Bioinformatics advances最新文献

Critical assessment in the age of AI: challenges and opportunities for computational biology. 人工智能时代的批判性评估：计算生物学的挑战与机遇。

IF 2.6

Bioinformatics advances Pub Date : 2026-10-16 eCollection Date: 2026-01-01 DOI: 10.1093/bioadv/vbag290

Russell Schwartz, Xuegong Zhang, Jian Ma, Yana Bromberg, Robert F Murphy, Lawrence E Hunter, Laxmi Parida, Julio Saez-Rodriguez, Gustavo Stolovitzky, Mihai Pop, Shoba Ranganathan, Donna K Slonim, Terry Gaasterland

{"title":"Critical assessment in the age of AI: challenges and opportunities for computational biology.","authors":"Russell Schwartz, Xuegong Zhang, Jian Ma, Yana Bromberg, Robert F Murphy, Lawrence E Hunter, Laxmi Parida, Julio Saez-Rodriguez, Gustavo Stolovitzky, Mihai Pop, Shoba Ranganathan, Donna K Slonim, Terry Gaasterland","doi":"10.1093/bioadv/vbag290","DOIUrl":"10.1093/bioadv/vbag290","url":null,"abstract":"Motivation: Effective computational tool development depends on our ability to measure progress with rigorous benchmarking, allowing developers to showcase advances and users to select the best of the available options. Computational biology has developed a robust infrastructure of benchmarking standards, tools/resources, and best practices to allow for rigorous and unbiased assessment. This infrastructure, however, faces growing challenges as modern artificial intelligence (AI) methods move to the forefront of computational analysis. The field has imperfectly adapted to issues problematic for benchmarking AI methods, including data entanglement, data leakage, problem-specific performance, limited theoretical guarantees, elusive ground-truth, opaque and often closed models that make reproducibility of results difficult, and dependence on impermanent data sources.Results: In this paper, we consider how the field should tackle benchmarking in the AI era. We revisit why benchmarking matters and its history in computational biology, examine emerging AI-specific challenges, and outline practical paths forward. We argue that benchmarks should be treated as long-lived community infrastructure rather than one-off artifacts bundled with individual papers. We envision a future of critical assessment i.e. dynamic and responsive to evolving use cases, sustaining progress for advancing science in the age of AI.Availability and implementation: N/A.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag290"},"PeriodicalIF":2.6,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13675102/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149217308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transcriptomic buffering detection: a computational approach to identifying essential gene dependencies in pancreatic cancer. 转录组缓冲检测：一种识别胰腺癌基本基因依赖性的计算方法。

IF 2.6

Bioinformatics advances Pub Date : 2026-10-07 eCollection Date: 2026-01-01 DOI: 10.1093/bioadv/vbag280

Magnus Boman, Eleni Afentaki, Julia Marie Greely, Noura Ezaz-Nikpay, Rula Zain, C I Edvard Smith

{"title":"Transcriptomic buffering detection: a computational approach to identifying essential gene dependencies in pancreatic cancer.","authors":"Magnus Boman, Eleni Afentaki, Julia Marie Greely, Noura Ezaz-Nikpay, Rula Zain, C I Edvard Smith","doi":"10.1093/bioadv/vbag280","DOIUrl":"10.1093/bioadv/vbag280","url":null,"abstract":"Motivation: Loss-of-function (LOF) mutations in essential genes (Goners) should be lethal, yet some tumours survive through compensatory buffering mechanisms. Existing computational methods for identifying synthetic lethal (SL) interactions focus on known tumour suppressor losses, missing essential gene dependencies. We developed a computational pipeline to identify SL buffering mechanisms by correlating Goner LOF burden with transcriptomic compensation.Results: Applying our pipeline to pancreatic adenocarcinoma (PAAD), we integrated CRISPR essentiality screens (DepMap), mutation data, and RNA expression profiles from 165 TCGA-PAAD cases. This PAAD analysis supports the feasibility of the pipeline but does not nominate a transcriptomic buffer; applicability to other cancer types remains to be tested.Availability and implementation: Analysis code and documentation are available at https://github.com/d3dd/goblet. Implemented in Python 3.9+ using pandas, scipy, and gseapy. All data are publicly available from TCGA and DepMap.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag280"},"PeriodicalIF":2.6,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13644534/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149139174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

dbcan-nf: a Nextflow pipeline for accurate CAZyme annotation in metagenomes. dbcan-nf: Nextflow在宏基因组中准确标注CAZyme的管道。

