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Journal articles on the topic 'Multi-omics pathway analysis'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Jeon, Jaemin, Eon Yong Han, and Inuk Jung. "MOPA: An integrative multi-omics pathway analysis method for measuring omics activity." PLOS ONE 18, no. 3 (2023): e0278272. http://dx.doi.org/10.1371/journal.pone.0278272.

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Pathways are composed of proteins forming a network to represent specific biological mechanisms and are often used to measure enrichment scores based on a list of genes in means to measure their biological activity. The pathway analysis is a de facto standard downstream analysis procedure in most genomic and transcriptomic studies. Here, we present MOPA (Multi-Omics Pathway Analysis), which is a multi-omics integrative method that scores individual pathways in a sample wise manner in terms of enriched multi-omics regulatory activity, which we refer to mES (multi-omics Enrichment Score). The mE

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Griss, Johannes, Guilherme Viteri, Konstantinos Sidiropoulos, Vy Nguyen, Antonio Fabregat, and Henning Hermjakob. "ReactomeGSA - Efficient Multi-Omics Comparative Pathway Analysis." Molecular & Cellular Proteomics 19, no. 12 (2020): 2115–24. http://dx.doi.org/10.1074/mcp.tir120.002155.

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Pathway analyses are key methods to analyze 'omics experiments. Nevertheless, integrating data from different 'omics technologies and different species still requires considerable bioinformatics knowledge.Here we present the novel ReactomeGSA resource for comparative pathway analyses of multi-omics datasets. ReactomeGSA can be used through Reactome's existing web interface and the novel ReactomeGSA R Bioconductor package with explicit support for scRNA-seq data. Data from different species is automatically mapped to a common pathway space. Public data from ExpressionAtlas and Single Cell Expre

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3

Wieder, Cecilia, Juliette Cooke, Clement Frainay, et al. "PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration." PLOS Computational Biology 20, no. 3 (2024): e1011814. http://dx.doi.org/10.1371/journal.pcbi.1011814.

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As terabytes of multi-omics data are being generated, there is an ever-increasing need for methods facilitating the integration and interpretation of such data. Current multi-omics integration methods typically output lists, clusters, or subnetworks of molecules related to an outcome. Even with expert domain knowledge, discerning the biological processes involved is a time-consuming activity. Here we propose PathIntegrate, a method for integrating multi-omics datasets based on pathways, designed to exploit knowledge of biological systems and thus provide interpretable models for such studies.

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Li, Feng, Tan Wu, Yanjun Xu, et al. "A comprehensive overview of oncogenic pathways in human cancer." Briefings in Bioinformatics 21, no. 3 (2019): 957–69. http://dx.doi.org/10.1093/bib/bbz046.

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Abstract Alterations of biological pathways can lead to oncogenesis. An overview of these oncogenic pathways would be highly valuable for researchers to reveal the pathogenic mechanism and develop novel therapeutic approaches for cancers. Here, we reviewed approximately 8500 literatures and documented experimentally validated cancer-pathway associations as benchmarking data set. This data resource includes 4709 manually curated relationships between 1557 paths and 49 cancers with 2427 upstream regulators in 7 species. Based on this resource, we first summarized the cancer-pathway associations

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Lan, Yiheng, Ruikun Sun, Jian Ouyang, et al. "AtMAD: Arabidopsis thaliana multi-omics association database." Nucleic Acids Research 49, no. D1 (2020): D1445—D1451. http://dx.doi.org/10.1093/nar/gkaa1042.

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Abstract Integration analysis of multi-omics data provides a comprehensive landscape for understanding biological systems and mechanisms. The abundance of high-quality multi-omics data (genomics, transcriptomics, methylomics and phenomics) for the model organism Arabidopsis thaliana enables scientists to study the genetic mechanism of many biological processes. However, no resource is available to provide comprehensive and systematic multi-omics associations for Arabidopsis. Here, we developed an Arabidopsis thaliana Multi-omics Association Database (AtMAD, http://www.megabionet.org/atmad), a

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Kim, Dokyoon, Ruowang Li, Anastasia Lucas, Shefali S. Verma, Scott M. Dudek, and Marylyn D. Ritchie. "Using knowledge-driven genomic interactions for multi-omics data analysis: metadimensional models for predicting clinical outcomes in ovarian carcinoma." Journal of the American Medical Informatics Association 24, no. 3 (2016): 577–87. http://dx.doi.org/10.1093/jamia/ocw165.

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It is common that cancer patients have different molecular signatures even though they have similar clinical features, such as histology, due to the heterogeneity of tumors. To overcome this variability, we previously developed a new approach incorporating prior biological knowledge that identifies knowledge-driven genomic interactions associated with outcomes of interest. However, no systematic approach has been proposed to identify interaction models between pathways based on multi-omics data. Here we have proposed such a novel methodological framework, called metadimensional knowledge-drive

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Sun, Pengcheng, Xing Liu, Yi Wang, et al. "Molecular characterization of allergic constitution based on network pharmacology and multi-omics analysis methods." Medicine 103, no. 7 (2024): e36892. http://dx.doi.org/10.1097/md.0000000000036892.

