Bioinformatics最新文献_第9页

IsoFrog: a reversible jump Markov Chain Monte Carlo feature selection-based method for predicting isoform functions. IsoFrog:一个可逆跳跃马尔可夫链蒙特卡罗特征选择为基础的方法预测异构体函数。

IF 5.8 3区生物学

Bioinformatics Pub Date : 2023-09-02 DOI: 10.1093/bioinformatics/btad530

Yiwei Liu, Changhuo Yang, Hong-Dong Li, Jianxin Wang

{"title":"IsoFrog: a reversible jump Markov Chain Monte Carlo feature selection-based method for predicting isoform functions.","authors":"Yiwei Liu, Changhuo Yang, Hong-Dong Li, Jianxin Wang","doi":"10.1093/bioinformatics/btad530","DOIUrl":"https://doi.org/10.1093/bioinformatics/btad530","url":null,"abstract":"Motivation: A single gene may yield several isoforms with different functions through alternative splicing. Continuous efforts are devoted to developing machine-learning methods to predict isoform functions. However, existing methods do not consider the relevance of each feature to specific functions and ignore the noise caused by the irrelevant features. In this case, we hypothesize that constructing a feature selection framework to extract the function-relevant features might help improve the model accuracy in isoform function prediction.Results: In this article, we present a feature selection-based approach named IsoFrog to predict isoform functions. First, IsoFrog adopts a reversible jump Markov Chain Monte Carlo (RJMCMC)-based feature selection framework to assess the feature importance to gene functions. Second, a sequential feature selection procedure is applied to select a subset of function-relevant features. This strategy screens the relevant features for the specific function while eliminating irrelevant ones, improving the effectiveness of the input features. Then, the selected features are input into our proposed method modified domain-invariant partial least squares, which prioritizes the most likely positive isoform for each positive MIG and utilizes diPLS for isoform function prediction. Tested on three datasets, our method achieves superior performance over six state-of-the-art methods, and the RJMCMC-based feature selection framework outperforms three classic feature selection methods. We expect this proposed methodology will promote the identification of isoform functions and further inspire the development of new methods.Availability and implementation: IsoFrog is freely available at https://github.com/genemine/IsoFrog.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10491952/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10335853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

DataDTA: a multi-feature and dual-interaction aggregation framework for drug-target binding affinity prediction. DataDTA：一个用于药物靶标结合亲和力预测的多特征和双重相互作用聚集框架。

IF 5.8 3区生物学

Bioinformatics Pub Date : 2023-09-02 DOI: 10.1093/bioinformatics/btad560

Yan Zhu, Lingling Zhao, Naifeng Wen, Junjie Wang, Chunyu Wang

{"title":"DataDTA: a multi-feature and dual-interaction aggregation framework for drug-target binding affinity prediction.","authors":"Yan Zhu, Lingling Zhao, Naifeng Wen, Junjie Wang, Chunyu Wang","doi":"10.1093/bioinformatics/btad560","DOIUrl":"10.1093/bioinformatics/btad560","url":null,"abstract":"Motivation: Accurate prediction of drug-target binding affinity (DTA) is crucial for drug discovery. The increase in the publication of large-scale DTA datasets enables the development of various computational methods for DTA prediction. Numerous deep learning-based methods have been proposed to predict affinities, some of which only utilize original sequence information or complex structures, but the effective combination of various information and protein-binding pockets have not been fully mined. Therefore, a new method that integrates available key information is urgently needed to predict DTA and accelerate the drug discovery process.Results: In this study, we propose a novel deep learning-based predictor termed DataDTA to estimate the affinities of drug-target pairs. DataDTA utilizes descriptors of predicted pockets and sequences of proteins, as well as low-dimensional molecular features and SMILES strings of compounds as inputs. Specifically, the pockets were predicted from the three-dimensional structure of proteins and their descriptors were extracted as the partial input features for DTA prediction. The molecular representation of compounds based on algebraic graph features was collected to supplement the input information of targets. Furthermore, to ensure effective learning of multiscale interaction features, a dual-interaction aggregation neural network strategy was developed. DataDTA was compared with state-of-the-art methods on different datasets, and the results showed that DataDTA is a reliable prediction tool for affinities estimation. Specifically, the concordance index (CI) of DataDTA is 0.806 and the Pearson correlation coefficient (R) value is 0.814 on the test dataset, which is higher than other methods.Availability and implementation: The codes and datasets of DataDTA are available at https://github.com/YanZhu06/DataDTA.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10516524/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10181115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 neighborhood-regularization method leveraging multiview data for predicting the frequency of drug-side effects. 利用多视角数据预测药物副作用发生频率的邻域正则化方法。

