Briefings in bioinformatics最新文献

{"title":"BOMIFA: biologically informed multi-omics integration with graph contrastive learning for cancer prognosis in women.","authors":"Zixiao Lu, Jiajun Wang, Yuping Liang, Zhenghao Lin, Yingyin Tan, Qian Ma, Wu Zhou, Yi Zhao, Siwen Xu","doi":"10.1093/bib/bbag500","DOIUrl":"10.1093/bib/bbag500","url":null,"abstract":"<p><p>Accurate survival prediction remains a central challenge in precision oncology, particularly for female patients whose sex-specific molecular characteristics are often under-modeled in prior studies. Although multi-omics integration enables a deeper exploration of prognostic biomarkers, existing methods rely on mathematically driven fusion strategies, which tend to dilute omics-specific signals and fail to capture biological regulatory hierarchies across omics layers. To address these limitations, we propose BOMIFA (Biologically informed Omics representation and Multi-omics Integration Framework), a deep graph-based framework for survival prediction and biomarker discovery in female patients using DNA methylation, mRNA, and miRNA expression data. BOMIFA incorporates two key innovations. First, graph contrastive learning is leveraged within each omics encoder to enhance intra-omics representation learning and amplify prognostically relevant signals. Then, a biologically informed cross-omics attention mechanism is deployed to explicitly model directional regulatory dependencies, enabling inter-omics information exchange aligned with known molecular hierarchies. Extensive benchmarking on eight cancer cohorts demonstrates that BOMIFA consistently outperforms existing prognostic methods in female patients. Moreover, saliency map-based gradient attribution enables the identification of female-associated prognostic biomarkers that were overlooked in prior mixed-sex analyses.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13563673/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148944293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CoMR: an integrative scoring pipeline for comprehensive mitochondrial proteome reconstruction across eukaryotes.","authors":"Julie Boisard, Shelby K Williams, Andrew J Roger, Courtney W Stairs","doi":"10.1093/bib/bbag474","DOIUrl":"10.1093/bib/bbag474","url":null,"abstract":"<p><p>Mitochondrial proteome reconstruction from eukaryotic sequence data typically relies on prediction of mitochondrial targeting signals (MTSs). However, MTS predictors are primarily trained on model organisms and may perform poorly in phylogenetically divergent lineages or in organisms with atypical or reduced targeting sequences. Accurate reconstruction therefore requires integration of complementary sources of evidence beyond targeting prediction alone. We developed Comprehensive Mitochondrial Reconstructor (CoMR), an integrative workflow that combines targeting prediction, curated homology searches, large-scale similarity searches, and automated phylogenetic analysis within a unified scoring framework. Benchmarking on the model yeast Saccharomyces cerevisiae yielded strong discriminatory performance [receiver operating characteristic (ROC)-area under the curve (AUC) = 0.92], exceeding standalone prediction with TargetP2, a predictor of N-terminal targeting peptides (ROC-AUC = 0.72). In the divergent anaerobic protist Paratrimastix pyriformis, CoMR maintained robust performance (ROC-AUC = 0.86) validated with an experimental proteome despite extreme class imbalance, achieving a precision-recall AUC of 0.183 (~78-fold enrichment over random expectation and ~10-fold improvement over TargetP2). Ablation analyses demonstrate that predictive performance is robust to individual evidence-layer removal, while overlap analyses showed that homology-based searches recovered candidates missed by targeting predictors, particularly in P. pyriformis. Overall, CoMR improves mitochondrial proteome reconstruction over targeting prediction alone and provides a reproducible workflow for predicting mitochondrial and mitochondrion-related organelle protein repertoires across eukaryotes to aid investigations of organelle evolution and proteome reduction.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13548318/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148900032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A high-dimensional benchmark of objective functions and biological resolutions for personalized metabolic phenotyping.","authors":"Mehmet Ali Erdoğan, Ali Cakmak","doi":"10.1093/bib/bbag528","DOIUrl":"https://doi.org/10.1093/bib/bbag528","url":null,"abstract":"<p><p>Genome-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.