IEEE/ACM Transactions on Computational Biology and Bioinformatics最新文献_第2页

A comprehensive evaluation framework for benchmarking multi-objective feature selection in omics-based biomarker discovery. 基于 omics 的生物标记发现中多目标特征选择基准的综合评估框架。

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-10-14 DOI: 10.1109/TCBB.2024.3480150

Luca Cattelani, Arindam Ghosh, Teemu Rintala, Vittorio Fortino

{"title":"A comprehensive evaluation framework for benchmarking multi-objective feature selection in omics-based biomarker discovery.","authors":"Luca Cattelani, Arindam Ghosh, Teemu Rintala, Vittorio Fortino","doi":"10.1109/TCBB.2024.3480150","DOIUrl":"https://doi.org/10.1109/TCBB.2024.3480150","url":null,"abstract":"Machine learning algorithms have been extensively used for accurate classification of cancer subtypes driven by gene expression-based biomarkers. However, biomarker models combining multiple gene expression signatures are often not reproducible in external validation datasets and their feature set size is often not optimized, jeopardizing their translatability into cost-effective clinical tools. We investigated how to solve the multi-objective problem of finding the best trade-offs between classification performance and set size applying seven algorithms for machine learning-driven feature subset selection and analyse how they perform in a benchmark with eight large-scale transcriptome datasets of cancer, covering both training and external validation sets. The benchmark includes evaluation metrics assessing the performance of the individual biomarkers and the solution sets, according to their accuracy, diversity, and stability of the composing genes. Moreover, a new evaluation metric for cross-validation studies is proposed that generalizes the hypervolume, which is commonly used to assess the performance of multi-objective optimization algorithms. Biomarkers exhibiting 0.8 of balanced accuracy on the external dataset for breast, kidney and ovarian cancer using respectively 4, 2 and 7 features, were obtained. Genetic algorithms often provided better performance than other considered algorithms, and the recently proposed NSGA2-CH and NSGA2-CHS were the best performing methods in most cases.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generative Biomedical Event Extraction with Constrained Decoding Strategy. 采用约束解码策略的生成式生物医学事件提取。

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-10-14 DOI: 10.1109/TCBB.2024.3480088

Fangfang Su, Chong Teng, Fei Li, Bobo Li, Jun Zhou, Donghong Ji

{"title":"Generative Biomedical Event Extraction with Constrained Decoding Strategy.","authors":"Fangfang Su, Chong Teng, Fei Li, Bobo Li, Jun Zhou, Donghong Ji","doi":"10.1109/TCBB.2024.3480088","DOIUrl":"https://doi.org/10.1109/TCBB.2024.3480088","url":null,"abstract":"Currently, biomedical event extraction has received considerable attention in various fields, including natural language processing, bioinformatics, and computational biomedicine. This has led to the emergence of numerous machine learning and deep learning models that have been proposed and applied to tackle this complex task. While existing models typically adopt an extraction-based approach, which requires breaking down the extraction of biomedical events into multiple subtasks for sequential processing, making it prone to cascading errors. This paper presents a novel approach by constructing a biomedical event generation model based on the framework of the pre-trained language model T5. We employ a sequence-tosequence generation paradigm to obtain events, the model utilizes constrained decoding algorithm to guide sequence generation, and a curriculum learning algorithm for efficient model learning. To demonstrate the effectiveness of our model, we evaluate it on two public benchmark datasets, Genia 2011 and Genia 2013. Our model achieves superior performance, illustrating the effectiveness of generative modeling of biomedical events.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GrapHiC: An integrative graph based approach for imputing missing Hi-C reads. GrapHiC：一种基于图的综合方法，用于估算缺失的 Hi-C 读数。

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-10-11 DOI: 10.1109/TCBB.2024.3477909

