Correction to: Inflated false discovery rate due to volcano plots: problem and solutions
Brief Bioinform. 2024 Mar 27;25(3):bbae149. doi: 10.1093/bib/bbae149.NO ABSTRACTPMID:38619418 | DOI:10.1093/bib/bbae149 (Source: Briefings in Bioinformatics)
Source: Briefings in Bioinformatics - April 15, 2024 Category: Bioinformatics Source Type: research
Correction to: DeepFormer: a hybrid network based on convolutional neural network and flow-attention mechanism for identifying the function of DNA sequences
Brief Bioinform. 2024 Mar 27;25(3):bbae181. doi: 10.1093/bib/bbae181.NO ABSTRACTPMID:38619419 | DOI:10.1093/bib/bbae181 (Source: Briefings in Bioinformatics)
Source: Briefings in Bioinformatics - April 15, 2024 Category: Bioinformatics Source Type: research
MGCNSS: miRNA-disease association prediction with multi-layer graph convolution and distance-based negative sample selection strategy
In this study, we propose a novel approach named MGCNSS with the multi-layer graph convolution and high-quality negative sample selection strategy. Specifically, MGCNSS first constructs a comprehensive heterogeneous network by integrating miRNA and disease similarity networks coupled with their known association relationships. Then, we employ the multi-layer graph convolution to automatically capture the meta-path relations with different lengths in the heterogeneous network and learn the discriminative representations of miRNAs and diseases. After that, MGCNSS establishes a highly reliable negative sample set from the unl...
Source: Briefings in Bioinformatics - April 15, 2024 Category: Bioinformatics Authors: Zhen Tian Chenguang Han Lewen Xu Zhixia Teng Wei Song Source Type: research
Fuzzy kernel evidence Random Forest for identifying pseudouridine sites
In this study, we propose fuzzy kernel evidence Random Forest (FKeERF) to identify pseudouridine sites. This method is called PseU-FKeERF, which demonstrates high accuracy in identifying pseudouridine sites from RNA sequencing data. The PseU-FKeERF model selected four RNA feature coding schemes with relatively good performance for feature combination, and then input them into the newly proposed FKeERF method for category prediction. FKeERF not only uses fuzzy logic to expand the original feature space, but also combines kernel methods that are easy to interpret in general for category prediction. Both cross-validation test...
Source: Briefings in Bioinformatics - April 15, 2024 Category: Bioinformatics Authors: Mingshuai Chen Mingai Sun Xi Su Prayag Tiwari Yijie Ding Source Type: research
Understanding YTHDF2-mediated mRNA degradation by m6A-BERT-Deg
Brief Bioinform. 2024 Mar 27;25(3):bbae170. doi: 10.1093/bib/bbae170.ABSTRACTN6-methyladenosine (m6A) is the most abundant mRNA modification within mammalian cells, holding pivotal significance in the regulation of mRNA stability, translation and splicing. Furthermore, it plays a critical role in the regulation of RNA degradation by primarily recruiting the YTHDF2 reader protein. However, the selective regulation of mRNA decay of the m6A-methylated mRNA through YTHDF2 binding is poorly understood. To improve our understanding, we developed m6A-BERT-Deg, a BERT model adapted for predicting YTHDF2-mediated degradation of m6A...
Source: Briefings in Bioinformatics - April 15, 2024 Category: Bioinformatics Authors: Ting-He Zhang Sumin Jo Michelle Zhang Kai Wang Shou-Jiang Gao Yufei Huang Source Type: research
Community cohesion looseness in gene networks reveals individualized drug targets and resistance
Brief Bioinform. 2024 Mar 27;25(3):bbae175. doi: 10.1093/bib/bbae175.ABSTRACTCommunity cohesion plays a critical role in the determination of an individual's health in social science. Intriguingly, a community structure of gene networks indicates that the concept of community cohesion could be applied between the genes as well to overcome the limitations of single gene-based biomarkers for precision oncology. Here, we develop community cohesion scores which precisely quantify the community ability to retain the interactions between the genes and their cellular functions in each individualized gene network. Using breast can...