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The objective of this study was to identify critical pathways associated with allergic constitution. Shared genes among allergic rhinitis (AR), asthma (AA), and atopic dermatitis (AD) were extracted from the GWAS catalog. RNA-seq data of AR, AA, and AD from gene expression omnibus (GEO) database were preprocessed and subjected to differential gene expression analysis. The differentially expressed genes (DEGs) were merged using the Robust Rank Aggregation (RRA) algorithm. Weighted gene co-expression network analysis (WGCNA) was performed to identify modules associated with allergies. Components

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8

Wang, Yuhan. "High-Throughput Analysis of Core Pathways in HeLa Cells: Single-Cell Sequencing and AI-driven Modeling of Multi-Pathway Interaction Networks." Applied and Computational Engineering 178, no. 1 (2025): 47–57. https://doi.org/10.54254/2755-2721/2025.po25414.

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HeLa cells, the first successfully cultured human cancer cell line, are pivotal in cancer research, virology, and drug screening. However, their multi-omics heterogeneity and complex cancer-related cascades challenge traditional bulk sequencing, which fails to capture dynamic cell-cell interactions and resolve pathway crosstalk. This review systematically examines single-cell multi-omics technologies (transcriptomics, proteomics, and data integration) and AI-driven network modeling (graph neural networks, deep learning) for decoding HeLa cells' core pathways and metastasis mechanisms. It revea

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Lin, Dongdong, Hima B. Yalamanchili, Xinmin Zhang, et al. "CHOmics: A web-based tool for multi-omics data analysis and interactive visualization in CHO cell lines." PLOS Computational Biology 16, no. 12 (2020): e1008498. http://dx.doi.org/10.1371/journal.pcbi.1008498.

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Chinese hamster ovary (CHO) cell lines are widely used in industry for biological drug production. During cell culture development, considerable effort is invested to understand the factors that greatly impact cell growth, specific productivity and product qualities of the biotherapeutics. While high-throughput omics approaches have been increasingly utilized to reveal cellular mechanisms associated with cell line phenotypes and guide process optimization, comprehensive omics data analysis and management have been a challenge. Here we developed CHOmics, a web-based tool for integrative analysi

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Gallo Cantafio, Maria, Katia Grillone, Daniele Caracciolo, et al. "From Single Level Analysis to Multi-Omics Integrative Approaches: A Powerful Strategy towards the Precision Oncology." High-Throughput 7, no. 4 (2018): 33. http://dx.doi.org/10.3390/ht7040033.

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Integration of multi-omics data from different molecular levels with clinical data, as well as epidemiologic risk factors, represents an accurate and promising methodology to understand the complexity of biological systems of human diseases, including cancer. By the extensive use of novel technologic platforms, a large number of multidimensional data can be derived from analysis of health and disease systems. Comprehensive analysis of multi-omics data in an integrated framework, which includes cumulative effects in the context of biological pathways, is therefore eagerly awaited. This strategy

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Linder, Henry, and Yuping Zhang. "A pan-cancer integrative pathway analysis of multi-omics data." Quantitative Biology 8, no. 2 (2020): 130–42. http://dx.doi.org/10.1007/s40484-019-0185-6.

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Ghosh, Sreemoyee, Chiara Pastrello, Omar Correa, et al. "Identification of Psoriatic Arthritis-Related Pathways Using Multi-Omics Data Integration." Journal of Rheumatology 52, Suppl 2 (2025): 73–74. https://doi.org/10.3899/jrheum.2025-0314.60.

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ObjectivesThe objectives of this study include (i) identifying and curating publicly available omics studies on psoriatic disease (PsD) to build a multiomics data integration portal (PsDIP) and (ii) integrating studies from PsDIP comparing the serum omics profiles of psoriatic arthritis (PsA) and cutaneous psoriasis (PsC) patients to identify PsA-related pathways.MethodsA scoping review was conducted to curate all publicly available omics studies in the field of PsD from 3 databases: Ovid MEDLINE, Embase and Cochrane Central. Inclusion criteria comprise all English-language studies related to

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Heylen, Dries, Jannes Peeters, Jan Aerts, Gökhan Ertaylan, and Jef Hooyberghs. "BioMOBS: A multi-omics visual analytics workflow for biomolecular insight generation." PLOS ONE 18, no. 12 (2023): e0295361. http://dx.doi.org/10.1371/journal.pone.0295361.