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13625657/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149104236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contextual evaluation of microRNA sequencing data harmonization for sample clustering.","authors":"Jian Zou, Yannick Düren, Xinyi Wang, Ying Xiang, Yunhui Qi, Miao Wang, Yilin Wu, Samuel Singer, Li-Xuan Qin","doi":"10.1093/bib/bbag514","DOIUrl":"10.1093/bib/bbag514","url":null,"abstract":"<p><p>Reliable translation of microRNA (miRNA) sequencing data depends on effective harmonization to mitigate artifacts from variable experimental handling. Although many harmonization methods exist, prior evaluations have focused mainly on differential expression analysis, leaving the impact on subgroup discovery understudied. We present a framework for evaluating miRNA sequencing data harmonization in the context of sample clustering that integrates artificial intelligence (AI)-augmented datasets, statistical evaluation pipelines, and accessible software tools, enabling systematic comparisons across diverse signal-to-artifact ratios and cluster-composition settings. Using this framework, we show that harmonization can, often partially, mitigate artifact-associated losses in clustering accuracy, especially at moderate signal-to-artifact ratios, with the extent of mitigation depending on the specific harmonization method, the paired clustering technique, and the cluster-composition setting. We further confirm these findings by analyzing reconstructed cohorts from The Cancer Genome Atlas breast cancer miRNA sequencing data. Collectively, these results underscore the need for tailored harmonization to support reliable subgroup discovery and highlight the broader importance of context-specific workflows in translational genomics.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13602272/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149036622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Differential analysis of microbial interaction networks.","authors":"Marianna Milano, Pietro Hiram Guzzi","doi":"10.1093/bib/bbag522","DOIUrl":"10.1093/bib/bbag522","url":null,"abstract":"<p><p>Microbiome studies increasingly indicate that disease-associated shifts cannot be understood from compositional changes alone. The functional architecture of microbial communities-encoded in patterns of association among microbial gene families-may reveal how these systems reorganize across biological conditions. Here, we present a network-based framework for characterizing microbiome rewiring across conditions. The approach combines condition-specific network inference, differential network analysis, and pathway-level network analysis to identify associations that are gained, lost, or altered between groups, with a specific focus on sex-dependent differences. We apply the framework to inflammatory bowel disease, type 2 diabetes, and atherosclerotic cardiovascular disease (ACVD), comparing male and female-specific microbial gene family networks within each disease context. Across these settings, differential networks flag large numbers of candidate rewired associations; however, permutation testing (sex labels shuffled, group sizes preserved, 500 permutations for gene-family networks, and 1000 for pathway networks) shows that the global amount of apparent rewiring is not greater than expected under the null at the global or edge level in any cohort, and that most edges exclusive to one group are induced by group-specific feature filtering rather than by a genuine change in association ($sim $80%-83% in ACVD). We therefore present the method as a rigorously validated framework and a cautionary case study: the differential-network machinery is sound, but the headline biological signal in a naive analysis is largely a property of correlation thresholding and, for the longitudinal inflammatory bowel disease (IBD) cohort, of pseudoreplication. The only non-null result across all validations is a SOHPIE-DNA per-taxon test in the IBD disease arm (15 taxa at FDR $< 0.05$), which we report as a single nominal finding requiring independent replication. Code, data, and supplementary information are available at https://github.com/mmilano87/NetMicrobiome.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13591281/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149011942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"KAT-Net: a hyperbolic routing framework for joint lysine acylation site prediction with shared sequence-structure representations.","authors":"Hao Wu, Yiyu Lin, Lun Zhu, Yan Wang, Shan Chang, Sen Yang","doi":"10.1093/bib/bbag567","DOIUrl":"10.1093/bib/bbag567","url":null,"abstract":"<p><p>Protein lysine acylation regulates protein function through changes in local chemistry, structure, and molecular interactions. Acetylation (Kac), succinylation (Ksucc), and crotonylation (Kcr) share the lysine centre but differ in acyl-group chemistry, motivating joint yet task-aware prediction. We developed KAT-Net, a multi-task framework integrating ESM-2 representations, physicochemical priors, and AlphaFold2-derived structural context. Predicted aligned error-aware graph modelling reduces uncertain structural relationships, while task-driven hyperbolic routing retrieves modification-specific information from shared representations. A masked asymmetric focal loss supports learning with unavailable labels and task-dependent class imbalance. Independent benchmark and external Kcr evaluations showed favourable performance, while ablation and single-task comparisons supported contributions