IF 2.6

Bioinformatics advances Pub Date : 2026-10-07 eCollection Date: 2026-01-01 DOI: 10.1093/bioadv/vbag304

Xinpeng Zhang, Haidong Yi, Jinfang Zheng, Yanbin Yin

{"title":"dbcan-nf: a Nextflow pipeline for accurate CAZyme annotation in metagenomes.","authors":"Xinpeng Zhang, Haidong Yi, Jinfang Zheng, Yanbin Yin","doi":"10.1093/bioadv/vbag304","DOIUrl":"https://doi.org/10.1093/bioadv/vbag304","url":null,"abstract":"Motivation: Carbohydrate-active enzymes (CAZymes) are crucial for carbohydrate degradation in any ecological environment. However, CAZyme annotation is not easy in whole-genome shotgun (WGS) metagenomes, due to the lack of accurate, scalable, robust, and reproducible workflows. We previously published dbCAN as a web server and associated standalone tool run_dbcan that performs CAZyme annotation with assembled genomes as the input but not with raw WGS reads.Results: Here, we present dbcan-nf, an accurate and scalable Nextflow pipeline integrating run_dbcan as a component step, for automated CAZyme annotation in metagenomes. dbcan-nf supports multiple analytical routes, including individual sample assembly (IA), multisample coassembly (CA), assembly-free (AF), and subsampling (SS), and accepts both short and long reads. Using five benchmark datasets, we showed that dbcan-nf (IA and AF routes) had a much higher accuracy (recall and F1 score) and detected more CAZyme families than the competing tool Cayman. Among the different routes, dbcan-nf-IA produced the most accurate CAZyme annotation, especially when the sequencing depth is sufficient (e.g., >1.0 Gb). However, as a read assembly-based method, dbcan-nf-IA had a higher computational cost than Cayman. Finally, dbcan-nf leverages Nextflow-supported high-performance computing execution for workflow-level scalability through concurrent processing of multiple samples.Availability and implementation: https://github.com/bcb-unl/dbcan-nf.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag304"},"PeriodicalIF":2.6,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13679938/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149229498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TSLC: differential abundance analysis method for identifying biologically relevant known microbes. 鉴别生物学上相关的已知微生物的差异丰度分析方法。

IF 2.6

Bioinformatics advances Pub Date : 2026-10-06 eCollection Date: 2026-01-01 DOI: 10.1093/bioadv/vbag297

Robert Beblavy, Cynthia L Monaco, Cassandra E Newkirk, Jin Xiao, Michael B Sohn

{"title":"TSLC: differential abundance analysis method for identifying biologically relevant known microbes.","authors":"Robert Beblavy, Cynthia L Monaco, Cassandra E Newkirk, Jin Xiao, Michael B Sohn","doi":"10.1093/bioadv/vbag297","DOIUrl":"10.1093/bioadv/vbag297","url":null,"abstract":"Motivation: Identifying biologically relevant microbes is a crucial task in human microbiome studies. Numerous computational and statistical methods have been proposed for this task. However, most existing methods are based solely on taxonomic information, even when whole-metagenome sequencing data are available, and thus cannot account for the fact that different microbes can perform the same or similar functions (known as functional redundancy) and for the contribution of unknown microbes to functional abundance. Hence, the identified differentially abundant microbes might not be biologically relevant to manifested phenotypes.Results: To address the issues with existing methods, we propose a two-stage procedure: (i) determining differentially abundant functions between groups; and then (ii) determining microbes significantly associated with each identified differentially abundant function. This procedure involves <math> <mrow> <mrow> <mrow> <mrow> <msub><mrow><mi>ℓ</mi></mrow> <mn>1</mn></msub> </mrow> </mrow> </mrow> </mrow> </math> -penalized log-contrast models and debiasing procedures to account for the compositional nature of microbiome data and the bias induced by the <math> <mrow> <mrow> <mrow> <mrow> <msub><mrow><mi>ℓ</mi></mrow> <mn>1</mn></msub> </mrow> </mrow> </mrow> </mrow> </math> penalty. We performed extensive simulation studies to illustrate the consequences of ignoring functional redundancy and the contribution of unknown microbes, and to assess the performance of the proposed method. We applied the proposed method to two whole-metagenome sequencing datasets and showcased substantial differences between the typical taxonomic-based analysis and the proposed method.Availability and implementation: An R package for the proposed method is available at https://github.com/rgbeblavy/TSLC.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag297"},"PeriodicalIF":2.6,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13674353/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149217358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Decoding channel pore dynamics mechanism in Anopheles gambiae Orco homotetramer. 冈比亚按蚊Orco同型四聚体通道孔隙动力学机制解码。