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One of the challenges in multi-omics data analysis for precision medicine is the efficient exploration of undiscovered molecular interactions in disease processes. We present BioMOBS, a workflow consisting of two data visualization tools integrated with an open-source molecular information database to perform clinically relevant analyses (https://github.com/driesheylen123/BioMOBS). We performed exploratory pathway analysis with BioMOBS and demonstrate its ability to generate relevant molecular hypotheses, by reproducing recent findings in type 2 diabetes UK biobank data. The central visualisat

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François, Maxime, Avinash V. Karpe, Jian-Wei Liu, et al. "Multi-Omics, an Integrated Approach to Identify Novel Blood Biomarkers of Alzheimer’s Disease." Metabolites 12, no. 10 (2022): 949. http://dx.doi.org/10.3390/metabo12100949.

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The metabolomic and proteomic basis of mild cognitive impairment (MCI) and Alzheimer’s disease (AD) is poorly understood, and the relationships between systemic abnormalities in metabolism and AD/MCI pathogenesis is unclear. This study compared the metabolomic and proteomic signature of plasma from cognitively normal (CN) and dementia patients diagnosed with MCI or AD, to identify specific cellular pathways and new biomarkers altered with the progression of the disease. We analysed 80 plasma samples from individuals with MCI or AD, as well as age- and gender-matched CN individuals, by utilisin

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Li, Zhicen, Yifan Wei, Yanqiu Shao, Lei Tang, and Jian Gong. "Multi-omics analysis of intertumoral heterogeneity within medulloblastoma uncharted-pathway subtypes." Brain Tumor Pathology 38, no. 3 (2021): 234–42. http://dx.doi.org/10.1007/s10014-021-00400-7.

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Shi, W. Jenny, Yonghua Zhuang, Pamela H. Russell, et al. "Unsupervised discovery of phenotype-specific multi-omics networks." Bioinformatics 35, no. 21 (2019): 4336–43. http://dx.doi.org/10.1093/bioinformatics/btz226.

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Abstract Motivation Complex diseases often involve a wide spectrum of phenotypic traits. Better understanding of the biological mechanisms relevant to each trait promotes understanding of the etiology of the disease and the potential for targeted and effective treatment plans. There have been many efforts towards omics data integration and network reconstruction, but limited work has examined the incorporation of relevant (quantitative) phenotypic traits. Results We propose a novel technique, sparse multiple canonical correlation network analysis (SmCCNet), for integrating multiple omics data

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Sigaud, Romain, Anja Stefanski, Florian Selt, et al. "LGG-14. INTEGRATED MULTI-OMICS EXPLORATION OF MAPK PATHWAY INHIBITION IN PEDIATRIC PILOCYTIC ASTROCYTOMA." Neuro-Oncology 26, Supplement_4 (2024): 0. http://dx.doi.org/10.1093/neuonc/noae064.407.

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Abstract Pilocytic astrocytomas (PA), the most common pediatric brain tumors, exhibit MAPK pathway alterations leading to its constitutive activation. They are also associated with low proliferation index due to the oncogene-induced senescence (OIS), sustained by senescence-associated secretory phenotype (SASP) factors. Little is known about the downstream consequences of MAPK activation leading to OIS-SASP, and of the molecular implications of MAPK pathway inhibition in senescent PA cells. Senescent DKFZ-BT66 cells derived from a primary KIAA::BRAF-fusion positive PA were used to generate RNA

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18

Lin, Bridget M., Hunyong Cho, Chuwen Liu, et al. "BZINB Model-Based Pathway Analysis and Module Identification Facilitates Integration of Microbiome and Metabolome Data." Microorganisms 11, no. 3 (2023): 766. http://dx.doi.org/10.3390/microorganisms11030766.

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Integration of multi-omics data is a challenging but necessary step to advance our understanding of the biology underlying human health and disease processes. To date, investigations seeking to integrate multi-omics (e.g., microbiome and metabolome) employ simple correlation-based network analyses; however, these methods are not always well-suited for microbiome analyses because they do not accommodate the excess zeros typically present in these data. In this paper, we introduce a bivariate zero-inflated negative binomial (BZINB) model-based network and module analysis method that addresses th

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19

Elbashir, Murtada K., Abdullah Almotilag, Mahmood A. Mahmood, and Mohanad Mohammed. "Enhancing Non-Small Cell Lung Cancer Survival Prediction through Multi-Omics Integration Using Graph Attention Network." Diagnostics 14, no. 19 (2024): 2178. http://dx.doi.org/10.3390/diagnostics14192178.

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Background: Cancer survival prediction is vital in improving patients’ prospects and recommending therapies. Understanding the molecular behavior of cancer can be enhanced through the integration of multi-omics data, including mRNA, miRNA, and DNA methylation data. In light of these multi-omics data, we proposed a graph attention network (GAT) model in this study to predict the survival of non-small cell lung cancer (NSCLC). Methods: The different omics data were obtained from The Cancer Genome Atlas (TCGA) and preprocessed and combined into a single dataset using the sample ID. We used the ch

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Wang, Joshua, Runyu Hong, Jimin Tan, Wenke Liu, and David Fenyȯ. "Abstract 888: Uncovering clinically relevant omics signatures from pan-cancer imaging and multi-omics data integration." Cancer Research 84, no. 6_Supplement (2024): 888. http://dx.doi.org/10.1158/1538-7445.am2024-888.

