of specific organs or tissues. However, these techniques (such as image-based SRT) can achieve single-cell resolution, but can only capture the expression levels of tens to hundreds of genes. Such spatial transcriptomics (ST) data, carrying a large number of undetected genes, have limited its application value. To address the challenge, we develop SpaDiT, a deep learning framework for spatial reconstruction and gene expression prediction using scRNA-seq data. SpaDiT employs scRNA-seq data as an a priori condition and utilizes shared genes between ST and scRNA-seq data as latent representations to construct inputs, thereby facilitating the accurate prediction of gene expression in ST data. SpaDiT enhances the accuracy of spatial gene expression predictions over a variety of spatial transcriptomics datasets. We have demonstrated the effectiveness of SpaDiT by conducting extensive experiments on both seq-based and image-based ST data. We compared SpaDiT with eight highly effective baseline methods and found that our proposed method achieved an 8%-12% improvement in performance across multiple metrics. Source code and all datasets used in this paper are available at https://github.com/wenwenmin/SpaDiT and https://zenodo.org/records/12792074.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"25 6","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11541600/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142603263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MPA-MutPred: a novel strategy for accurately predicting the binding affinity change upon mutation in membrane protein complexes. MPA-MutPred：准确预测膜蛋白复合物突变时结合亲和力变化的新策略。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-09-23 DOI: 10.1093/bib/bbae598

Fathima Ridha, M Michael Gromiha

{"title":"MPA-MutPred: a novel strategy for accurately predicting the binding affinity change upon mutation in membrane protein complexes.","authors":"Fathima Ridha, M Michael Gromiha","doi":"10.1093/bib/bbae598","DOIUrl":"10.1093/bib/bbae598","url":null,"abstract":"Mutations in the interface of membrane protein (MP) complexes are key contributors to a broad spectrum of human diseases, primarily due to changes in their binding affinities. While various methods exist for predicting the mutation-induced changes in binding affinity (ΔΔG) in protein-protein complexes, none are specific to MP complexes. This study proposes a novel strategy for ΔΔG prediction in MP complexes, which combines linear and nonlinear models, to obtain a more robust model with improved prediction accuracy. We used multiple linear regression to extract informative features that influence the binding affinity in MP complexes, which included changes in the stability of the complex, conservation score, electrostatic interaction, relatively accessible surface area, and interface contacts. Further, using gradient boosting regressor on the selected features, we developed MPA-MutPred, a novel method specific for predicting the ΔΔG of membrane protein-protein complexes, and it is freely accessible at https://web.iitm.ac.in/bioinfo2/MPA-MutPred/. Our method achieved a correlation of 0.75 and a mean absolute error (MAE) of 0.73 kcal/mol in the jack-knife test conducted on a dataset of 770 mutants. We further validated the method using a blind test set of 86 mutations, obtaining a correlation of 0.85 and an MAE of 0.77 kcal/mol. We anticipate that this method can be used for large-scale studies to understand the influence of binding affinity change on disease-causing mutations in MP complexes, thereby aiding in the understanding of disease mechanisms and the identification of potential therapeutic targets.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"25 6","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11568875/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142643857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep learning in template-free de novo biosynthetic pathway design of natural products. 天然产品无模板从头生物合成途径设计中的深度学习。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-09-23 DOI: 10.1093/bib/bbae495

Xueying Xie, Lin Gui, Baixue Qiao, Guohua Wang, Shan Huang, Yuming Zhao, Shanwen Sun

