BootCellNet, a resampling-based procedure, promotes unsupervised identification of cell populations via robust inference of gene regulatory networks DOI Creative Commons
Yutaro Kumagai

PLoS Computational Biology, Год журнала: 2024, Номер 20(9), С. e1012480 - e1012480

Опубликована: Сен. 30, 2024

Recent advances in measurement technologies, particularly single-cell RNA sequencing (scRNA-seq), have revolutionized our ability to acquire large amounts of omics-level data on cellular states. As techniques evolve, there has been an increasing need for analysis methodologies, especially those focused cell-type identification and inference gene regulatory networks (GRNs). We developed a new method named BootCellNet, which employs smoothing resampling infer GRNs. Using the inferred GRNs, BootCellNet further infers minimum dominating set (MDS), genes that determines dynamics entire network. demonstrated robustly GRNs their MDSs from scRNA-seq facilitates unsupervised cell clusters using datasets peripheral blood mononuclear cells hematopoiesis. It also identified COVID-19 patient-specific potential transcription factors. not only identifies types explainable way but provides insights into characteristics through MDS.

Язык: Английский

Emerging methods for genome-scale metabolic modeling of microbial communities DOI Creative Commons
Chaimaa Tarzi, Guido Zampieri, Neil T. Sullivan

и другие.

Trends in Endocrinology and Metabolism, Год журнала: 2024, Номер 35(6), С. 533 - 548

Опубликована: Апрель 3, 2024

Genome-scale metabolic models (GEMs) are consolidating as platforms for studying mixed microbial populations, by combining biological data and knowledge with mathematical rigor. However, deploying these to answer research questions can be challenging due the increasing number of available computational tools, lack universal standards, their inherent limitations. Here, we present a comprehensive overview foundational concepts building evaluating genome-scale communities. We then compare tools in terms requirements, capabilities, applications. Next, highlight current pitfalls open challenges consider when adopting existing developing new ones. Our compendium relevant expanding community modelers, both at entry experienced levels.

Язык: Английский

Процитировано

12
A mini-review on perturbation modelling across single-cell omic modalities DOI Creative Commons
George Gavriilidis, Vasileios Vasileiou, Aspasia Orfanou

и другие.

Computational and Structural Biotechnology Journal, Год журнала: 2024, Номер 23, С. 1886 - 1896

Опубликована: Апрель 25, 2024

Язык: Английский

Процитировано

11
Cross-attention enables deep learning on limited omics-imaging-clinical data of 130 lung cancer patients DOI Creative Commons
Suraj Verma, Giuseppe Magazzù,

Noushin Eftekhari

и другие.

Cell Reports Methods, Год журнала: 2024, Номер 4(7), С. 100817 - 100817

Опубликована: Июль 1, 2024

Deep-learning tools that extract prognostic factors derived from multi-omics data have recently contributed to individualized predictions of survival outcomes. However, the limited size integrated omics-imaging-clinical datasets poses challenges. Here, we propose two biologically interpretable and robust deep-learning architectures for prediction non-small cell lung cancer (NSCLC) patients, learning simultaneously computed tomography (CT) scan images, gene expression data, clinical information. The proposed models integrate patient-specific clinical, transcriptomic, imaging incorporate Kyoto Encyclopedia Genes Genomes (KEGG) Reactome pathway information, adding biological knowledge within process biomarkers molecular pathways. While both accurately stratify patients in high- low-risk groups when trained on a dataset only 130 introducing cross-attention mechanism sparse autoencoder significantly improves performance, highlighting tumor regions NSCLC-related genes as potential thus offering significant methodological advancement small imaging-omics-clinical samples.

Язык: Английский

Процитировано

2
Enhancing deep learning for demand forecasting to address large data gaps DOI Creative Commons
Chirine Riachy, Mengda He, Sina Joneidy

и другие.

Expert Systems with Applications, Год журнала: 2024, Номер unknown, С. 126200 - 126200

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

1
BootCellNet, a resampling-based procedure, promotes unsupervised identification of cell populations via robust inference of gene regulatory networks DOI Creative Commons
Yutaro Kumagai

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Фев. 8, 2024

Abstract Recent advances in measurement technologies, particularly single-cell RNA sequencing (scRNA-seq), have revolutionized our ability to acquire large amounts of omics-level data on cellular states. As techniques evolve, there has been an increasing need for analysis methodologies, especially those focused cell-type identification and inference gene regulatory networks (GRNs). We developed a new method named BootCellNet, which employs smoothing resampling infer GRNs. Using the inferred GRNs, BootCellNet further infers minimum dominating set (MDS), genes that determines dynamics entire network. demonstrated robustly GRNs their MDSs from scRNA-seq facilitates unsupervised cell clusters using datasets peripheral blood mononuclear cells hematopoiesis. It also identified COVID-19 patient-specific potential transcription factors. not only identifies types explainable way but provides insights into characteristics through MDS. Author Summary Single-cell omics such as RNA-seq are instrumental identifying novel subsets involved various biological processes diseases. These however, require development analysis, areas interactions between genes. The problem essentially involves clustering, necessitates balance distinguishing different states grouping similar ones together. Current clustering methods still suffer uncertainty determining appropriate number explaining why some clustered together others separated. genes, network (GRN), remains challenging due noisy nature scRNA-seq. utilizes cluster identify types. addresses challenges GRN simultaneously will facilitate generation working hypotheses amount data.

Язык: Английский

Процитировано

0
An Exploration on Explainable AI with Background and Motivation for XAI DOI

B. P. Sheela,

H Girisha

Algorithms for intelligent systems, Год журнала: 2024, Номер unknown, С. 481 - 489

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

0
BootCellNet, a resampling-based procedure, promotes unsupervised identification of cell populations via robust inference of gene regulatory networks DOI Creative Commons
Yutaro Kumagai

PLoS Computational Biology, Год журнала: 2024, Номер 20(9), С. e1012480 - e1012480

Опубликована: Сен. 30, 2024

Recent advances in measurement technologies, particularly single-cell RNA sequencing (scRNA-seq), have revolutionized our ability to acquire large amounts of omics-level data on cellular states. As techniques evolve, there has been an increasing need for analysis methodologies, especially those focused cell-type identification and inference gene regulatory networks (GRNs). We developed a new method named BootCellNet, which employs smoothing resampling infer GRNs. Using the inferred GRNs, BootCellNet further infers minimum dominating set (MDS), genes that determines dynamics entire network. demonstrated robustly GRNs their MDSs from scRNA-seq facilitates unsupervised cell clusters using datasets peripheral blood mononuclear cells hematopoiesis. It also identified COVID-19 patient-specific potential transcription factors. not only identifies types explainable way but provides insights into characteristics through MDS.

Язык: Английский

Процитировано

0