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Abstract Despite advancements in deep learning for histopathology, integrating these insights with multi-omics data to uncover clinically relevant omics pathway-level signatures remains a challenge. Our study addresses this gap by applying unsupervised learning techniques on pan-cancer multi-omics data, leveraging 3,080 Hematoxylin and Eosin (H&E) images from 1,010 patients in Clinical Proteomic Tumor Analysis Consortium (CPTAC) to uncover omics pathway-level signatures that drive discernable morphology phenotypes at the tissue level. First, imaging models were trained to predict clinical

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Lv, Zhenghao, Dongying Zhou, Xiaolong Shi, et al. "Comparative Multi-Omics Analysis Reveals Lignin Accumulation Affects Peanut Pod Size." International Journal of Molecular Sciences 23, no. 21 (2022): 13533. http://dx.doi.org/10.3390/ijms232113533.

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Pod size is one of the important factors affecting peanut yield. However, the metabolites relating to pod size and their biosynthesis regulatory mechanisms are still unclear. In the present study, two peanut varieties (Tif and Lps) with contrasting pod sizes were used for a comparative metabolome and transcriptome analysis. Developing peanut pods were sampled at 10, 20 and 30 days after pegging (DAP). A total of 720 metabolites were detected, most of which were lipids (20.3%), followed by phenolic acids (17.8%). There were 43, 64 and 99 metabolites identified as differentially accumulated meta

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Sanches, Pedro H. Godoy, Nicolly Clemente de Melo, Andreia M. Porcari, and Lucas Miguel de Carvalho. "Integrating Molecular Perspectives: Strategies for Comprehensive Multi-Omics Integrative Data Analysis and Machine Learning Applications in Transcriptomics, Proteomics, and Metabolomics." Biology 13, no. 11 (2024): 848. http://dx.doi.org/10.3390/biology13110848.

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With the advent of high-throughput technologies, the field of omics has made significant strides in characterizing biological systems at various levels of complexity. Transcriptomics, proteomics, and metabolomics are the three most widely used omics technologies, each providing unique insights into different layers of a biological system. However, analyzing each omics data set separately may not provide a comprehensive understanding of the subject under study. Therefore, integrating multi-omics data has become increasingly important in bioinformatics research. In this article, we review strate

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Lipman, Danika, Sandra E. Safo, and Thierry Chekouo. "Multi-omic analysis reveals enriched pathways associated with COVID-19 and COVID-19 severity." PLOS ONE 17, no. 4 (2022): e0267047. http://dx.doi.org/10.1371/journal.pone.0267047.

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COVID-19 is a disease characterized by its seemingly unpredictable clinical outcomes. In order to better understand the molecular signature of the disease, a recent multi-omics study was done which looked at correlations between biomolecules and used a tree- based machine learning approach to predict clinical outcomes. This study specifically looked at patients admitted to the hospital experiencing COVID-19 or COVID-19 like symptoms. In this paper we examine the same multi-omics data, however we take a different approach, and we identify stable molecules of interest for further pathway analysi

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Lipman, Danika, Sandra E. Safo, and Thierry Chekouo. "Multi-omic analysis reveals enriched pathways associated with COVID-19 and COVID-19 severity." PLOS ONE 17, no. 4 (2022): e0267047. http://dx.doi.org/10.1371/journal.pone.0267047.

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COVID-19 is a disease characterized by its seemingly unpredictable clinical outcomes. In order to better understand the molecular signature of the disease, a recent multi-omics study was done which looked at correlations between biomolecules and used a tree- based machine learning approach to predict clinical outcomes. This study specifically looked at patients admitted to the hospital experiencing COVID-19 or COVID-19 like symptoms. In this paper we examine the same multi-omics data, however we take a different approach, and we identify stable molecules of interest for further pathway analysi

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Dai, Zhihui, Lin Chen, KaiLing Pan, et al. "Multi-omics Analysis of the Role of PHGDH in Colon Cancer." Technology in Cancer Research & Treatment 22 (January 2023): 153303382211459. http://dx.doi.org/10.1177/15330338221145994.

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Objectives: Serine metabolism is essential for tumor cells. Endogenous serine arises from de novo synthesis pathways. As the rate-limiting enzyme of this pathway, PHGDH is highly expressed in a variety of tumors including colon cancer. Therefore, targeted inhibition of PHGDH is an important strategy for anti-tumor therapy research. However, the specific gene expression and metabolic pathways regulated by PHGDH in colon cancer are still unclear. Our study was aimed to clarified the role of PHGDH in serine metabolism in colon cancer to provide new knowledge for in-depth understanding of serine m

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Sathyanarayanan, Anita, Rohit Gupta, Erik W. Thompson, Dale R. Nyholt, Denis C. Bauer, and Shivashankar H. Nagaraj. "A comparative study of multi-omics integration tools for cancer driver gene identification and tumour subtyping." Briefings in Bioinformatics 21, no. 6 (2019): 1920–36. http://dx.doi.org/10.1093/bib/bbz121.