{"title":"Deep learning in template-free de novo biosynthetic pathway design of natural products.","authors":"Xueying Xie, Lin Gui, Baixue Qiao, Guohua Wang, Shan Huang, Yuming Zhao, Shanwen Sun","doi":"10.1093/bib/bbae495","DOIUrl":"10.1093/bib/bbae495","url":null,"abstract":"Natural products (NPs) are indispensable in drug development, particularly in combating infections, cancer, and neurodegenerative diseases. However, their limited availability poses significant challenges. Template-free de novo biosynthetic pathway design provides a strategic solution for NP production, with deep learning standing out as a powerful tool in this domain. This review delves into state-of-the-art deep learning algorithms in NP biosynthesis pathway design. It provides an in-depth discussion of databases like Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, and UniProt, which are essential for model training, along with chemical databases such as Reaxys, SciFinder, and PubChem for transfer learning to expand models' understanding of the broader chemical space. It evaluates the potential and challenges of sequence-to-sequence and graph-to-graph translation models for accurate single-step prediction. Additionally, it discusses search algorithms for multistep prediction and deep learning algorithms for predicting enzyme function. The review also highlights the pivotal role of deep learning in improving catalytic efficiency through enzyme engineering, which is essential for enhancing NP production. Moreover, it examines the application of large language models in pathway design, enzyme discovery, and enzyme engineering. Finally, it addresses the challenges and prospects associated with template-free approaches, offering insights into potential advancements in NP biosynthesis pathway design.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"25 6","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11456888/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142380028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A robust statistical approach for finding informative spatially associated pathways. 寻找信息丰富的空间关联路径的稳健统计方法。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-09-23 DOI: 10.1093/bib/bbae543

Leqi Tian, Jiashun Xiao, Tianwei Yu

{"title":"A robust statistical approach for finding informative spatially associated pathways.","authors":"Leqi Tian, Jiashun Xiao, Tianwei Yu","doi":"10.1093/bib/bbae543","DOIUrl":"https://doi.org/10.1093/bib/bbae543","url":null,"abstract":"Spatial transcriptomics offers deep insights into cellular functional localization and communication by mapping gene expression to spatial locations. Traditional approaches that focus on selecting spatially variable genes often overlook the complexity of biological pathways and the interactions among genes. Here, we introduce a novel framework that shifts the focus towards directly identifying functional pathways associated with spatial variability by adapting the Brownian distance covariance test in an innovative manner to explore the heterogeneity of biological functions over space. Unlike most other methods, this statistical testing approach is free of gene selection and parameter selection and allows nonlinear and complex dependencies. It allows for a deeper understanding of how cells coordinate their activities across different spatial domains through biological pathways. By analyzing real human and mouse datasets, the method found significant pathways that were associated with spatial variation, as well as different pathway patterns among inner- and edge-cancer regions. This innovative framework offers a new perspective on analyzing spatial transcriptomic data, contributing to our understanding of tissue architecture and disease pathology. The implementation is publicly available at https://github.com/tianlq-prog/STpathway.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"25 6","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11503753/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142495333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prototype-based contrastive substructure identification for molecular property prediction. 用于分子特性预测的基于原型的对比子结构识别。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-09-23 DOI: 10.1093/bib/bbae565

Gaoqi He, Shun Liu, Zhuoran Liu, Changbo Wang, Kai Zhang, Honglin Li

{"title":"Prototype-based contrastive substructure identification for molecular property prediction.","authors":"Gaoqi He, Shun Liu, Zhuoran Liu, Changbo Wang, Kai Zhang, Honglin Li","doi":"10.1093/bib/bbae565","DOIUrl":"10.1093/bib/bbae565","url":null,"abstract":"Substructure-based representation learning has emerged as a powerful approach to featurize complex attributed graphs, with promising results in molecular property prediction (MPP). However, existing MPP methods mainly rely on manually defined rules to extract substructures. It remains an open challenge to adaptively identify meaningful substructures from numerous molecular graphs to accommodate MPP tasks. To this end, this paper proposes Prototype-based cOntrastive Substructure IdentificaTion (POSIT), a self-supervised framework to autonomously discover substructural prototypes across graphs so as to guide end-to-end molecular fragmentation. During pre-training, POSIT emphasizes two key aspects of substructure identification: firstly, it imposes a soft connectivity constraint to encourage the generation of topologically meaningful substructures; secondly, it aligns resultant substructures with derived prototypes through a prototype-substructure contrastive clustering objective, ensuring attribute-based similarity within clusters. In the fine-tuning stage, a cross-scale attention mechanism is designed to integrate substructure-level information to enhance molecular representations. The effectiveness of the POSIT framework is demonstrated by experimental results from diverse real-world datasets, covering both classification and regression tasks. Moreover, visualization analysis validates the consistency of chemical priors with identified substructures. The source code is publicly available at https://github.com/VRPharmer/POSIT.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"25 6","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11533112/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142567282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

