BRIEFINGS IN BIOINFORMATICS生物信息学简报
BRIEFINGS IN BIOINFORMATICS(英文缩写 BRIEF BIOINFORM),ISSN 1467-5463,eISSN 1477-4054,中文译名:生物信息学简报 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-10-01 至 2026-09-30，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 13.994 | Q1 |
| 2022 | 9.500 | Q1 |
| 2023 | 6.800 | Q1 |
| 2024 | 7.700 | Q1 |
| 2025 | 7.300 | Q1 |
BRIEFINGS IN BIOINFORMATICS 最新收录文献
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1. Stepwise multi-scale reconstruction of cell spatial organization from single-cell RNA sequencing data with Cell2space.
PMID:日期:2026-09-01Elucidating the spatial organization of cells is fundamental to understanding tissue architecture and function, yet tissue dissociation during single-cell RNA sequencing (scRNA-seq) strips cells of their native architectural context. It is essential to integrate scRNA-seq data with spatial transcriptomics (ST) references to infer the spatial information of individual cells. Despite the development of various tools, achieving precise and biologically relevant spatial reconstructions remains challenging. Here, we present Cell2space, a deep learning framework that integrates scRNA-seq data with ST references to reconstruct multi-scale cellular spatial organization in a stepwise manner. Instead of treating spatial reconstruction as coordinate regression, Cell2space learns a universal spatial affinity function and employs a hierarchical inference strategy that integrates domain-level priors to refine cellular neighborhoods. This approach enables accurate assignment of single cells to spatial domains and inference of cell-cell neighborhood relationships. Comprehensive evaluations demonstrate that Cell2space robustly achieves superior performance in both domain assignment and neighborhood inference. Applied to the mouse visual cortex and human skin, Cell2space identifies layer-specific markers, reveals continuous gene expression gradients, and faithfully reconstructs stratified tissue architecture without requiring prior annotations. Together, Cell2space provides a powerful framework for integrating single-cell and spatial modalities, enabling deeper insights into tissue organization and cellular microenvironments.
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2. Machine learning methodology using a masked neural network for robust genetic risk score calculation from noisy and missing data.
PMID:日期:2026-09-01Genetic risk scores (GRSs) are summaries of genetic data that can improve prediction of disease risk and progression. GRSs are increasing available but rely on high-quality input data to produce good output results; with noisy or missing inputs the GRS may be inaccurate. We aimed to develop a method to produce a robust estimate of the GRS when input data are missing, noisy or both. We developed a neural network approach, named masked-multi-layer perceptron (MLP), for robust GRS calculation trained on a set of GRS scores calculated on clean data. The masked-MLP includes additional input data and has noise inserted during training, both which make the model more robust. A GRS for type 1 diabetes (T1D) calculated on input data with 10% of the data corrupted had a Spearman rank correlation to the clean GRS of 0.669 (0.665-0.674) while the equivalent for the masked-MLP was 0.951 (0.950-0.952). For the same data, the area under the receiver operating characteristic curve for separation of T1D from population samples fell from 0.919 (0.904-0.932) to 0.808 (0.787-0.827) for the GRS while the masked-MLP fell to 0.910 (0.895-0.924). The masked-MLP was more robust to noise when calculating a GRS than using standard approaches. Our approach has the potential to enable GRS calculations which are more robust to noise.
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3. spMosaic: multi-sample integration for scalable spatial domain discovery based on spatial transcriptomics.
PMID:日期:2026-09-01Spatial transcriptomics enables high-resolution profiling of gene expression within intact tissue architecture, providing new opportunities to study the spatial organization of tissue structures and cell types. However, identifying spatial domains that are reproducible across samples and individuals remains challenging due to batch effects, inter-individual heterogeneity, increasing dataset sizes and the incomparability of spatial coordinates across independently processed tissue sections. We propose spMosaic, a scalable computational framework for multi-sample spatial domain identification. The method integrates two key components: (i) generalized additive model-based spatial smoothing to identify spatially variable genes shared across samples, and (ii) a batch-aware dual-encoder autoencoder that learns low-dimensional representations while encouraging sample-associated variation to be represented separately from domain-relevant biological variation. A subsequent deep embedded clustering refinement step further improves the separation and compactness of inferred domain assignments. We evaluated spMosaic against nine existing methods in both single-individual and multi-individual settings across diverse datasets spanning five technologies. spMosaic consistently achieved strong clustering performance relative to existing methods while maintaining competitive sample integration. In addition, under the computational settings used in our benchmark, spMosaic was the only method among those evaluated that successfully scaled to a large cohort, enabling the analysis of 378 918 spatial spots from 31 frontal cortex samples across 12 mice. These results demonstrate that spMosaic provides an integrative and scalable approach for multi-sample spatial transcriptomics analysis, enabling accurate and interpretable characterization of spatial tissue organization.