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Abstract Oncogenesis and cancer can arise as a consequence of a wide range of genomic aberrations including mutations, copy number alterations, expression changes and epigenetic modifications encompassing multiple omics layers. Integrating genomic, transcriptomic, proteomic and epigenomic datasets via multi-omics analysis provides the opportunity to derive a deeper and holistic understanding of the development and progression of cancer. There are two primary approaches to integrating multi-omics data: multi-staged (focused on identifying genes driving cancer) and meta-dimensional (focused on e

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Haga, Masatoshi, та Mariko Okada. "Systems approaches to investigate the role of NF-κB signaling in aging". Biochemical Journal 479, № 2 (2022): 161–83. http://dx.doi.org/10.1042/bcj20210547.

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The nuclear factor-κB (NF-κB) signaling pathway is one of the most well-studied pathways related to inflammation, and its involvement in aging has attracted considerable attention. As aging is a complex phenomenon and is the result of a multi-step process, the involvement of the NF-κB pathway in aging remains unclear. To elucidate the role of NF-κB in the regulation of aging, different systems biology approaches have been employed. A multi-omics data-driven approach can be used to interpret and clarify unknown mechanisms but cannot generate mechanistic regulatory structures alone. In contrast,

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Tang, Nanyun, Kristin Leskoske, Krystine Garcia-Mansfield, et al. "CSIG-31. MULTI-OMICS TO EDGE INTO PRECISION MEDICINE FOR DIPG." Neuro-Oncology 23, Supplement_6 (2021): vi40. http://dx.doi.org/10.1093/neuonc/noab196.157.

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Abstract DIPG is an incurable pediatric brain tumor with 80% of patients harboring H3F3A (H3.3) mutation that substitutes methionine for lysine at position 27 (K27M), resulting in global depletion of H3.3K27 me3 (trimethylation). These histone mutations modify the epigenome and alter oncogenic transcription, causing oncogenic insults to progenitor cells in early neurodevelopment (1). To determine the reprogramming pathways in the cell context of H3.3K27M tumors, we conducted LC-MS based proteomic and phosphoproteomic analysis on seven patient-derived DIPG cell lines. Three normal neuronal stem

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Wei, Qingcong, Dan Wang, Kaijin Wei, Bin Xu, and Jin Xu. "The Mechanism of Elizabethkingia miricola Infection of the Black Spotted Frog as Revealed by Multi-Omics Analysis." Fishes 9, no. 3 (2024): 91. http://dx.doi.org/10.3390/fishes9030091.

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Elizabethkingia miricola (E. miricola) is a significant pathogen that causes the crooked head disease in black spotted frogs. This disease has plagued numerous frog farms in China and has resulted in substantial losses to the frog farming industry. Nonetheless, the exact mechanism that causes the disease in frogs remains unknown. In this study, transcriptomic and microbiomic analyses were conducted to analyze frog samples infected with E. miricola to reveal the infection mechanism of the pathogen. Liver transcriptomic analysis indicated that the livers of infected frogs had 1469 differentially

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Zhang, Shixuan, Luli Wu, Junrou Zhang, et al. "Multi-omics analysis reveals Mn exposure affects ferroptosis pathway in zebrafish brain." Ecotoxicology and Environmental Safety 253 (March 2023): 114616. http://dx.doi.org/10.1016/j.ecoenv.2023.114616.

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Zhang, Wenxi, Huan Zhang, Weijuan Yao, et al. "Morphometric, Hemodynamic, and Multi-Omics Analyses in Heart Failure Rats with Preserved Ejection Fraction." International Journal of Molecular Sciences 21, no. 9 (2020): 3362. http://dx.doi.org/10.3390/ijms21093362.

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(1) Background: There are no successive treatments for heart failure with preserved ejection fraction (HFpEF) because of complex interactions between environmental, histological, and genetic risk factors. The objective of the study is to investigate changes in cardiomyocytes and molecular networks associated with HFpEF. (2) Methods: Dahl salt-sensitive (DSS) rats developed HFpEF when fed with a high-salt (HS) diet for 7 weeks, which was confirmed by in vivo and ex vivo measurements. Shotgun proteomics, microarray, Western blot, and quantitative RT-PCR analyses were further carried out to inves

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Velazquez Villarreal, Enrique, and Ei-Wen Yang. "Enhancing colorectal cancer precision medicine through multi-omics and clinical data integration with artificial intelligence." Journal of Clinical Oncology 43, no. 16_suppl (2025): 3603. https://doi.org/10.1200/jco.2025.43.16_suppl.3603.

