AI-driven multi-omics integration for multi-scale predictive modeling of genotype-environment-phenotype relationships

Computational and Structural Biotechnology Journal, Journal Year: 2025, Volume and Issue: 27, P. 265 - 277

Published: Jan. 1, 2025

Despite the wealth of single-cell multi-omics data, it remains challenging to predict consequences novel genetic and chemical perturbations in human body. It requires knowledge molecular interactions at all biological levels, encompassing disease models humans. Current machine learning methods primarily establish statistical correlations between genotypes phenotypes but struggle identify physiologically significant causal factors, limiting their predictive power. Key challenges modeling include scarcity labeled generalization across different domains, disentangling causation from correlation. In light recent advances data integration, we propose a new artificial intelligence (AI)-powered biology-inspired multi-scale framework tackle these issues. This will integrate organism hierarchies, species genotype-environment-phenotype relationships under various conditions. AI inspired by biology may targets, biomarkers, pharmaceutical agents, personalized medicines for presently unmet medical needs.

Language: Английский

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High-order Topology for Deep Single-cell Multi-view Fuzzy Clustering

IEEE Transactions on Fuzzy Systems, Journal Year: 2024, Volume and Issue: 32(8), P. 4448 - 4459

Published: May 13, 2024

Single-cell multi-view clustering is essential for analyzing the different cell subtypes of same from views. Some attempts have been made, but most these models still struggle to handle single-cell sequencing data, primarily due their non-specific design cellular data. We observe that such data distinctively exhibits: (1) a profusion high-order topological correlations, (2) disparate distribution information across views, and (3) inherent fuzzy characteristics, indicating cell's potential associate with multiple cluster identities. Neglecting key patterns could significantly impair medical clustering. In response, we propose specialized application namely deep Multi-view Fuzzy Clustering (scMFC) method. Concretely, employ random walk technique capture relationships on graph developed cross-view aggregation mechanism adaptively assigns weights Furthermore, accurately reflect dynamic insight in development, strategy allows cells diverse clusters. Extensive experiments conducted three real-world datasets demonstrate our method's superior performance.

Language: Английский

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Integrating Molecular Perspectives: Strategies for Comprehensive Multi-Omics Integrative Data Analysis and Machine Learning Applications in Transcriptomics, Proteomics, and Metabolomics

Biology, Journal Year: 2024, Volume and Issue: 13(11), P. 848 - 848

Published: Oct. 22, 2024

With the advent of high-throughput technologies, field omics has made significant strides in characterizing biological systems at various levels complexity. Transcriptomics, proteomics, and metabolomics are three most widely used each providing unique insights into different layers a system. However, analyzing data set separately may not provide comprehensive understanding subject under study. Therefore, integrating multi-omics become increasingly important bioinformatics research. In this article, we review strategies for transcriptomics, data, including co-expression analysis, metabolite-gene networks, constraint-based models, pathway enrichment interactome analysis. We discuss combined integration approaches, correlation-based strategies, machine learning techniques that utilize one or more types data. By presenting these methods, aim to researchers with better how integrate gain view system, facilitating identification complex patterns interactions might be missed by single-omics analyses.

Language: Английский

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Developmental Programming of the Fetal Immune System by Maternal Western-Style Diet: Mechanisms and Implications for Disease Pathways in the Offspring

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(11), P. 5951 - 5951

Published: May 29, 2024

Maternal obesity and over/undernutrition can have a long-lasting impact on offspring health during critical periods in the first 1000 days of life. Children born to mothers with reduced immune responses stimuli which increase susceptibility infections. Recently, maternal western-style diets (WSDs), high fat simple sugars, been associated skewing neonatal cell development, recent evidence suggests that dysregulation innate immunity early life has long-term consequences metabolic diseases behavioral disorders later Several factors contribute abnormal tolerance or trained immunity, including changes gut microbiota, metabolites, epigenetic modifications. Critical knowledge gaps remain regarding mechanisms whereby these fetal postnatal especially precursor stem cells bone marrow liver. Components microbiota are transferred from consuming WSD their understudied identifying cause effect adaptive development needs be refined. Tools single-cell RNA-sequencing, analysis, spatial location specific liver for understanding system programming. Considering vital role function plays health, it will important understand how control developmental programming immunity.

