DeepFGRN: inference of gene regulatory network with regulation type based on directed graph embedding
Brief Bioinform. 2024 Mar 27;25(3):bbae143. doi: 10.1093/bib/bbae143.ABSTRACTThe inference of gene regulatory networks (GRNs) from gene expression profiles has been a key issue in systems biology, prompting many researchers to develop diverse computational methods. However, most of these methods do not reconstruct directed GRNs with regulatory types because of the lack of benchmark datasets or defects in the computational methods. Here, we collect benchmark datasets and propose a deep learning-based model, DeepFGRN, for reconstructing fine gene regulatory networks (FGRNs) with both regulation types and directions. In addit...
Source: Briefings in Bioinformatics - April 6, 2024 Category: Bioinformatics Authors: Zhen Gao Yansen Su Junfeng Xia Rui-Fen Cao Yun Ding Chun-Hou Zheng Pi-Jing Wei Source Type: research
Bayesian functional analysis for untargeted metabolomics data with matching uncertainty and small sample sizes
Brief Bioinform. 2024 Mar 27;25(3):bbae141. doi: 10.1093/bib/bbae141.ABSTRACTUntargeted metabolomics based on liquid chromatography-mass spectrometry technology is quickly gaining widespread application, given its ability to depict the global metabolic pattern in biological samples. However, the data are noisy and plagued by the lack of clear identity of data features measured from samples. Multiple potential matchings exist between data features and known metabolites, while the truth can only be one-to-one matches. Some existing methods attempt to reduce the matching uncertainty, but are far from being able to remove the ...
Source: Briefings in Bioinformatics - April 6, 2024 Category: Bioinformatics Authors: Guoxuan Ma Jian Kang Tianwei Yu Source Type: research
From tradition to innovation: conventional and deep learning frameworks in genome annotation
Brief Bioinform. 2024 Mar 27;25(3):bbae138. doi: 10.1093/bib/bbae138.ABSTRACTFollowing the milestone success of the Human Genome Project, the 'Encyclopedia of DNA Elements (ENCODE)' initiative was launched in 2003 to unearth information about the numerous functional elements within the genome. This endeavor coincided with the emergence of numerous novel technologies, accompanied by the provision of vast amounts of whole-genome sequences, high-throughput data such as ChIP-Seq and RNA-Seq. Extracting biologically meaningful information from this massive dataset has become a critical aspect of many recent studies, particularl...
Source: Briefings in Bioinformatics - April 6, 2024 Category: Bioinformatics Authors: Zhaojia Chen Noor Ul Ain Qian Zhao Xingtan Zhang Source Type: research
PUTransGCN: identification of piRNA-disease associations based on attention encoding graph convolutional network and positive unlabelled learning
Brief Bioinform. 2024 Mar 27;25(3):bbae144. doi: 10.1093/bib/bbae144.ABSTRACTPiwi-interacting RNAs (piRNAs) play a crucial role in various biological processes and are implicated in disease. Consequently, there is an escalating demand for computational tools to predict piRNA-disease interactions. Although there have been computational methods proposed for the detection of piRNA-disease associations, the problem of imbalanced and sparse dataset has brought great challenges to capture the complex relationships between piRNAs and diseases. In response to this necessity, we have developed a novel computational architecture, de...