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3603 Background: The integration of multi-omics and clinical data in precision medicine research for colorectal cancer (CRC) is a complex task that requires advanced computational tools. Artificial Intelligence agent for High-Optimization and Precision mEdicine (AI-HOPE) has emerged as a transformative platform, streamlining data integration, analysis, and discovery efforts. AI-HOPE is designed to integrate and analyze multi-omics alongside clinical data, facilitating novel insights into CRC pathogenesis, therapeutic responses, and precision medicine applications. Methods: AI-HOPE leverages La

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Bellavance, Caroline, Virgile Raufaste-Cazavieille, Felix-Antoine Trifiro, et al. "Multi-omics evaluation of relapsed pediatric cancers: What information do these sequential analyses yield?" Journal of Clinical Oncology 43, no. 16_suppl (2025): 10047. https://doi.org/10.1200/jco.2025.43.16_suppl.10047.

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10047 Background: While cure rates for children with cancer have significantly improved, relapses remain a challenge, requiring deeper understanding to address them. Nowadays, genomic analyses are widely used at diagnosis and in relapse settings, becoming a standard-of-care in pediatric. The aim of this study is to describe the genomic evolution of relapsed pediatric tumors in search of clonal selection and pathway identification. We also want to assess the clinical value of these new data obtained in relapsed tumors. Methods: This is a retrospective analysis from canadian pediatric oncology p

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Oh, Jung Hun, Wookjin Choi, Euiseong Ko, Mingon Kang, Allen Tannenbaum, and Joseph O. Deasy. "PathCNN: interpretable convolutional neural networks for survival prediction and pathway analysis applied to glioblastoma." Bioinformatics 37, Supplement_1 (2021): i443—i450. http://dx.doi.org/10.1093/bioinformatics/btab285.

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Abstract Motivation Convolutional neural networks (CNNs) have achieved great success in the areas of image processing and computer vision, handling grid-structured inputs and efficiently capturing local dependencies through multiple levels of abstraction. However, a lack of interpretability remains a key barrier to the adoption of deep neural networks, particularly in predictive modeling of disease outcomes. Moreover, because biological array data are generally represented in a non-grid structured format, CNNs cannot be applied directly. Results To address these issues, we propose a novel meth

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Park, Jeongmin, Ja Min Byun, Dong-Yeop Shin, et al. "Abstract 5002: Multi-omics insights into the role of clonal hematopoiesis in multiple myeloma." Cancer Research 85, no. 8_Supplement_1 (2025): 5002. https://doi.org/10.1158/1538-7445.am2025-5002.

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Abstract Multiple myeloma (MM) is a cancer involving the abnormal proliferation of malignant plasma cells in the bone marrow (BM). Clonal hematopoiesis (CH), a condition involving the clonal expansion of blood cells due to somatic mutations, is commonly observed with aging. However, its precise role in the pathogenesis of MM remains unclear. This study hypothesizes that CH influences MM cells and the microenvironment through paracrine mechanisms, thereby contributing to disease progression and treatment resistance. Using UK Biobank data, somatic CH variants were called, excluding those with a

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Turi, Kedir N., Cole R. Michel, Jonathan Manke, Katrina A. Doenges, Nichole Reisdorph, and Alison K. Bauer. "Multi-Omics Analysis of Lung Tissue Demonstrates Changes to Lipid Metabolism during Allergic Sensitization in Mice." Metabolites 13, no. 3 (2023): 406. http://dx.doi.org/10.3390/metabo13030406.

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Allergy and asthma pathogenesis are associated with the dysregulation of metabolic pathways. To understand the effects of allergen sensitization on metabolic pathways, we conducted a multi-omics study using BALB/cJ mice sensitized to house dust mite (HDM) extract or saline. Lung tissue was used to perform untargeted metabolomics and transcriptomics while both lung tissue and plasma were used for targeted lipidomics. Following statistical comparisons, an integrated pathway analysis was conducted. Histopathological changes demonstrated an allergic response in HDM-sensitized mice. Untargeted meta

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Huang, Jingyi, Ming Liu, Hongwei Zhang, et al. "Multi-Omics Integrative Analyses Identified Two Endotypes of Hip Osteoarthritis." Metabolites 14, no. 9 (2024): 480. http://dx.doi.org/10.3390/metabo14090480.

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(1) Background: Osteoarthritis (OA) is a heterogeneous disorder, and subgroup classification of OA remains elusive. The aim of our study was to identify endotypes of hip OA and investigate the altered pathways in the different endotypes. (2) Methods: Metabolomic profiling and genome-wide genotyping were performed on fasting blood. Transcriptomic profiling was performed on RNA extracted from cartilage samples. Machine learning methods were used to identify endotypes of hip OA. Pathway analysis was used to identify the altered pathways between hip endotypes and controls. GWAS was performed on ea

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Viet-Nhi, Nguyen-Kieu, Tran Minh Quan, Vu Cong Truc, et al. "Multi-Omics Analysis Reveals the IFI6 Gene as a Prognostic Indicator and Therapeutic Target in Esophageal Cancer." International Journal of Molecular Sciences 25, no. 5 (2024): 2691. http://dx.doi.org/10.3390/ijms25052691.