IF 2.6

Bioinformatics advances Pub Date : 2026-10-06 eCollection Date: 2026-01-01 DOI: 10.1093/bioadv/vbag295

Vaanathi Chidambara Thanu, Amara Jabeen, Georgia Kythreoti, Kostas Iatrou, Spyros E Zographos, Philip W Taylor, Shoba Ranganathan

{"title":"Decoding channel pore dynamics mechanism in Anopheles gambiae Orco homotetramer.","authors":"Vaanathi Chidambara Thanu, Amara Jabeen, Georgia Kythreoti, Kostas Iatrou, Spyros E Zographos, Philip W Taylor, Shoba Ranganathan","doi":"10.1093/bioadv/vbag295","DOIUrl":"https://doi.org/10.1093/bioadv/vbag295","url":null,"abstract":"Motivation: Insect odorant receptors (iORs) essential for olfaction function as 7-transmembrane-domain (7TM) ion channels. iORs form tetrameric complexes that include a highly conserved odorant receptor co-receptor (Orco). Functional Orco homotetramers have also known to form ex vivo. Blocking Orco ex vivo and in vivo disrupts olfactory signalling, making it a promising target for functional intervention. Understanding its gating behaviour is critical for developing strategies to disrupt odour-guided behaviours in disease-vector insects. Thus, insights into the dynamic gating of the Anopheles gambiae Orco have implications on the rational design of vector-control strategies against malaria. We studied molecular dynamics (MD) simulations on homotetrameric AgamOrco models generated using template-based modelling (TBM) and AlphaFold3 (AF3), to investigate ligand-dependent pore dynamics. Agonist and antagonist bound systems were analysed to understand the ligand-dependent gating.Results: Our results show that apo and antagonistic IPC-bound AgamOrco homotetramer models remain in a stable, closed, and non-conductive state across both models. TBM, but not AF3, successfully captures agonistic VUAA1-induced activation, consistent with experimental observations, exhibiting asymmetric pore widening and conformational changes. Antagonist displacement simulations confirm IPC-mediated pore closure. Overall, the TBM AgamOrco model provides a reliable framework for structure-guided discovery of novel agents for control of insect pests and vector diseases.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag295"},"PeriodicalIF":2.6,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13679930/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149229509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

BandQuant: a web application for western blot quantification. BandQuant: western blot定量的web应用程序。

IF 2.6

Bioinformatics advances Pub Date : 2026-10-05 eCollection Date: 2026-01-01 DOI: 10.1093/bioadv/vbag292