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4. Hidden causal inference delineates dynamic lncRNA regulation in autism spectrum disorder.
PMID:日期:2026-09-01Long non-coding RNAs (lncRNAs) are increasingly implicated in autism spectrum disorder (ASD), yet their dynamic regulatory roles remain poorly characterized. Conventional static causal inference methods are limited in capturing the dynamic interplay of lncRNAs across diverse brain biological contexts. Here, we develop DCRNet (Dynamic Causal Regulation Network), a hidden causality-based framework to infer lncRNA regulatory relationships in ASD. Our analysis reveals that dark causality, representing a hybrid form of positive and negative interdependencies, is prevalent in lncRNA-mediated regulation. Moreover, these dynamic regulatory patterns emerge across multiple biological contexts, and eight ASD susceptibility lncRNAs may be linked to disrupted gene regulatory programs. We also indicate that lncRNA expression signatures distinguish sex and cell type with high accuracy, offering candidate diagnostic biomarkers. Notably, 20 immune-related lncRNAs are predicted to be involved in neuroimmune regulation. Additionally, cell-cell interaction networks show notable differences between ASD and control cohorts. Furthermore, DCRNet performs comparably to the other two causal inference methods and outperforms a correlation-based approach in capturing dynamic lncRNA regulation. This study provides a framework for delineating dynamic lncRNA regulation and suggests their potential relevance to ASD pathophysiology.
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5. MMP2Mol: a matched molecular pairs-based framework for ligand-based de novo drug design.
PMID:日期:2026-09-01Designing target-relevant molecules is particularly difficult when only limited bioactivity data are available. We present MMP2Mol, a ligand-based generative framework that combines matched molecular pair (MMP) analysis with a pretrained chemical language model (CLM). MMP2Mol extracts target-specific structure-activity transformations from available ligands, applies the prioritized transformations to construct a virtual focused library, and fine-tunes the CLM toward target-relevant chemical space. The framework was evaluated across ten therapeutic targets and compared with direct CLM fine-tuning, Seq2Seq, and Reinvent 4. In repeated experiments on F2, BRD4, and PARP1, MMP2Mol achieved 92.8%-95.3% validity and 99.5%-99.7% uniqueness. When both methods were evaluated against the same original active-compound reference, molecular novelty increased from 57.8%-64.0% for the CLM baseline to 79.8%-82.0% for MMP2Mol, while internal diversity remained broadly comparable. The gains were most evident for F2 and BRD4, whereas performance varied across the smaller target datasets. These findings indicate that MMP-derived chemical knowledge can improve the focus and reproducibility of ligand-based molecular generation under limited-data conditions. MMP2Mol therefore provides a practical strategy for computational candidate generation and prioritization in early-stage drug discovery.
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6. A high-dimensional benchmark of objective functions and biological resolutions for personalized metabolic phenotyping.
PMID:日期:2026-09-01Genome-scale personalized metabolic modeling relies on objective functions to simulate intracellular flux phenotypes, yet the optimal formulation for characterizing complex human diseases remains unknown. We ask which combination of objective function, biological resolution, and feature-selection strategy yields the most reliable diagnostic signal, treating machine-learning architecture as a controlled factor. We benchmarked these choices across 57 600 experimental configurations spanning six diverse pathologies, from oncology to neurodegeneration. Within this benchmark, pathway-level minimum reaction flux was the best-performing representation, indicating that diagnostic signals are more strongly associated with rate-limiting metabolic bottlenecks than with aggregate pathway activity. Under a leakage-free evaluation, diagnostic accuracy was largely insensitive to feature density (mean $F_{1}$-score varying by <0.003 between the top-10% subset and the full feature space), so aggressive feature selection was not required. Among classifiers, only Logistic Regression lost accuracy at full density, whereas SVM was essentially unaffected and tree-based ensembles were resilient. Statistical regularization (robust_sigma) ensured consistent generalization, while topological objectives (local_k) were highly sensitive to metabolic noise, excelling only in specific high-signal cohorts. Finally, integrating objective function values as competitive features recovered the regulatory context lost during pathway aggregation. Accurate metabolic phenotyping was best captured by isolating restrictive bottleneck reactions. Predictive accuracy was largely insensitive to feature density; tree-based architectures tolerated dense omics data, while feature selection benefited mainly Logistic Regression. Overall, this benchmark offers systematic, evidence-based guidance for personalized metabolic modeling, complementing heuristic parameter tuning with quantitative comparison.