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The role of the IFI6 gene has been described in several cancers, but its involvement in esophageal cancer (ESCA) remains unclear. This study aimed to identify novel prognostic indicators for ESCA-targeted therapy by investigating IFI6’s expression, epigenetic mechanisms, and signaling activities. We utilized public data from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) to analyze IFI6’s expression, clinical characteristics, gene function, pathways, and correlation with different immune cells in ESCA. The TIMER2.0 database was employed to assess the pan-cancer expression

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Wang, Huiqing, Xiao Han, Jianxue Ren, et al. "A prognostic prediction model for ovarian cancer using a cross-modal view correlation discovery network." Mathematical Biosciences and Engineering 21, no. 1 (2023): 736–64. http://dx.doi.org/10.3934/mbe.2024031.

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<abstract><p>Ovarian cancer is a tumor with different clinicopathological and molecular features, and the vast majority of patients have local or extensive spread at the time of diagnosis. Early diagnosis and prognostic prediction of patients can contribute to the understanding of the underlying pathogenesis of ovarian cancer and the improvement of therapeutic outcomes. The occurrence of ovarian cancer is influenced by multiple complex mechanisms, including the genome, transcriptome and proteome. Different types of omics analysis help predict the survival rate of ovarian cancer pat

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Adiga, Usha, Sampara Vasishta, and Alfred J. Augustine. "Integrative Genomic and Transcriptomic Analysis of Anorexia Nervosa: A Multi-omics Approach to Identify Genetic and Molecular Pathways." Biomedical and Biotechnology Research Journal 9, no. 2 (2025): 170–78. https://doi.org/10.4103/bbrj.bbrj_83_25.

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Background: Anorexia nervosa (AN) is a complex psychiatric disorder, characterized by extreme food restriction, significant weight loss, and distorted body image perception. Methods: A secondary analysis of previously published genome-wide association study data was conducted, comprising 1033 AN cases and 3733 pediatric controls. Genetic, transcriptomic, and proteomic factors underlying AN were examined, and pathway enrichment analyses were performed. Results: Significant associations were identified with genes including low-density lipoprotein receptor-related protein 2, Netrin-G1, ZNF804B, a

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Nguyen, Trinh, Xiaopeng Bian, David Roberson, et al. "Multi-omics Pathways Workflow (MOPAW): An Automated Multi-omics Workflow on the Cancer Genomics Cloud." Cancer Informatics 22 (January 2023). http://dx.doi.org/10.1177/11769351231180992.

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Introduction: In the era of big data, gene-set pathway analyses derived from multi-omics are exceptionally powerful. When preparing and analyzing high-dimensional multi-omics data, the installation process and programing skills required to use existing tools can be challenging. This is especially the case for those who are not familiar with coding. In addition, implementation with high performance computing solutions is required to run these tools efficiently. Methods: We introduce an automatic multi-omics pathway workflow, a point and click graphical user interface to Multivariate Single Samp

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Odom, Gabriel J., Antonio Colaprico, Tiago C. Silva, X. Steven Chen, and Lily Wang. "PathwayMultiomics: An R Package for Efficient Integrative Analysis of Multi-Omics Datasets With Matched or Un-matched Samples." Frontiers in Genetics 12 (December 22, 2021). http://dx.doi.org/10.3389/fgene.2021.783713.

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Recent advances in technology have made multi-omics datasets increasingly available to researchers. To leverage the wealth of information in multi-omics data, a number of integrative analysis strategies have been proposed recently. However, effectively extracting biological insights from these large, complex datasets remains challenging. In particular, matched samples with multiple types of omics data measured on each sample are often required for multi-omics analysis tools, which can significantly reduce the sample size. Another challenge is that analysis techniques such as dimension reductio

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Canzler, Sebastian, and Jörg Hackermüller. "multiGSEA: a GSEA-based pathway enrichment analysis for multi-omics data." BMC Bioinformatics 21, no. 1 (2020). http://dx.doi.org/10.1186/s12859-020-03910-x.

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Abstract Background Gaining biological insights into molecular responses to treatments or diseases from omics data can be accomplished by gene set or pathway enrichment methods. A plethora of different tools and algorithms have been developed so far. Among those, the gene set enrichment analysis (GSEA) proved to control both type I and II errors well. In recent years the call for a combined analysis of multiple omics layers became prominent, giving rise to a few multi-omics enrichment tools. Each of these has its own drawbacks and restrictions regarding its universal application. Results Here,

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Swart, Austin, Ron Caspi, Suzanne Paley, and Peter D. Karp. "Visual analysis of multi-omics data." Frontiers in Bioinformatics 4 (September 10, 2024). http://dx.doi.org/10.3389/fbinf.2024.1395981.