COFFEE: consensus single cell-type specific inference for gene regulatory networks. COFFEE：基因调控网络的共识性单细胞类型特异性推断。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-09-23 DOI: 10.1093/bib/bbae457

Musaddiq K Lodi, Anna Chernikov, Preetam Ghosh

{"title":"COFFEE: consensus single cell-type specific inference for gene regulatory networks.","authors":"Musaddiq K Lodi, Anna Chernikov, Preetam Ghosh","doi":"10.1093/bib/bbae457","DOIUrl":"10.1093/bib/bbae457","url":null,"abstract":"The inference of gene regulatory networks (GRNs) is crucial to understanding the regulatory mechanisms that govern biological processes. GRNs may be represented as edges in a graph, and hence, it have been inferred computationally for scRNA-seq data. A wisdom of crowds approach to integrate edges from several GRNs to create one composite GRN has demonstrated improved performance when compared with individual algorithm implementations on bulk RNA-seq and microarray data. In an effort to extend this approach to scRNA-seq data, we present COFFEE (COnsensus single cell-type speciFic inFerence for gEnE regulatory networks), a Borda voting-based consensus algorithm that integrates information from 10 established GRN inference methods. We conclude that COFFEE has improved performance across synthetic, curated, and experimental datasets when compared with baseline methods. Additionally, we show that a modified version of COFFEE can be leveraged to improve performance on newer cell-type specific GRN inference methods. Overall, our results demonstrate that consensus-based methods with pertinent modifications continue to be valuable for GRN inference at the single cell level. While COFFEE is benchmarked on 10 algorithms, it is a flexible strategy that can incorporate any set of GRN inference algorithms according to user preference. A Python implementation of COFFEE may be found on GitHub: https://github.com/lodimk2/coffee.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"25 6","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418232/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142280435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Diagnostics of viral infections using high-throughput genome sequencing data. 利用高通量基因组测序数据诊断病毒感染。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-09-23 DOI: 10.1093/bib/bbae501

Haochen Ning, Ian Boyes, Ibrahim Numanagić, Michael Rott, Li Xing, Xuekui Zhang

{"title":"Diagnostics of viral infections using high-throughput genome sequencing data.","authors":"Haochen Ning, Ian Boyes, Ibrahim Numanagić, Michael Rott, Li Xing, Xuekui Zhang","doi":"10.1093/bib/bbae501","DOIUrl":"https://doi.org/10.1093/bib/bbae501","url":null,"abstract":"Plant viral infections cause significant economic losses, totalling $350 billion USD in 2021. With no treatment for virus-infected plants, accurate and efficient diagnosis is crucial to preventing and controlling these diseases. High-throughput sequencing (HTS) enables cost-efficient identification of known and unknown viruses. However, existing diagnostic pipelines face challenges. First, many methods depend on subjectively chosen parameter values, undermining their robustness across various data sources. Second, artifacts (e.g. false peaks) in the mapped sequence data can lead to incorrect diagnostic results. While some methods require manual or subjective verification to address these artifacts, others overlook them entirely, affecting the overall method performance and leading to imprecise or labour-intensive outcomes. To address these challenges, we introduce IIMI, a new automated analysis pipeline using machine learning to diagnose infections from 1583 plant viruses with HTS data. It adopts a data-driven approach for parameter selection, reducing subjectivity, and automatically filters out regions affected by artifacts, thus improving accuracy. Testing with in-house and published data shows IIMI's superiority over existing methods. Besides a prediction model, IIMI also provides resources on plant virus genomes, including annotations of regions prone to artifacts. The method is available as an R package (iimi) on CRAN and will integrate with the web application www.virtool.ca, enhancing accessibility and user convenience.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"25 6","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11483527/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142486027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep learning model for protein multi-label subcellular localization and function prediction based on multi-task collaborative training. 基于多任务协作训练的蛋白质多标签亚细胞定位和功能预测深度学习模型。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-09-23 DOI: 10.1093/bib/bbae568