Source: Briefings in Bioinformatics - April 6, 2024 Category: Bioinformatics Authors: Qiuhao Chen Liyuan Zhang Yaojia Liu Zhonghao Qin Tianyi Zhao Source Type: research
A new paradigm for applying deep learning to protein-ligand interaction prediction
This study presents a novel framework for DL-based prediction of protein-ligand interactions, contributing to the advancement of this field. The IGModel is available at GitHub repository https://github.com/zchwang/IGModel.PMID:38581420 | PMC:PMC10998640 | DOI:10.1093/bib/bbae145 (Source: Briefings in Bioinformatics)
Source: Briefings in Bioinformatics - April 6, 2024 Category: Bioinformatics Authors: Zechen Wang Sheng Wang Yangyang Li Jingjing Guo Yanjie Wei Yuguang Mu Liangzhen Zheng Weifeng Li Source Type: research
Biologically meaningful regulatory logic enhances the convergence rate in Boolean networks and bushiness of their state transition graph
Brief Bioinform. 2024 Mar 27;25(3):bbae150. doi: 10.1093/bib/bbae150.ABSTRACTBoolean models of gene regulatory networks (GRNs) have gained widespread traction as they can easily recapitulate cellular phenotypes via their attractor states. Their overall dynamics are embodied in a state transition graph (STG). Indeed, two Boolean networks (BNs) with the same network structure and attractors can have drastically different STGs depending on the type of Boolean functions (BFs) employed. Our objective here is to systematically delineate the effects of different classes of BFs on the structural features of the STG of reconstructe...
Source: Briefings in Bioinformatics - April 6, 2024 Category: Bioinformatics Authors: Priyotosh Sil Ajay Subbaroyan Saumitra Kulkarni Olivier C Martin Areejit Samal Source Type: research
HyGAnno: hybrid graph neural network-based cell type annotation for single-cell ATAC sequencing data
Brief Bioinform. 2024 Mar 27;25(3):bbae152. doi: 10.1093/bib/bbae152.ABSTRACTReliable cell type annotations are crucial for investigating cellular heterogeneity in single-cell omics data. Although various computational approaches have been proposed for single-cell RNA sequencing (scRNA-seq) annotation, high-quality cell labels are still lacking in single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) data, because of extreme sparsity and inconsistent chromatin accessibility between datasets. Here, we present a novel automated cell annotation method that transfers cell type information from a well-l...
Source: Briefings in Bioinformatics - April 6, 2024 Category: Bioinformatics Authors: Weihang Zhang Yang Cui Bowen Liu Martin Loza Sung-Joon Park Kenta Nakai Source Type: research
Computational model for drug research
Brief Bioinform. 2024 Mar 27;25(3):bbae158. doi: 10.1093/bib/bbae158.ABSTRACTThis special issue focuses on computational model for drug research regarding drug bioactivity prediction, drug-related interaction prediction, modelling for immunotherapy and modelling for treatment of a specific disease, as conveyed by the following six research and four review articles. Notably, these 10 papers described a wide variety of in-depth drug research from the computational perspective and may represent a snapshot of the wide research landscape.PMID:38581423 | PMC:PMC10998638 | DOI:10.1093/bib/bbae158 (Source: Briefings in Bioinformatics)
Source: Briefings in Bioinformatics - April 6, 2024 Category: Bioinformatics Authors: Xing Chen Li Huang Source Type: research
Correction to: Introducing π-HelixNovo for practical large-scale de novo peptide sequencing
Brief Bioinform. 2024 Mar 27;25(3):bbae134. doi: 10.1093/bib/bbae134.NO ABSTRACTPMID:38581650 | PMC:PMC10998637 | DOI:10.1093/bib/bbae134 (Source: Briefings in Bioinformatics)
Source: Briefings in Bioinformatics - April 6, 2024 Category: Bioinformatics Source Type: research
Correction to: Adjustment of scRNA-seq data to improve cell-type decomposition of spatial transcriptomics
Brief Bioinform. 2024 Mar 27;25(3):bbae155. doi: 10.1093/bib/bbae155.NO ABSTRACTPMID:38581651 | PMC:PMC10998650 | DOI:10.1093/bib/bbae155 (Source: Briefings in Bioinformatics)
Source: Briefings in Bioinformatics - April 6, 2024 Category: Bioinformatics Source Type: research
GLDM: hit molecule generation with constrained graph latent diffusion model
Brief Bioinform. 2024 Mar 27;25(3):bbae142. doi: 10.1093/bib/bbae142.ABSTRACTDiscovering hit molecules with desired biological activity in a directed manner is a promising but profound task in computer-aided drug discovery. Inspired by recent generative AI approaches, particularly Diffusion Models (DM), we propose Graph Latent Diffusion Model (GLDM)-a latent DM that preserves both the effectiveness of autoencoders of compressing complex chemical data and the DM's capabilities of generating novel molecules. Specifically, we first develop an autoencoder to encode the molecular data into low-dimensional latent representations...