Muyang He, Shanglin Jin, Hongjian Wan, Chengfeng Zhang

{"title":"BandQuant: a web application for western blot quantification.","authors":"Muyang He, Shanglin Jin, Hongjian Wan, Chengfeng Zhang","doi":"10.1093/bioadv/vbag292","DOIUrl":"https://doi.org/10.1093/bioadv/vbag292","url":null,"abstract":"Motivation: Western blot quantification typically requires multiple tools and manual data handling, which is time-consuming and difficult to audit. Existing software is either tied to proprietary hardware or requires local installation, and none provides an integrated path from raw image to fold-change.Results: We present BandQuant, a free, browser-based web application that integrates the entire western blot quantification workflow into a single session. It combines automated band detection, rolling-ball background subtraction, housekeeping normalization, fold-change computation, and interactive bar-chart visualization in a single interface. A relational database binds experimental metadata to the image at upload, enabling automated normalization and cross-experiment queries without manual relabeling. BandQuant measurements showed high agreement with Fiji (Pearson r = 0.994), and analysis time was reduced by 65%.Availability and implementation: BandQuant is freely available at https://www.tiaodaibao.com/? locale=en-US. Source code: https://github.com/wanhj-git/BandQuant. Archived release: https://zenodo.org/records/19453998.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag292"},"PeriodicalIF":2.6,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13661006/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149186600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PerSeqPIPE: Nextflow workflow for per-sequence analysis of small RNA-sequencing data. PerSeqPIPE：用于小rna测序数据逐序列分析的Nextflow工作流。

IF 2.6

Bioinformatics advances Pub Date : 2026-10-05 eCollection Date: 2026-01-01 DOI: 10.1093/bioadv/vbag294

Karolína Trachtová, Dagmar Al Tukmachi, Vojtěch Bystrý, Ondřej Slabý

{"title":"PerSeqPIPE: Nextflow workflow for per-sequence analysis of small RNA-sequencing data.","authors":"Karolína Trachtová, Dagmar Al Tukmachi, Vojtěch Bystrý, Ondřej Slabý","doi":"10.1093/bioadv/vbag294","DOIUrl":"10.1093/bioadv/vbag294","url":null,"abstract":"Summary: Small RNA sequencing enables high-throughput profiling of diverse small non-coding RNAs, but their complex biology creates substantial bioinformatic challenges. Conventional gene-centric pipelines often discard multi-mapping reads or impose arbitrary feature assignments, leading to biased quantification results. We present PerSeqPIPE, a modular, fully containerized Nextflow pipeline implementing a sequence-centric approach that quantifies each unique read sequence and annotates it with all compatible genomic and transcriptomic features. This preserves biologically relevant signals and enables broad small non-coding RNA profiling. PerSeqPIPE integrates harmonized reference resources for multiple small non-coding RNA classes and supports reproducible execution using Docker and Apptainer. The accompanying Streamlit-based PerSeqPIPE VIZ application enables interactive exploration, filtering, visualization, and differential expression analysis. Application to a published extracellular vesicle dataset revealed isomiR diversity and multi-mapped sncRNAs that were obscured by conventional approaches. Together, PerSeqPIPE and PerSeqPIPE VIZ provide an open, scalable, and transparent framework for small RNA-seq analysis.Availability and implementation: PerSeqPIPE is freely available at https://github.com/ktrachtova/perseqpipe and PerSeqPIPE VIZ at https://github.com/ktrachtova/perseqpipe_viz. PerSeqPIPE is implemented in Nextflow, Python, Bash, and R; PerSeqPIPE VIZ is a Streamlit app. Both are compatible with macOS and Linux operating systems.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag294"},"PeriodicalIF":2.6,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13676317/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149217444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancements in forecasting liver cytosol metabolic stability: a comprehensive update on machine learning predictive models. 预测肝细胞质代谢稳定性的进展：机器学习预测模型的全面更新。

IF 2.6

Bioinformatics advances Pub Date : 2026-09-30 eCollection Date: 2026-01-01 DOI: 10.1093/bioadv/vbag291