Source: Briefings in Bioinformatics - April 15, 2024 Category: Bioinformatics Authors: Seunghyun Wang Doheon Lee Source Type: research
SEGCECO: Subgraph Embedding of Gene expression matrix for prediction of CEll-cell COmmunication
Brief Bioinform. 2024 Mar 27;25(3):bbae160. doi: 10.1093/bib/bbae160.ABSTRACTRecent advances in single-cell RNA sequencing technology have eased analyses of signaling networks of cells. Recently, cell-cell interaction has been studied based on various link prediction approaches on graph-structured data. These approaches have assumptions about the likelihood of node interaction, thus showing high performance for only some specific networks. Subgraph-based methods have solved this problem and outperformed other approaches by extracting local subgraphs from a given network. In this work, we present a novel method, called Subg...
Source: Briefings in Bioinformatics - April 12, 2024 Category: Bioinformatics Authors: Akram Vasighizaker Sheena Hora Raymond Zeng Luis Rueda Source Type: research
KDGene: knowledge graph completion for disease gene prediction using interactional tensor decomposition
Brief Bioinform. 2024 Mar 27;25(3):bbae161. doi: 10.1093/bib/bbae161.ABSTRACTThe accurate identification of disease-associated genes is crucial for understanding the molecular mechanisms underlying various diseases. Most current methods focus on constructing biological networks and utilizing machine learning, particularly deep learning, to identify disease genes. However, these methods overlook complex relations among entities in biological knowledge graphs. Such information has been successfully applied in other areas of life science research, demonstrating their effectiveness. Knowledge graph embedding methods can learn ...
Source: Briefings in Bioinformatics - April 12, 2024 Category: Bioinformatics Authors: Xinyan Wang Kuo Yang Ting Jia Fanghui Gu Chongyu Wang Kuan Xu Zixin Shu Jianan Xia Qiang Zhu Xuezhong Zhou Source Type: research
Self-supervised learning on millions of primary RNA sequences from 72 vertebrates improves sequence-based RNA splicing prediction
In this study, we have developed SpliceBERT, a language model pretrained on primary ribonucleic acids (RNA) sequences from 72 vertebrates by masked language modeling, and applied it to sequence-based modeling of RNA splicing. Pretraining SpliceBERT on diverse species enables effective identification of evolutionarily conserved elements. Meanwhile, the learned hidden states and attention weights can characterize the biological properties of splice sites. As a result, SpliceBERT was shown effective on several downstream tasks: zero-shot prediction of variant effects on splicing, prediction of branchpoints in humans, and cros...
Source: Briefings in Bioinformatics - April 12, 2024 Category: Bioinformatics Authors: Ken Chen Yue Zhou Maolin Ding Yu Wang Zhixiang Ren Yuedong Yang Source Type: research
BEERS2: RNA-Seq simulation through high fidelity in silico modeling
Brief Bioinform. 2024 Mar 27;25(3):bbae164. doi: 10.1093/bib/bbae164.ABSTRACTSimulation of RNA-seq reads is critical in the assessment, comparison, benchmarking and development of bioinformatics tools. Yet the field of RNA-seq simulators has progressed little in the last decade. To address this need we have developed BEERS2, which combines a flexible and highly configurable design with detailed simulation of the entire library preparation and sequencing pipeline. BEERS2 takes input transcripts (typically fully length messenger RNA transcripts with polyA tails) from either customizable input or from CAMPAREE simulated RNA s...
Source: Briefings in Bioinformatics - April 12, 2024 Category: Bioinformatics Authors: Thomas G Brooks Nicholas F Lahens Antonijo Mr čela Dimitra Sarantopoulou Soumyashant Nayak Amruta Naik Shaon Sengupta Peter S Choi Gregory R Grant Source Type: research
MUSCLE: multi-view and multi-scale attentional feature fusion for microRNA-disease associations prediction
Brief Bioinform. 2024 Mar 27;25(3):bbae167. doi: 10.1093/bib/bbae167.ABSTRACTMicroRNAs (miRNAs) synergize with various biomolecules in human cells resulting in diverse functions in regulating a wide range of biological processes. Predicting potential disease-associated miRNAs as valuable biomarkers contributes to the treatment of human diseases. However, few previous methods take a holistic perspective and only concentrate on isolated miRNA and disease objects, thereby ignoring that human cells are responsible for multiple relationships. In this work, we first constructed a multi-view graph based on the relationships betwe...