IF 5.8 3区生物学

Bioinformatics Pub Date : 2023-09-02 DOI: 10.1093/bioinformatics/btad532

Lin Wang, Chenhao Sun, Xianyu Xu, Jia Li, Wenjuan Zhang

{"title":"A neighborhood-regularization method leveraging multiview data for predicting the frequency of drug-side effects.","authors":"Lin Wang, Chenhao Sun, Xianyu Xu, Jia Li, Wenjuan Zhang","doi":"10.1093/bioinformatics/btad532","DOIUrl":"https://doi.org/10.1093/bioinformatics/btad532","url":null,"abstract":"Motivation: A critical issue in drug benefit-risk assessment is to determine the frequency of side effects, which is performed by randomized controlled trails. Computationally predicted frequencies of drug side effects can be used to effectively guide the randomized controlled trails. However, it is more challenging to predict drug side effect frequencies, and thus only a few studies cope with this problem.Results: In this work, we propose a neighborhood-regularization method (NRFSE) that leverages multiview data on drugs and side effects to predict the frequency of side effects. First, we adopt a class-weighted non-negative matrix factorization to decompose the drug-side effect frequency matrix, in which Gaussian likelihood is used to model unknown drug-side effect pairs. Second, we design a multiview neighborhood regularization to integrate three drug attributes and two side effect attributes, respectively, which makes most similar drugs and most similar side effects have similar latent signatures. The regularization can adaptively determine the weights of different attributes. We conduct extensive experiments on one benchmark dataset, and NRFSE improves the prediction performance compared with five state-of-the-art approaches. Independent test set of post-marketing side effects further validate the effectiveness of NRFSE.Availability and implementation: Source code and datasets are available at https://github.com/linwang1982/NRFSE or https://codeocean.com/capsule/4741497/tree/v1.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10491955/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10285623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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: 1

Joint embedding of biological networks for cross-species functional alignment. 用于跨物种功能比对的生物网络的联合嵌入。

IF 5.8 3区生物学

Bioinformatics Pub Date : 2023-09-02 DOI: 10.1093/bioinformatics/btad529

Lechuan Li, Ruth Dannenfelser, Yu Zhu, Nathaniel Hejduk, Santiago Segarra, Vicky Yao

{"title":"Joint embedding of biological networks for cross-species functional alignment.","authors":"Lechuan Li, Ruth Dannenfelser, Yu Zhu, Nathaniel Hejduk, Santiago Segarra, Vicky Yao","doi":"10.1093/bioinformatics/btad529","DOIUrl":"10.1093/bioinformatics/btad529","url":null,"abstract":"Motivation: Model organisms are widely used to better understand the molecular causes of human disease. While sequence similarity greatly aids this cross-species transfer, sequence similarity does not imply functional similarity, and thus, several current approaches incorporate protein-protein interactions to help map findings between species. Existing transfer methods either formulate the alignment problem as a matching problem which pits network features against known orthology, or more recently, as a joint embedding problem.Results: We propose a novel state-of-the-art joint embedding solution: Embeddings to Network Alignment (ETNA). ETNA generates individual network embeddings based on network topological structure and then uses a Natural Language Processing-inspired cross-training approach to align the two embeddings using sequence-based orthologs. The final embedding preserves both within and between species gene functional relationships, and we demonstrate that it captures both pairwise and group functional relevance. In addition, ETNA's embeddings can be used to transfer genetic interactions across species and identify phenotypic alignments, laying the groundwork for potential opportunities for drug repurposing and translational studies.Availability and implementation: https://github.com/ylaboratory/ETNA.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10477935/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10286575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Correction to: Robust joint clustering of multi-omics single-cell data via multi-modal high-order neighborhood Laplacian matrix optimization. 修正:基于多模态高阶邻域拉普拉斯矩阵优化的多组学单细胞数据鲁棒联合聚类。