Ghulam Murtaza, Justin Wagner, Justin M Zook, Ritambhara Singh

{"title":"GrapHiC: An integrative graph based approach for imputing missing Hi-C reads.","authors":"Ghulam Murtaza, Justin Wagner, Justin M Zook, Ritambhara Singh","doi":"10.1109/TCBB.2024.3477909","DOIUrl":"10.1109/TCBB.2024.3477909","url":null,"abstract":"Hi-C experiments allow researchers to study and understand the 3D genome organization and its regulatory function. Unfortunately, sequencing costs and technical constraints severely restrict access to high-quality Hi-C data for many cell types. Existing frameworks rely on a sparse Hi-C dataset or cheaper-to-acquire ChIP-seq data to predict Hi-C contact maps with high read coverage. However, these methods fail to generalize to sparse or cross-cell-type inputs because they do not account for the contributions of epigenomic features or the impact of the structural neighborhood in predicting Hi-C reads. We propose GrapHiC, which combines Hi-C and ChIP-seq in a graph representation, allowing more accurate embedding of structural and epigenomic features. Each node represents a binned genomic region, and we assign edge weights using the observed Hi-C reads. Additionally, we embed ChIP-seq and relative positional information as node attributes, allowing our representation to capture structural neighborhoods and the contributions of proteins and their modifications for predicting Hi-C reads. We show that GrapHiC generalizes better than the current state-of-the-art on cross-cell-type settings and sparse Hi-C inputs. Moreover, we can utilize our framework to impute Hi-C reads even when no Hi-C contact map is available, thus making high-quality Hi-C data accessible for many cell types. Availability: https://github.com/rsinghlab/GrapHiC.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142406376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Guest Editors' Introduction to the Special Section on Bioinformatics Research and Applications 特邀编辑对生物信息学研究与应用专栏的介绍

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-10-09 DOI: 10.1109/TCBB.2024.3390374

Zhipeng Cai;Alexander Zelikovsky

引用次数: 0

De Novo Drug Design by Multi-Objective Path Consistency Learning with Beam A∗ Search. 利用光束 A∗ 搜索的多目标路径一致性学习进行新药设计。

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-10-09 DOI: 10.1109/TCBB.2024.3477592

Dengwei Zhao, Jingyuan Zhou, Shikui Tu, Lei Xu

{"title":"De Novo Drug Design by Multi-Objective Path Consistency Learning with Beam A∗ Search.","authors":"Dengwei Zhao, Jingyuan Zhou, Shikui Tu, Lei Xu","doi":"10.1109/TCBB.2024.3477592","DOIUrl":"10.1109/TCBB.2024.3477592","url":null,"abstract":"Generating high-quality and drug-like molecules from scratch within the expansive chemical space presents a significant challenge in the field of drug discovery. In prior research, value-based reinforcement learning algorithms have been employed to generate molecules with multiple desired properties iteratively. The immediate reward was defined as the evaluation of intermediate-state molecules at each step, and the learning objective would be maximizing the expected cumulative evaluation scores for all molecules along the generative path. However, this definition of the reward was misleading, as in reality, the optimization target should be the evaluation score of only the final generated molecule. Furthermore, in previous works, randomness was introduced into the decision-making process, enabling the generation of diverse molecules but no longer pursuing the maximum future rewards. In this paper, immediate reward is defined as the improvement achieved through the modification of the molecule to maximize the evaluation score of the final generated molecule exclusively. Originating from the A ∗ search, path consistency (PC), i.e., f values on one optimal path should be identical, is employed as the objective function in the update of the f value estimator to train a multi-objective de novo drug designer. By incorporating the f value into the decision-making process of beam search, the DrugBA∗ algorithm is proposed to enable the large-scale generation of molecules that exhibit both high quality and diversity. Experimental results demonstrate a substantial enhancement over the state-of-theart algorithm QADD in multiple molecular properties of the generated molecules.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142390227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Guest Editorial Selected Papers From BIOKDD 2022 特邀编辑 BIOKDD 2022 论文选

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-10-09 DOI: 10.1109/TCBB.2024.3429784

Da Yan;Catia Pesquita;Carsten Görg;Jake Y. Chen

引用次数: 0

Orientation Determination of Cryo-EM Projection Images Using Reliable Common Lines and Spherical Embeddings. 利用可靠的共线和球形嵌入确定冷冻电镜投影图像的方向

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-10-09 DOI: 10.1109/TCBB.2024.3476619

Xiangwen Wang, Qiaoying Jin, Li Zou, Xianghong Lin, Yonggang Lu

{"title":"Orientation Determination of Cryo-EM Projection Images Using Reliable Common Lines and Spherical Embeddings.","authors":"Xiangwen Wang, Qiaoying Jin, Li Zou, Xianghong Lin, Yonggang Lu","doi":"10.1109/TCBB.2024.3476619","DOIUrl":"10.1109/TCBB.2024.3476619","url":null,"abstract":"Three-dimensional (3D) reconstruction in single-particle cryo-electron microscopy (cryo-EM) is a critical technique for recovering and studying the fine 3D structure of proteins and other biological macromolecules, where the primary issue is to determine the orientations of projection images with high levels of noise. This paper proposes a method to determine the orientations of cryo-EM projection images using reliable common lines and spherical embeddings. First, the reliability of common lines between projection images is evaluated using a weighted voting algorithm based on an iterative improvement technique and binarized weighting. Then, the reliable common lines are used to calculate the normal vectors and local X-axis vectors of projection images after two spherical embeddings. Finally, the orientations of projection images are determined by aligning the results of the two spherical embeddings using an orthogonal constraint. Experimental results on both synthetic and real cryo-EM projection image datasets demonstrate that the proposed method can achieve higher accuracy in estimating the orientations of projection images and higher resolution in reconstructing preliminary 3D structures than some common line-based methods, indicating that the proposed method is effective in single-particle cryo-EM 3D reconstruction.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142390230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A knowledge graph-based method for drug-drug interaction prediction with contrastive learning. 基于知识图谱的药物相互作用预测方法与对比学习。