Source: Briefings in Bioinformatics - April 6, 2024 Category: Bioinformatics Authors: Conghao Wang Hiok Hian Ong Shunsuke Chiba Jagath C Rajapakse Source Type: research
DeepFGRN: inference of gene regulatory network with regulation type based on directed graph embedding
Brief Bioinform. 2024 Mar 27;25(3):bbae143. doi: 10.1093/bib/bbae143.ABSTRACTThe inference of gene regulatory networks (GRNs) from gene expression profiles has been a key issue in systems biology, prompting many researchers to develop diverse computational methods. However, most of these methods do not reconstruct directed GRNs with regulatory types because of the lack of benchmark datasets or defects in the computational methods. Here, we collect benchmark datasets and propose a deep learning-based model, DeepFGRN, for reconstructing fine gene regulatory networks (FGRNs) with both regulation types and directions. In addit...
Source: Briefings in Bioinformatics - April 6, 2024 Category: Bioinformatics Authors: Zhen Gao Yansen Su Junfeng Xia Rui-Fen Cao Yun Ding Chun-Hou Zheng Pi-Jing Wei Source Type: research
Bayesian functional analysis for untargeted metabolomics data with matching uncertainty and small sample sizes
Brief Bioinform. 2024 Mar 27;25(3):bbae141. doi: 10.1093/bib/bbae141.ABSTRACTUntargeted metabolomics based on liquid chromatography-mass spectrometry technology is quickly gaining widespread application, given its ability to depict the global metabolic pattern in biological samples. However, the data are noisy and plagued by the lack of clear identity of data features measured from samples. Multiple potential matchings exist between data features and known metabolites, while the truth can only be one-to-one matches. Some existing methods attempt to reduce the matching uncertainty, but are far from being able to remove the ...
Source: Briefings in Bioinformatics - April 6, 2024 Category: Bioinformatics Authors: Guoxuan Ma Jian Kang Tianwei Yu Source Type: research
From tradition to innovation: conventional and deep learning frameworks in genome annotation
Brief Bioinform. 2024 Mar 27;25(3):bbae138. doi: 10.1093/bib/bbae138.ABSTRACTFollowing the milestone success of the Human Genome Project, the 'Encyclopedia of DNA Elements (ENCODE)' initiative was launched in 2003 to unearth information about the numerous functional elements within the genome. This endeavor coincided with the emergence of numerous novel technologies, accompanied by the provision of vast amounts of whole-genome sequences, high-throughput data such as ChIP-Seq and RNA-Seq. Extracting biologically meaningful information from this massive dataset has become a critical aspect of many recent studies, particularl...
Source: Briefings in Bioinformatics - April 6, 2024 Category: Bioinformatics Authors: Zhaojia Chen Noor Ul Ain Qian Zhao Xingtan Zhang Source Type: research
PUTransGCN: identification of piRNA-disease associations based on attention encoding graph convolutional network and positive unlabelled learning
Brief Bioinform. 2024 Mar 27;25(3):bbae144. doi: 10.1093/bib/bbae144.ABSTRACTPiwi-interacting RNAs (piRNAs) play a crucial role in various biological processes and are implicated in disease. Consequently, there is an escalating demand for computational tools to predict piRNA-disease interactions. Although there have been computational methods proposed for the detection of piRNA-disease associations, the problem of imbalanced and sparse dataset has brought great challenges to capture the complex relationships between piRNAs and diseases. In response to this necessity, we have developed a novel computational architecture, de...
Source: Briefings in Bioinformatics - April 6, 2024 Category: Bioinformatics Authors: Qiuhao Chen Liyuan Zhang Yaojia Liu Zhonghao Qin Tianyi Zhao Source Type: research
