Machine-learning-based models to predict cardiovascular risk using oculomics and clinic variables in KNHANES
Recent researches have found a strong correlation between the triglyceride-glucose (TyG) index or the atherogenic index of plasma (AIP) and cardiovascular disease (CVD) risk. However, there is a lack of resear... (Source: BioData Mining)
Source: BioData Mining - April 22, 2024 Category: Bioinformatics Authors: Yuqi Zhang, Sijin Li, Weijie Wu, Yanqing Zhao, Jintao Han, Chao Tong, Niansang Luo and Kun Zhang Tags: Research Source Type: research
TEC-miTarget: enhancing microRNA target prediction based on deep learning of ribonucleic acid sequences
MicroRNAs play a critical role in regulating gene expression by binding to specific target sites within gene transcripts, making the identification of microRNA targets a prominent focus of research. Convention... (Source: BMC Bioinformatics)
Source: BMC Bioinformatics - April 20, 2024 Category: Bioinformatics Authors: Tingpeng Yang, Yu Wang and Yonghong He Tags: Research Source Type: research
Noisecut: a python package for noise-tolerant classification of binary data using prior knowledge integration and max-cut solutions
Classification of binary data arises naturally in many clinical applications, such as patient risk stratification through ICD codes. One of the key practical challenges in data classification using machine lea... (Source: BMC Bioinformatics)
Source: BMC Bioinformatics - April 20, 2024 Category: Bioinformatics Authors: Moein E. Samadi, Hedieh Mirzaieazar, Alexander Mitsos and Andreas Schuppert Tags: Software Source Type: research
Drug-Online: an online platform for drug-target interaction, affinity, and binding sites identification using deep learning
Accurately identifying drug-target interaction (DTI), affinity (DTA), and binding sites (DTS) is crucial for drug screening, repositioning, and design, as well as for understanding the functions of target. Alt... (Source: BMC Bioinformatics)
Source: BMC Bioinformatics - April 20, 2024 Category: Bioinformatics Authors: Xin Zeng, Guang-Peng Su, Shu-Juan Li, Shuang-Qing Lv, Meng-Liang Wen and Yi Li Tags: Software Source Type: research
A protein network refinement method based on module discovery and biological information
The identification of essential proteins can help in understanding the minimum requirements for cell survival and development to discover drug targets and prevent disease. Nowadays, node ranking methods are a ... (Source: BMC Bioinformatics)
Source: BMC Bioinformatics - April 20, 2024 Category: Bioinformatics Authors: Li Pan, Haoyue Wang, Bo Yang and Wenbin Li Tags: Research Source Type: research
MMGAT: a graph attention network framework for ATAC-seq motifs finding
Motif finding in Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) data is essential to reveal the intricacies of transcription factor binding sites (TFBSs) and their pivotal roles in gene... (Source: BMC Bioinformatics)
Source: BMC Bioinformatics - April 20, 2024 Category: Bioinformatics Authors: Xiaotian Wu, Wenju Hou, Ziqi Zhao, Lan Huang, Nan Sheng, Qixing Yang, Shuangquan Zhang and Yan Wang Tags: Research Source Type: research
Bounds on the Ultrasensitivity of Biochemical Reaction Cascades
We examined an upper bound for the Hill coefficient of the composition of two functions, namely the product of their individual Hill coefficients. We proved that this upper bound holds for compositions of Hill functions, and that there are instances of counterexamples that exist for more general sigmoidal functions. Additionally, we tested computationally other types of sigmoidal functions, such as the logistic and inverse trigonometric functions, and we provided computational evidence that in these cases the inequality also holds. We show that in large generality there is a limit to how ultrasensitive the composition of t...
Source: Bulletin of Mathematical Biology - April 18, 2024 Category: Bioinformatics Authors: Marcello Pajoh-Casco Abishek Vinujudson German Enciso Source Type: research
Structural simulation and selective inhibitor discovery study for histone demethylases KDM4E/6B from a computational perspective
This study focuses on two members of the lysine demethylase (KDM) family, KDM4E and KDM6B, which are significant in gene regulation and disease pathogenesis. KDM4E demonstrates selectivity for gene regulation, particularly concerning cancer, while KDM6B is implicated in inflammation and cancer. The study utilizes specific inhibitors, DA-24905 and GSK-J1, showcasing their exceptional selectivity for KDM4E and KDM6B, respectively. Employing an array of computational simulations, including sequence alignment, molecular docking, dynamics simulations, and free energy calculations, we conclude that although the binding cavities ...
Source: Computational Biology and Chemistry - April 18, 2024 Category: Bioinformatics Authors: Chenxiao Wang Baichun Hu Yi Yang Yihan Wang Juyue Qin Xiaolian Wen Yikuan Li Hui Li Yutong Wang Jian Wang Yang Liu Source Type: research
TMBstable: a variant caller controls performance variation across heterogeneous sequencing samples
In this study, we introduce TMBstable, an innovative method that dynamically selects optimal variant calling strategies for specific genomic regions using a meta-learning framework, distinguishing it from traditional callers with uniform sample-wide strategies. The process begins with segmenting the sample into windows and extracting meta-features for clustering, followed by using a pre-trained meta-model to select suitable algorithms for each cluster, thereby addressing strategy-sample mismatches, reducing performance fluctuations and ensuring consistent performance across various samples. We evaluated TMBstable using bot...