Gyutae Lim, Sankalp Jain, Xin Xu, Pranav Shah

{"title":"Advancements in forecasting liver cytosol metabolic stability: a comprehensive update on machine learning predictive models.","authors":"Gyutae Lim, Sankalp Jain, Xin Xu, Pranav Shah","doi":"10.1093/bioadv/vbag291","DOIUrl":"10.1093/bioadv/vbag291","url":null,"abstract":"Motivation: Drug metabolism plays a key role in determining a compound's pharmacokinetics and overall success in development. While cytochrome P450 enzymes have been widely studied, non-cytochrome P450 enzymes, such as aldehyde oxidase, xanthine oxidase, and carboxylesterases, are increasingly recognized as important contributors to drug clearance. Since standard liver microsomal screening fails to include these enzymes, stability assessment using liver cytosols is essential to prevent late-stage development failures.Results: We expanded our dataset to over 7000 cytosol metabolic stability measurements for human, mouse, and rat. We developed and evaluated machine learning models using molecular fingerprints and descriptors, with performance assessed on scaffold-based external test sets. Class imbalance was addressed using stratified bagging which improved identification of minority class compounds. The final models achieved an external balanced accuracy (BACC) of 0.812 for human and 0.658 for mouse. The rat model, built from a dataset with severe minority class scarcity, reached a BACC of 0.724 and the highest area under the curve (0.886). This work improves the BACC of our previous human model by 29.7% and introduces the first publicly available models for mouse and rat.Availability and implementation: These in silico models are freely accessible through the ADME@NCATS platform for early-stage assessment of species-specific cytosol metabolic stability.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag291"},"PeriodicalIF":2.6,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13665460/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149198152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Correction: Bioinformatics in Español: expanding open, diverse, and collaborative science through SEH2Bioinfo. 更正：Español中的生物信息学：通过SEH2Bioinfo扩展开放、多样化和协作的科学。

IF 2.6

Bioinformatics advances Pub Date : 2026-09-28 eCollection Date: 2026-01-01 DOI: 10.1093/bioadv/vbag271

引用次数: 0

NeighborFinder: an R package inferring local microbial network around a species of interest. NeighborFinder：一个R包，推断一个感兴趣物种周围的本地微生物网络。

IF 2.6

Bioinformatics advances Pub Date : 2026-09-28 eCollection Date: 2026-01-01 DOI: 10.1093/bioadv/vbag201

Mathilde Sola, Adrien Paravel, Sandrine Auger, Jean-Marc Chatel, Florian Plaza Oñate, Emmanuelle Le Chatelier, Marion Leclerc, Patrick Veiga, Clémence Frioux, Mahendra Mariadassou, Magali Berland

{"title":"NeighborFinder: an R package inferring local microbial network around a species of interest.","authors":"Mathilde Sola, Adrien Paravel, Sandrine Auger, Jean-Marc Chatel, Florian Plaza Oñate, Emmanuelle Le Chatelier, Marion Leclerc, Patrick Veiga, Clémence Frioux, Mahendra Mariadassou, Magali Berland","doi":"10.1093/bioadv/vbag201","DOIUrl":"https://doi.org/10.1093/bioadv/vbag201","url":null,"abstract":"Motivation: Understanding interactions from microbiome data is a central aspect in microbial ecology, as it provides insights into ecosystem stability, disease mechanisms, and can be used to design synthetic communities. Current network inference tools reconstruct global networks from co-abundance data, which means they capture the overall correlation structure for the entire set of taxa considered. These approaches are computationally intensive and suboptimal when the focus is on the local neighborhood of one or a few taxa of interest.Results: We introduce NeighborFinder, a local network inference method that enables the targeted discovery of direct neighbors around a species of interest. Using cross-validated multiple linear regression with <math> <mrow> <mrow> <msub><mrow><mi>ℓ</mi></mrow> <mn>1</mn></msub> </mrow> </mrow> </math> penalty and microbiome-specific filters, our approach infers interpretable species-centered interactions, with F1 score <math><mo>≥</mo></math> 0.95 on simulated cohorts ranging from 250 to 1000 samples. This method is well-suited for large metagenomic datasets and is particularly valuable for exploratory studies where the targeted hypotheses outweigh the need for global community structure. The approach complements existing methods by being biologically intuitive and computationally efficient.Availability and implementation: The R package is available on CRAN https://CRAN.R-project.org/package=NeighborFinder. The data and source code used to calculate performances and produce the use case example can be found respectively at: https://doi.org/10.57745/UPITJ0 and https://doi.org/10.57745/HJLWW4.Supplementary information: Supplementary data are available at Bioinformatics Advances online.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag201"},"PeriodicalIF":2.6,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13626711/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149119246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0