Source: Briefings in Bioinformatics - April 12, 2024 Category: Bioinformatics Authors: Boya Ji Haitao Zou Liwen Xu Xiaolan Xie Shaoliang Peng Source Type: research
Enhancing cryo-EM structure prediction with DeepTracer and AlphaFold2 integration
In this study, we present DeepTracer-Refine, an automated method that refines AlphaFold predicted structures by aligning them to DeepTracers modeled structure. Our method was evaluated on 39 multi-domain proteins and we improved the average residue coverage from 78.2 to 90.0% and average local Distance Difference Test score from 0.67 to 0.71. We also compared DeepTracer-Refine with Phenixs AlphaFold refinement and demonstrated that our method not only performs better when the initial AlphaFold model is less precise but also surpasses Phenix in run-time performance.PMID:38609330 | PMC:PMC11014792 | DOI:10.1093/bib/bbae118 (...
Source: Briefings in Bioinformatics - April 12, 2024 Category: Bioinformatics Authors: Jason Chen Ayisha Zia Albert Luo Hanze Meng Fengbin Wang Jie Hou Renzhi Cao Dong Si Source Type: research
Surveying biomedical relation extraction: a critical examination of current datasets and the proposal of a new resource
Brief Bioinform. 2024 Mar 27;25(3):bbae132. doi: 10.1093/bib/bbae132.ABSTRACTNatural language processing (NLP) has become an essential technique in various fields, offering a wide range of possibilities for analyzing data and developing diverse NLP tasks. In the biomedical domain, understanding the complex relationships between compounds and proteins is critical, especially in the context of signal transduction and biochemical pathways. Among these relationships, protein-protein interactions (PPIs) are of particular interest, given their potential to trigger a variety of biological reactions. To improve the ability to pred...
Source: Briefings in Bioinformatics - April 12, 2024 Category: Bioinformatics Authors: Ming-Siang Huang Jen-Chieh Han Pei-Yen Lin Yu-Ting You Richard Tzong-Han Tsai Wen-Lian Hsu Source Type: research
SEGCECO: Subgraph Embedding of Gene expression matrix for prediction of CEll-cell COmmunication
Brief Bioinform. 2024 Mar 27;25(3):bbae160. doi: 10.1093/bib/bbae160.ABSTRACTRecent advances in single-cell RNA sequencing technology have eased analyses of signaling networks of cells. Recently, cell-cell interaction has been studied based on various link prediction approaches on graph-structured data. These approaches have assumptions about the likelihood of node interaction, thus showing high performance for only some specific networks. Subgraph-based methods have solved this problem and outperformed other approaches by extracting local subgraphs from a given network. In this work, we present a novel method, called Subg...
Source: Briefings in Bioinformatics - April 12, 2024 Category: Bioinformatics Authors: Akram Vasighizaker Sheena Hora Raymond Zeng Luis Rueda Source Type: research
KDGene: knowledge graph completion for disease gene prediction using interactional tensor decomposition
Brief Bioinform. 2024 Mar 27;25(3):bbae161. doi: 10.1093/bib/bbae161.ABSTRACTThe accurate identification of disease-associated genes is crucial for understanding the molecular mechanisms underlying various diseases. Most current methods focus on constructing biological networks and utilizing machine learning, particularly deep learning, to identify disease genes. However, these methods overlook complex relations among entities in biological knowledge graphs. Such information has been successfully applied in other areas of life science research, demonstrating their effectiveness. Knowledge graph embedding methods can learn ...
Source: Briefings in Bioinformatics - April 12, 2024 Category: Bioinformatics Authors: Xinyan Wang Kuo Yang Ting Jia Fanghui Gu Chongyu Wang Kuan Xu Zixin Shu Jianan Xia Qiang Zhu Xuezhong Zhou Source Type: research