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-10-09 DOI: 10.1109/TCBB.2024.3477410

Jian Zhong, Haochen Zhao, Qichang Zhao, Jianxin Wang

{"title":"A knowledge graph-based method for drug-drug interaction prediction with contrastive learning.","authors":"Jian Zhong, Haochen Zhao, Qichang Zhao, Jianxin Wang","doi":"10.1109/TCBB.2024.3477410","DOIUrl":"https://doi.org/10.1109/TCBB.2024.3477410","url":null,"abstract":"Precisely predicting Drug-Drug Interactions (DDIs) carries the potential to elevate the quality and safety of drug therapies, protecting the well-being of patients, and providing essential guidance and decision support at every stage of the drug development process. In recent years, leveraging large-scale biomedical knowledge graphs has improved DDI prediction performance. However, the feature extraction procedures in these methods are still rough. More refined features may further improve the quality of predictions. To overcome these limitations, we develop a knowledge graph-based method for multi-typed DDI prediction with contrastive learning (KG-CLDDI). In KG-CLDDI, we combine drug knowledge aggregation features from the knowledge graph with drug topological aggregation features from the DDI graph. Additionally, we build a contrastive learning module that uses horizontal reversal and dropout operations to produce high-quality embeddings for drug-drug pairs. The comparison results indicate that KG-CLDDI is superior to state-of-the-art models in both the transductive and inductive settings. Notably, for the inductive setting, KG-CLDDI outperforms the previous best method by 17.49% and 24.97% in terms of AUC and AUPR, respectively. Furthermore, we conduct the ablation analysis and case study to show the effectiveness of KG-CLDDI. These findings illustrate the potential significance of KG-CLDDI in advancing DDI research and its clinical applications. The codes of KG-CLDDI are available at https://github.com/jianzhong123/KG-CLDDI.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142390226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving Molecule Generation and Drug Discovery with a Knowledge-enhanced Generative Model. 利用知识增强型生成模型改进分子生成和药物发现。

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-10-09 DOI: 10.1109/TCBB.2024.3477313

Aditya Malusare, Vaneet Aggarwal

引用次数: 0

RFLP-inator: interactive web platform for in silico simulation and complementary tools of the PCR-RFLP technique. RFLP-inator：用于 PCR-RFLP 技术硅学模拟和补充工具的交互式网络平台。

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-10-08 DOI: 10.1109/TCBB.2024.3476453

Kiefer Andre Bedoya Benites, Wilser Andres Garcia-Quispes

{"title":"RFLP-inator: interactive web platform for in silico simulation and complementary tools of the PCR-RFLP technique.","authors":"Kiefer Andre Bedoya Benites, Wilser Andres Garcia-Quispes","doi":"10.1109/TCBB.2024.3476453","DOIUrl":"10.1109/TCBB.2024.3476453","url":null,"abstract":"Polymerase chain reaction - Restriction Fragment Length Polymorphism (PCR-RFLP) is an established molecular biology technique leveraging DNA sequence variability for organism identification, genetic disease detection, biodiversity analysis, etc. Traditional PCR-RFLP requires wet-laboratory procedures that can result in technical errors, procedural challenges, and financial costs. With the aim of providing an accessible and efficient PCR-RFLP technique complement, we introduce RFLP-inator. This is a comprehensive web-based platform developed in R using the package Shiny, which simulates the PCR-RFLP technique, integrates analysis capabilities, and offers complementary tools for both pre- and post-evaluation of in vitro results. We developed the RFLP-inator's algorithm independently and our platform offers seven dynamic tools: RFLP simulator, Pattern identifier, Enzyme selector, RFLP analyzer, Multiplex PCR, Restriction map maker, and Gel plotter. Moreover, the software includes a restriction pattern database of more than 250,000 sequences of the bacterial 16S rRNA gene. We successfully validated the core tools against published research findings. This new platform is open access and user-friendly, offering a valuable resource for researchers, educators, and students specializing in molecular genetics. RFLP-inator not only streamlines RFLP technique application but also supports pedagogical efforts in genetics, illustrating its utility and reliability. The software is available for free at https://kodebio.shinyapps.io/RFLP-inator/.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142390231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0