from sequence-structure integration, structural modelling, hyperbolic routing, and multi-task learning. Sequence and structural analyses revealed modification-related positional, residue, and spatial patterns around candidate lysines. External functional analysis using an independent mouse lung acetylome and succinylome provided complementary evidence of functional plausibility, with Kac predictions showing partial concordance with experimentally reported metabolic processes rather than serving as a binary test of general Kac-site discrimination. For Kac prediction, model development and primary benchmarking were performed using the curated non-histone acetylation dataset from TransPTM and should be interpreted within this benchmark scope rather than as a comprehensive evaluation of the complete acetylome. KAT-Net therefore provides a unified framework for lysine-acylation prediction and candidate-site prioritization within the evaluated benchmark settings.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13657056/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149164050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UcTCRp: a TCRβ-based framework for identifying and profiling MAIT- and iNKT-associated repertoire signals.","authors":"Linlin Chen, Yueyi Li, Shiwen Shan, Kaixuan Wang, Chao Feng, Yunsheng Dou, Qihang Xu, Linru Cai, Hao Wang, Haiyang Wang, Xiaochen Bo, Jian Zhang","doi":"10.1093/bib/bbag553","DOIUrl":"10.1093/bib/bbag553","url":null,"abstract":"<p><p>Mucosal-associated invariant T (MAIT) cells and invariant natural killer T (iNKT) cells are conventionally identified using invariant or semi-invariant TCRα chains, antigen-loaded tetramers, or transcriptomic phenotypes. These requirements limit their detection in public and clinical immune-repertoire datasets that contain only TCRβ sequences. Here we present UcTCRp, a TCRβ-only framework for detecting and profiling relative MAIT- and iNKT-associated signals in bulk immune repertoires. We first identified model-informative TCRβ features, including biased V-gene usage and reproducible CDR3β amino acid composition, in collected human and mouse MAIT and iNKT TCRβ sequences. UcTCRp subsequently integrates these features using species-specific transformer-based representation pretrained separately on large human and mouse TCRβ repertoires, followed by independent supervised modeling with curated MAIT, iNKT, and conventional T cell references for each species. The resulting species-specific models generalized robustly across independent human and mouse cohorts. In paired scRNA-seq/scTCR-seq datasets, UcTCRp recovered transcriptome-defined MAIT and iNKT cells and identified additional MAIT-like candidates supported by receptor evidence but missed by expression-only annotation. Bulk calibration against paired single-cell references and synthetic spike-in experiments established operating characteristics for repertoire-level abundance estimation. These results establish unpaired TCRβ repertoires as an actionable substrate for detecting relative unconventional T cell-associated immune signals, enabling archived repertoire resources to be repurposed for systems-level studies of tissue immunity, disease, and therapeutic response.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13677069/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149219890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive evaluation of AlphaFold/OpenFold prediction of experimentally unresolved proteins through novel metrics.","authors":"Florencia R Díaz, Daniela Orschanski, Juan I Folco, Juana Espain Ceci, Guadalupe Nibeyro, Horderlin Robles Vega, Juan P Nicola, Elmer A Fernández","doi":"10.1093/bib/bbag461","DOIUrl":"10.1093/bib/bbag461","url":null,"abstract":"<p><p>Predicting accurate protein structures is essential for understanding molecular mechanisms, interpreting the impact of sequence variation, and supporting translational applications ranging from drug discovery to clinical genomics. Recent advances in deep-learning-based predictors such as AlphaFold2, OpenFold, and AlphaFold3 have transformed structural biology, enabling routine in silico modeling even for challenging or previously uncharacterized proteins. However, systematic benchmarking of these tools-especially for novel targets and single amino acid variants-remains limited. Conventional global metrics often fail to capture biologically meaningful discrepancies. By evaluating multiple implementations of AlphaFold2 and OpenFold, together with ColabFold and the AlphaFold3 server, across 10 different proteins and 222 single amino acid protein variants encompassing a wide range of sizes, structures, and functions, we show that although widely used global indicators-like mean pLDDT, pTM-score, and RMSD-frequently suggest comparable performance, substantial local-level differences remain elusive. To address this gap, we introduce a comparative framework leveraging Bland-Altman agreement analysis, to evaluate per-residue Cα-confidence differences and Per-Residue profiles (PRPs), complemented by Uniform Manifold Approximation and Projection (UMAP). This approach reveals marked localized divergences, particularly within flexible or intrinsically disordered regions, where both predictor choice and single-residue substitutions trigger the largest conformational shifts. We further demonstrate that using reduced homology databases has minimal impact on predicted structural quality, offering computationally efficient alternatives. Collectively, our findings underscore the importance of integrating global and residue-specific evaluations to more accurately assess robustness, agreement, and practical usability across contemporary protein structure prediction methods.