Source: Briefings in Bioinformatics - April 18, 2024 Category: Bioinformatics Authors: Shenjie Wang Xiaoyan Zhu Xuwen Wang Yuqian Liu Minchao Zhao Zhili Chang Xiaonan Wang Yang Shao Jiayin Wang Source Type: research
Topological and geometric analysis of cell states in single-cell transcriptomic data
Brief Bioinform. 2024 Mar 27;25(3):bbae176. doi: 10.1093/bib/bbae176.ABSTRACTSingle-cell RNA sequencing (scRNA-seq) enables dissecting cellular heterogeneity in tissues, resulting in numerous biological discoveries. Various computational methods have been devised to delineate cell types by clustering scRNA-seq data, where clusters are often annotated using prior knowledge of marker genes. In addition to identifying pure cell types, several methods have been developed to identify cells undergoing state transitions, which often rely on prior clustering results. The present computational approaches predominantly investigate t...
Source: Briefings in Bioinformatics - April 18, 2024 Category: Bioinformatics Authors: Tram Huynh Zixuan Cang Source Type: research
Identifying Diffuse Glioma Subtypes Based on Pathway Enrichment Evaluation
AbstractGliomas are highly heterogeneous in molecular, histology, and microenvironment. However, a classification of gliomas by integrating different tumor microenvironment (TME) components remains unexplored. Based on the enrichment scores of 17 pathways involved in immune, stromal, DNA repair, and nervous system signatures in diffuse gliomas, we performed consensus clustering to uncover novel subtypes of gliomas. Consistently in three glioma datasets (TCGA-glioma, CGGA325, and CGGA301), we identified three subtypes: Stromal-enriched (Str-G), Nerve-enriched (Ner-G), and mixed (Mix-G). Ner-G was charactered by low immune...
Source: Interdisciplinary Sciences, Computational Life Sciences - April 18, 2024 Category: Bioinformatics Source Type: research
TrieDedup: a fast trie-based deduplication algorithm to handle ambiguous bases in high-throughput sequencing
High-throughput sequencing is a powerful tool that is extensively applied in biological studies. However, sequencers may produce low-quality bases, leading to ambiguous bases, ‘N’s. PCR duplicates introduced i... (Source: BMC Bioinformatics)
Source: BMC Bioinformatics - April 18, 2024 Category: Bioinformatics Authors: Jianqiao Hu, Sai Luo, Ming Tian and Adam Yongxin Ye Tags: Software Source Type: research
stDiff: a diffusion model for imputing spatial transcriptomics through single-cell transcriptomics
Brief Bioinform. 2024 Mar 27;25(3):bbae171. doi: 10.1093/bib/bbae171.ABSTRACTSpatial transcriptomics (ST) has become a powerful tool for exploring the spatial organization of gene expression in tissues. Imaging-based methods, though offering superior spatial resolutions at the single-cell level, are limited in either the number of imaged genes or the sensitivity of gene detection. Existing approaches for enhancing ST rely on the similarity between ST cells and reference single-cell RNA sequencing (scRNA-seq) cells. In contrast, we introduce stDiff, which leverages relationships between gene expression abundance in scRNA-se...
Source: Briefings in Bioinformatics - April 17, 2024 Category: Bioinformatics Authors: Kongming Li Jiahao Li Yuhao Tao Fei Wang Source Type: research
Attention-guided variational graph autoencoders reveal heterogeneity in spatial transcriptomics
In this study, we introduce AttentionVGAE (AVGN), which integrates slice images, spatial information and raw gene expression while calibrating low-quality gene expression. By combining the variational graph autoencoder with multi-head attention blocks (MHA blocks), AVGN captures spatial relationships in tissue gene expression, adaptively focusing on key features and alleviating the need for prior knowledge of cluster numbers, thereby achieving superior clustering performance. Particularly, AVGN attempts to balance the model's attention focus on local and global structures by utilizing MHA blocks, an aspect that current gra...
Source: Briefings in Bioinformatics - April 17, 2024 Category: Bioinformatics Authors: Lixin Lei Kaitai Han Zijun Wang Chaojing Shi Zhenghui Wang Ruoyan Dai Zhiwei Zhang Mengqiu Wang Qianjin Guo Source Type: research
Attention-guided variational graph autoencoders reveal heterogeneity in spatial transcriptomics
In this study, we introduce AttentionVGAE (AVGN), which integrates slice images, spatial information and raw gene expression while calibrating low-quality gene expression. By combining the variational graph autoencoder with multi-head attention blocks (MHA blocks), AVGN captures spatial relationships in tissue gene expression, adaptively focusing on key features and alleviating the need for prior knowledge of cluster numbers, thereby achieving superior clustering performance. Particularly, AVGN attempts to balance the model's attention focus on local and global structures by utilizing MHA blocks, an aspect that current gra...
Source: Briefings in Bioinformatics - April 17, 2024 Category: Bioinformatics Authors: Lixin Lei Kaitai Han Zijun Wang Chaojing Shi Zhenghui Wang Ruoyan Dai Zhiwei Zhang Mengqiu Wang Qianjin Guo Source Type: research