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We present a tool for multi-omics data analysis that enables simultaneous visualization of up to four types of omics data on organism-scale metabolic network diagrams. The tool’s interactive web-based metabolic charts depict the metabolic reactions, pathways, and metabolites of a single organism as described in a metabolic pathway database for that organism; the charts are constructed using automated graphical layout algorithms. The multi-omics visualization facility paints each individual omics dataset onto a different “visual channel” of the metabolic-network diagram. For example, a transcri

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Clark, Christopher, Loïc Dayon, Mojgan Masoodi, Gene L. Bowman, and Julius Popp. "An integrative multi-omics approach reveals new central nervous system pathway alterations in Alzheimer’s disease." Alzheimer's Research & Therapy 13, no. 1 (2021). http://dx.doi.org/10.1186/s13195-021-00814-7.

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Abstract Background Multiple pathophysiological processes have been described in Alzheimer’s disease (AD). Their inter-individual variations, complex interrelations, and relevance for clinical manifestation and disease progression remain poorly understood. We hypothesize that specific molecular patterns indicating both known and yet unidentified pathway alterations are associated with distinct aspects of AD pathology. Methods We performed multi-level cerebrospinal fluid (CSF) omics in a well-characterized cohort of older adults with normal cognition, mild cognitive impairment, and mild dementi

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Biswas, Nupur, Krishna Kumar, Sarpita Bose, Raisa Bera, and Saikat Chakrabarti. "Analysis of Pan-omics Data in Human Interactome Network (APODHIN)." Frontiers in Genetics 11 (December 8, 2020). http://dx.doi.org/10.3389/fgene.2020.589231.

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Analysis of Pan-omics Data in Human Interactome Network (APODHIN) is a platform for integrative analysis of transcriptomics, proteomics, genomics, and metabolomics data for identification of key molecular players and their interconnections exemplified in cancer scenario. APODHIN works on a meta-interactome network consisting of human protein–protein interactions (PPIs), miRNA-target gene regulatory interactions, and transcription factor-target gene regulatory relationships. In its first module, APODHIN maps proteins/genes/miRNAs from different omics data in its meta-interactome network and ext

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Ahmed, Zeeshan, Shibiao Wan, Fan Zhang, and Wen Zhong. "Artificial intelligence for omics data analysis." BMC Methods 1, no. 1 (2024). http://dx.doi.org/10.1186/s44330-024-00004-5.

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AbstractRecent technological advancements have vastly improved access to high-throughput biological instrumentation, sparking an unparalleled surge in omics data generation. The implementation of artificial intelligence techniques is revolutionizing omics data interpretation. The BMC Methods Collection "Artificial intelligence for omics data analysis" will feature novel artificial intelligence approaches leveraging multi-omics data to accelerate discoveries in personalized medicine, disease diagnostics, drug development, and biological pathway elucidation.

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Slobodyanyuk, Mykhaylo, Alexander T. Bahcheli, Zoe P. Klein, Masroor Bayati, Lisa J. Strug, and Jüri Reimand. "Directional integration and pathway enrichment analysis for multi-omics data." Nature Communications 15, no. 1 (2024). http://dx.doi.org/10.1038/s41467-024-49986-4.

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AbstractOmics techniques generate comprehensive profiles of biomolecules in cells and tissues. However, a holistic understanding of underlying systems requires joint analyses of multiple data modalities. We present DPM, a data fusion method for integrating omics datasets using directionality and significance estimates of genes, transcripts, or proteins. DPM allows users to define how the input datasets are expected to interact directionally given the experimental design or biological relationships between the datasets. DPM prioritises genes and pathways that change consistently across the data

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Cho, Hyun Jae, Mia Shu, Stefan Bekiranov, Chongzhi Zang, and Aidong Zhang. "Interpretable Meta-learning of Multi-omics Data for Survival Analysis and Pathway Enrichment." Bioinformatics, March 2, 2023. http://dx.doi.org/10.1093/bioinformatics/btad113.

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Abstract Motivation Despite the success of recent machine learning algorithms’ applications to survival analysis, their black-box nature hinders interpretability, which is arguably the most important aspect. Similarly, multi-omics data integration for survival analysis is often constrained by the underlying relationships and correlations that are rarely well understood. The goal of this work is to alleviate the interpretability problem in machine learning approaches for survival analysis and also demonstrate how multi-omics data integration improves survival analysis and pathway enrichment. We

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Zhao, Yue, and Dong-Guk Shin. "Deep Pathway Analysis V2.0: A Pathway Analysis Framework Incorporating Multi-dimensional Omics Data." IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2019, 1. http://dx.doi.org/10.1109/tcbb.2019.2945959.

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