Language: Английский

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Understanding testicular single cell transcriptional atlas: from developmental complications to male infertility

Frontiers in Endocrinology, Journal Year: 2024, Volume and Issue: 15

Published: July 11, 2024

Spermatogenesis is a multi-step biological process where mitotically active diploid (2n) spermatogonia differentiate into haploid (n) spermatozoa via regulated meiotic programming. The alarming rise in male infertility has become global concern during the past decade thereby demanding an extensive profiling of testicular gene expression. Advancements Next-Generation Sequencing (NGS) technologies have revolutionized our empathy towards complex events including spermatogenesis. However, despite multiple attempts made to reveal transcriptional signature(s) either with bulk tissues or at single-cell, level, comprehensive reviews on transcriptomics and associated disorders are limited. Notably, explicating genome-wide expression patterns various stages spermatogenic progression provide dynamic molecular landscape transcription. Our review discusses advantages single-cell RNA-sequencing (Sc-RNA-seq) over RNA-seq concerning tissues. Additionally, we highlight cellular heterogeneity, spatial transcriptomics, cell-to-cell interactions distinct cell populations within testes germ cells (Gc), Sertoli (Sc), Peritubular (PTc), Leydig (Lc), etc. Furthermore, summary key finding transcriptomic studies that shed light developmental mechanisms implicated infertility. These insights emphasize pivotal roles Sc-RNA-seq advancing knowledge regarding may serve as potential resource formulate future clinical interventions for reproductive health.

Language: Английский

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Influence of intersignaling crosstalk on the intracellular localization of YAP/TAZ in lung cells

Cell Communication and Signaling, Journal Year: 2024, Volume and Issue: 22(1)

Published: May 27, 2024

Abstract A cell is a dynamic system in which various processes occur simultaneously. In particular, intra- and intercellular signaling pathway crosstalk has significant impact on cell’s life cycle, differentiation, proliferation, growth, regeneration, and, consequently, the normal functioning of an entire organ. Hippo YAP/TAZ nucleocytoplasmic shuttling play pivotal role development, homeostasis, tissue particularly lung cells. Intersignaling communication core components localization. This review describes between key pathways (WNT, SHH, TGFβ, Notch, Rho, mTOR) using cells as example highlights remaining unanswered questions.

Language: Английский

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SIMS: A deep-learning label transfer tool for single-cell RNA sequencing analysis

Cell Genomics, Journal Year: 2024, Volume and Issue: 4(6), P. 100581 - 100581

Published: May 31, 2024

Cell atlases serve as vital references for automating cell labeling in new samples, yet existing classification algorithms struggle with accuracy. Here we introduce SIMS (scalable, interpretable machine learning single cell), a low-code data-efficient pipeline single-cell RNA classification. We benchmark against datasets from different tissues and species. demonstrate SIMS's efficacy classifying cells the brain, achieving high accuracy even small training sets (<3,500 cells) across samples. accurately predicts neuronal subtypes developing shedding light on genetic changes during differentiation postmitotic fate refinement. Finally, apply to of cortical organoids predict identities uncover variations between lines. identifies cell-line differences misannotated lineages human derived pluripotent stem Altogether, show that is versatile robust tool cell-type datasets.

Language: Английский

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scAnnoX: an R package integrating multiple public tools for single-cell annotation

PeerJ, Journal Year: 2024, Volume and Issue: 12, P. e17184 - e17184

Published: March 28, 2024

Single-cell annotation plays a crucial role in the analysis of single-cell genomics data. Despite existence numerous algorithms, comprehensive tool for integrating and comparing these algorithms is also lacking.

Language: Английский

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Comprehensive review on single-cell RNA sequencing: A new frontier in Alzheimer's disease research

Ageing Research Reviews, Journal Year: 2024, Volume and Issue: 100, P. 102454 - 102454

Published: Aug. 12, 2024

Language: Английский

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Application of bioinformatic tools in cell type classification for single-cell RNA-seq data

Computational Biology and Chemistry, Journal Year: 2025, Volume and Issue: 115, P. 108332 - 108332

Published: Jan. 5, 2025

Language: Английский

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