IF 5.8 3区生物学

Bioinformatics Pub Date : 2023-09-02 DOI: 10.1093/bioinformatics/btad554

引用次数: 0

Minmers are a generalization of minimizers that enable unbiased local Jaccard estimation. 最小化器是实现无偏局部Jaccard估计的最小化器的推广。

IF 4.4 3区生物学

Bioinformatics Pub Date : 2023-09-02 DOI: 10.1093/bioinformatics/btad512

Bryce Kille, Erik Garrison, Todd J Treangen, Adam M Phillippy

{"title":"Minmers are a generalization of minimizers that enable unbiased local Jaccard estimation.","authors":"Bryce Kille, Erik Garrison, Todd J Treangen, Adam M Phillippy","doi":"10.1093/bioinformatics/btad512","DOIUrl":"10.1093/bioinformatics/btad512","url":null,"abstract":"Motivation: The Jaccard similarity on k-mer sets has shown to be a convenient proxy for sequence identity. By avoiding expensive base-level alignments and comparing reduced sequence representations, tools such as MashMap can scale to massive numbers of pairwise comparisons while still providing useful similarity estimates. However, due to their reliance on minimizer winnowing, previous versions of MashMap were shown to be biased and inconsistent estimators of Jaccard similarity. This directly impacts downstream tools that rely on the accuracy of these estimates.Results: To address this, we propose the minmer winnowing scheme, which generalizes the minimizer scheme by use of a rolling minhash with multiple sampled k-mers per window. We show both theoretically and empirically that minmers yield an unbiased estimator of local Jaccard similarity, and we implement this scheme in an updated version of MashMap. The minmer-based implementation is over 10 times faster than the minimizer-based version under the default ANI threshold, making it well-suited for large-scale comparative genomics applications.Availability and implementation: MashMap3 is available at https://github.com/marbl/MashMap.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10505501/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10304418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Correction to: "Retraction of: DeepCRISTL: deep transfer learning to predict CRISPR/Cas9 functional and endogenous on-target editing efficiency". 更正:“撤回:deepcrisstl:深度迁移学习预测CRISPR/Cas9功能和内源性靶向编辑效率”。

IF 5.8 3区生物学

Bioinformatics Pub Date : 2023-09-02 DOI: 10.1093/bioinformatics/btad562

引用次数: 0

Machine learning-based quantification for disease uncertainty increases the statistical power of genetic association studies. 基于机器学习的疾病不确定性量化增加了遗传关联研究的统计能力。

IF 5.8 3区生物学

Bioinformatics Pub Date : 2023-09-02 DOI: 10.1093/bioinformatics/btad534

Jun Young Park, Jang Jae Lee, Younghwa Lee, Dongsoo Lee, Jungsoo Gim, Lindsay Farrer, Kun Ho Lee, Sungho Won

{"title":"Machine learning-based quantification for disease uncertainty increases the statistical power of genetic association studies.","authors":"Jun Young Park, Jang Jae Lee, Younghwa Lee, Dongsoo Lee, Jungsoo Gim, Lindsay Farrer, Kun Ho Lee, Sungho Won","doi":"10.1093/bioinformatics/btad534","DOIUrl":"10.1093/bioinformatics/btad534","url":null,"abstract":"Motivation: Allowance for increasingly large samples is a key to identify the association of genetic variants with Alzheimer's disease (AD) in genome-wide association studies (GWAS). Accordingly, we aimed to develop a method that incorporates patients with mild cognitive impairment and unknown cognitive status in GWAS using a machine learning-based AD prediction model.Results: Simulation analyses showed that weighting imputed phenotypes method increased the statistical power compared to ordinary logistic regression using only AD cases and controls. Applied to real-world data, the penalized logistic method had the highest AUC (0.96) for AD prediction and weighting imputed phenotypes method performed well in terms of power. We identified an association (P<5.0×10-8) of AD with several variants in the APOE region and rs143625563 in LMX1A. Our method, which allows the inclusion of individuals with mild cognitive impairment, improves the statistical power of GWAS for AD. We discovered a novel association with LMX1A.Availability and implementation: Simulation codes can be accessed at https://github.com/Junkkkk/wGEE_GWAS.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10539075/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10151455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