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13541284/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148886372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Benchmarking methods for extracting microbial signal from host-dominated metatranscriptomes.","authors":"Antonin Colajanni, Raluca Uricaru, Samuel Darko, Rahul Subramanian, Daniel C Douek, Rodolphe Thiébaut, Patricia Thebault","doi":"10.1093/bib/bbag454","DOIUrl":"10.1093/bib/bbag454","url":null,"abstract":"<p><p>Human RNA sequencing (RNA-seq) data originally generated for human transcriptome profiling are overwhelmingly dominated by host sequences, yet they often contain a small fraction of non-human reads that can be exploited for microbial detection. When such datasets are repurposed for secondary microbiome-oriented analyses, extracting and accurately classifying this weak microbial signal becomes technically challenging, and no ready-to-use pipeline currently exists. In this study, we evaluate computational strategies for filtering host reads and classifying microbial transcripts in host-dominated RNA sequencing data. We compare assembly-based approaches similar to those used in a previous study focusing on microbial translocation with state-of-the-art assembly-free methods, and assess their respective strengths and limitations using simulated datasets reflecting low microbial abundance. Our results show that assembly-based methods yield accurate taxonomic predictions but struggle at low read depth, whereas assembly-free methods are more robust in sparse settings at the cost of reduced precision. To leverage the complementarity of both approaches, we propose a hybrid pipeline that integrates assembly-based and assembly-free classification. On simulated data, this hybrid strategy improves microbial classification performance compared with either approach alone. Application to a real human metatranscriptomic dataset analyzed in a microbial translocation context illustrates the broader microbial signal captured by the hybrid approach, despite intrinsic challenges related to the absence of reliable ground truth and the risk of host read misclassification. Our work provides a framework for extracting microbial signals from host-dominated human metatranscriptomes, enabling the reuse of existing transcriptomic datasets for microbiome-related analyses, including but not limited to microbial translocation studies.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13537457/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148878798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IRS: iterative reference selection improves normalization of microbiome sequencing data.","authors":"Yiming Shi, Lili Liu, Jun Chen, Kristine M Wylie, Todd N Wylie, Sung Hee Park, Ruiwen Zhou, Yin Cao, Stephanie A Fritz, Molly J Stout, Maria Cristina Vazquez Guillamet, Lei Liu","doi":"10.1093/bib/bbag495","DOIUrl":"10.1093/bib/bbag495","url":null,"abstract":"<p><p>Microbiome studies often seek to determine how the absolute abundances of individual taxa change across biological conditions, yet sequencing read counts are sample-specific scaled representations of those abundances. Because sampling depth can differ across samples, fold changes calculated directly from sequencing read counts do not generally represent absolute-abundance fold changes. Normalization methods attempt to account for these between-sample differences in sampling depth, but their accuracy depends on the reference used. In particular, total-sum scaling uses all taxa as the reference and can introduce compositional bias. Reference-based methods instead rely on taxa that are stable across conditions, but contamination of the reference set by differentially abundant (DA) taxa can distort sampling-depth estimation and downstream inference. Here, we present iterative reference selection (IRS), a robust normalization method that iteratively screens and refines a candidate reference set to exclude DA taxa. By deriving a clean reference set, IRS accurately captures between-sample differences in sampling depth and recovers absolute-abundance fold changes. Benchmarking using simulations and datasets with experimental absolute quantification shows that IRS outperforms standard scaling and existing reference-based methods in controlling false discovery rates while maintaining power.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13573600/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148966493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}