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7. Gene set mediation analysis in high-dimensional epigenetic studies.
PMID:日期:2026-09-01Mediation analysis has significantly enhanced the elucidation of underlying epigenetic mechanisms by identifying mediators that influence the effect of an exposure on an outcome. In epigenomics research, the exponential increase in the dimensionality of mediators necessitates the development of advanced high-dimensional mediation models. Here we introduce a novel Gene Set High-Dimensional Mediation Analysis (gHDMA) model to understand the gene-level mediation role of DNA methylations, building upon our previous HDMA method. We begin our analysis by applying functional principal component analysis to capture gene variation signals across regions, thereby enhancing the model's statistical power. Following this, we employ the Sure Independence Screening method, based on Wilks' $\varLambda$-test, to efficiently reduce the dimensionality to a more manageable level, enabling high-dimensional inference. Our model is distinguished by its minimal reliance on stringent assumptions about the relationship between mediators and the outcome. Extensive simulation studies, by varying the type of basis function, the nature of the outcome variable, and the dimensionality of the gene region, demonstrate the robust performance of the gHDMA model. A corroborative case study further validates the methodology's efficacy. The gHDMA model emerges as a powerful analytical tool, with an enhanced capacity to identify genes characterized by hypermethylation or hypomethylation that mediate the effects of exposure on biological outcomes.
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8. Response to "Caution: Fundamental data quality issues underlying intelligent prediction of enzyme optimal pH".
PMID:日期:2026-09-01We respond to concerns regarding fundamental data quality issues in datasets used for machine learning prediction of enzyme optimal pH (pHopt). We re-examined six questioned protein entries by tracing their annotated pH values to the original literature. Our analysis confirmed substantial discrepancies for several entries, including incorrect experimental values, misassigned protein identities, and proteins lacking the reported catalytic activity, whereas one entry was consistent with the literature. These findings support the need for rigorous verification of biochemical data underlying predictive models. We emphasize that careful literature based curation is essential for improving dataset reliability and model validity, and future iterations of pHopt predictors will be developed using more rigorously validated datasets.
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9. DDTRN: predicting bacterial transcriptional regulatory networks based on gene sequences using dual descriptor.
PMID:日期:2026-09-01Accurate computational reconstruction of bacterial transcriptional regulatory network (TRN) remains a fundamental challenge in systems biology, particularly for non-model organisms lacking extensive transcriptomic data. We present DDTRN, a sequence-driven framework that formulates TRN inference as a binary classification task over concatenated regulator-target gene sequence pairs and employs a Dual Descriptor (DD) model to predict regulatory interactions. The DD architecture represents a sequence using two learnable components: Composition Weight Map (CWM) and Position Weight Function (PWF). We comprehensively evaluate DDTRN against six conventional machine learning (ML) baselines across eight benchmark bacterial datasets, including Escherichia coli (DREAM5, RegulonDB), Bacillus subtilis, Salmonella enterica, Corynebacterium glutamicum, Mycobacterium tuberculosis, Pseudomonas aeruginosa, and Streptomyces coelicolor. DDTRN achieves superior overall performance, attaining average area under the receiver operating characteristic curve and area under the precision-recall curve scores of 0.869 and 0.868, respectively, with particularly pronounced advantages at lower descriptor ranks where positional weighting compensates for limited sequence context. Systematic sensitivity analyses of rank, embedding dimension, and basis function count reveal stable optimal operating regimes, while subsampling experiments demonstrate strong robustness even with limited training data. Interpretability analyses show that PWF learns distinct periodic contributions across different rank granularities and that CWM preferentially weights meaningful k-mers. A case study on the E. coli dataset further illustrates that DDTRN identifies method-specific candidate targets complementary to those proposed by conventional approaches. By operating solely on genomic sequence, DDTRN provides a scalable, interpretable, and data-efficient framework for bacterial TRN inference in species where expression data are scarce, and it establishes a foundation for future multimodal integration with condition-specific regulatory information.
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10. Caution: fundamental data quality issues underlying intelligent prediction of enzyme optimal pH.
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