Peihao Bai, Guanghui Li, Jiawei Luo, Cheng Liang

{"title":"Deep learning model for protein multi-label subcellular localization and function prediction based on multi-task collaborative training.","authors":"Peihao Bai, Guanghui Li, Jiawei Luo, Cheng Liang","doi":"10.1093/bib/bbae568","DOIUrl":"10.1093/bib/bbae568","url":null,"abstract":"The functional study of proteins is a critical task in modern biology, playing a pivotal role in understanding the mechanisms of pathogenesis, developing new drugs, and discovering novel drug targets. However, existing computational models for subcellular localization face significant challenges, such as reliance on known Gene Ontology (GO) annotation databases or overlooking the relationship between GO annotations and subcellular localization. To address these issues, we propose DeepMTC, an end-to-end deep learning-based multi-task collaborative training model. DeepMTC integrates the interrelationship between subcellular localization and the functional annotation of proteins, leveraging multi-task collaborative training to eliminate dependence on known GO databases. This strategy gives DeepMTC a distinct advantage in predicting newly discovered proteins without prior functional annotations. First, DeepMTC leverages pre-trained language model with high accuracy to obtain the 3D structure and sequence features of proteins. Additionally, it employs a graph transformer module to encode protein sequence features, addressing the problem of long-range dependencies in graph neural networks. Finally, DeepMTC uses a functional cross-attention mechanism to efficiently combine upstream learned functional features to perform the subcellular localization task. The experimental results demonstrate that DeepMTC outperforms state-of-the-art models in both protein function prediction and subcellular localization. Moreover, interpretability experiments revealed that DeepMTC can accurately identify the key residues and functional domains of proteins, confirming its superior performance. The code and dataset of DeepMTC are freely available at https://github.com/ghli16/DeepMTC.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"25 6","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11531862/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142567262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Model ensembling as a tool to form interpretable multi-omic predictors of cancer pharmacosensitivity. 以模型组合为工具，形成可解释的癌症药敏性多组学预测指标。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-09-23 DOI: 10.1093/bib/bbae567

Sébastien De Landtsheer, Apurva Badkas, Dagmar Kulms, Thomas Sauter

{"title":"Model ensembling as a tool to form interpretable multi-omic predictors of cancer pharmacosensitivity.","authors":"Sébastien De Landtsheer, Apurva Badkas, Dagmar Kulms, Thomas Sauter","doi":"10.1093/bib/bbae567","DOIUrl":"10.1093/bib/bbae567","url":null,"abstract":"Stratification of patients diagnosed with cancer has become a major goal in personalized oncology. One important aspect is the accurate prediction of the response to various drugs. It is expected that the molecular characteristics of the cancer cells contain enough information to retrieve specific signatures, allowing for accurate predictions based solely on these multi-omic data. Ideally, these predictions should be explainable to clinicians, in order to be integrated in the patients care. We propose a machine-learning framework based on ensemble learning to integrate multi-omic data and predict sensitivity to an array of commonly used and experimental compounds, including chemotoxic compounds and targeted kinase inhibitors. We trained a set of classifiers on the different parts of our dataset to produce omic-specific signatures, then trained a random forest classifier on these signatures to predict drug responsiveness. We used the Cancer Cell Line Encyclopedia dataset, comprising multi-omic and drug sensitivity measurements for hundreds of cell lines, to build the predictive models, and validated the results using nested cross-validation. Our results show good performance for several compounds (Area under the Receiver-Operating Curve >79%) across the most frequent cancer types. Furthermore, the simplicity of our approach allows to examine which omic layers have a greater importance in the models and identify new putative markers of drug responsiveness. We propose several models based on small subsets of transcriptional markers with the potential to become useful tools in personalized oncology, paving the way for clinicians to use the molecular characteristics of the tumors to predict sensitivity to therapeutic compounds.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"25 6","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11532660/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142567268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0