RNA 3D structure modeling by fragment assembly with small-angle X-ray scattering restraints. 基于小角度x射线散射约束的RNA片段组装三维结构建模。

IF 5.8 3区生物学

Bioinformatics Pub Date : 2023-09-02 DOI: 10.1093/bioinformatics/btad527

Grzegorz Chojnowski, Rafał Zaborowski, Marcin Magnus, Sunandan Mukherjee, Janusz M Bujnicki

{"title":"RNA 3D structure modeling by fragment assembly with small-angle X-ray scattering restraints.","authors":"Grzegorz Chojnowski, Rafał Zaborowski, Marcin Magnus, Sunandan Mukherjee, Janusz M Bujnicki","doi":"10.1093/bioinformatics/btad527","DOIUrl":"https://doi.org/10.1093/bioinformatics/btad527","url":null,"abstract":"Summary: Structure determination is a key step in the functional characterization of many non-coding RNA molecules. High-resolution RNA 3D structure determination efforts, however, are not keeping up with the pace of discovery of new non-coding RNA sequences. This increases the importance of computational approaches and low-resolution experimental data, such as from the small-angle X-ray scattering experiments. We present RNA Masonry, a computer program and a web service for a fully automated modeling of RNA 3D structures. It assemblies RNA fragments into geometrically plausible models that meet user-provided secondary structure constraints, restraints on tertiary contacts, and small-angle X-ray scattering data. We illustrate the method description with detailed benchmarks and its application to structural studies of viral RNAs with SAXS restraints.Availability and implementation: The program web server is available at http://iimcb.genesilico.pl/rnamasonry. The source code is available at https://gitlab.com/gchojnowski/rnamasonry.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10474949/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10285624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Genome-wide multimediator analyses using the generalized Berk-Jones statistics with the composite test. 全基因组多介质分析使用广义伯克-琼斯统计与复合检验。

IF 5.8 3区生物学

Bioinformatics Pub Date : 2023-09-02 DOI: 10.1093/bioinformatics/btad544

En-Yu Lai, Yen-Tsung Huang

{"title":"Genome-wide multimediator analyses using the generalized Berk-Jones statistics with the composite test.","authors":"En-Yu Lai, Yen-Tsung Huang","doi":"10.1093/bioinformatics/btad544","DOIUrl":"https://doi.org/10.1093/bioinformatics/btad544","url":null,"abstract":"Motivation: Mediation analysis is performed to evaluate the effects of a hypothetical causal mechanism that marks the progression from an exposure, through mediators, to an outcome. In the age of high-throughput technologies, it has become routine to assess numerous potential mechanisms at the genome or proteome scales. Alongside this, the necessity to address issues related to multiple testing has also arisen. In a sparse scenario where only a few genes or proteins are causally involved, conventional methods for assessing mediation effects lose statistical power because the composite null distribution behind this experiment cannot be attained. The power loss hence decreases the true mechanisms identified after multiple testing corrections. To fairly delineate a uniform distribution under the composite null, Huang (Genome-wide analyses of sparse mediation effects under composite null hypotheses. Ann Appl Stat 2019a;13:60-84; AoAS) proposed the composite test to provide adjusted P-values for single-mediator analyses.Results: Our contribution is to extend the method to multimediator analyses, which are commonly encountered in genomic studies and also flexible to various biological interests. Using the generalized Berk-Jones statistics with the composite test, we proposed a multivariate approach that favors dense and diverse mediation effects, a decorrelation approach that favors sparse and consistent effects, and a hybrid approach that captures the edges of both approaches. Our analysis suite has been implemented as an R package MACtest. The utility is demonstrated by analyzing the lung adenocarcinoma datasets from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium. We further investigate the genes and networks whose expression may be regulated by smoking-induced epigenetic aberrations.Availability and implementation: An R package MACtest is available on https://github.com/roqe/MACtest.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500087/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10286120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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