ArticleBiostatistics (Oxford, England)2016
Integrating multidimensional omics data for cancer outcome.
Article in Biostatistics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed.
- Artificial intelligence integrated multi-omics and multimodal studies promote the efficacy of neoadjuvant chemotherapy in breast cancer: opportunities, challenges, and future perspectives.Breast cancer research : BCR · 2026Review
- NetMIM: network-based multi-omics integration with block missingness for biomarker selection and disease outcome prediction.Briefings in bioinformatics · 2024Article
- Article
- HETEROGENEITY ANALYSIS VIA INTEGRATING MULTI-SOURCES HIGH-DIMENSIONAL DATA WITH APPLICATIONS TO CANCER STUDIES.Statistica Sinica · 2023Article
- Multidimensional molecular measurements-environment interaction analysis for disease outcomes.Biometrics · 2022Article
- Heterogeneous data integration methods for patient similarity networks.Briefings in bioinformatics · 2022Review
- Assisted differential network analysis for gene expression data.Genetic epidemiology · 2021Article
- Vertical integration methods for gene expression data analysis.Briefings in bioinformatics · 2021Review
- Integrating Multi-Omics Data for Gene-Environment Interactions.Biotech (Basel (Switzerland)) · 2021Article
- Integration strategies of multi-omics data for machine learning analysis.Computational and structural biotechnology journal · 2021Review
- Integration of Proteomics and Other Omics Data.Methods in molecular biology (Clifton, N.J.) · 2021Article
- An integrative sparse boosting analysis of cancer genomic commonality and difference.Statistical methods in medical research · 2020Article
- Multi-kernel linear mixed model with adaptive lasso for prediction analysis on high-dimensional multi-omics data.Bioinformatics (Oxford, England) · 2020Article
- Steroidomics for the Prevention, Assessment, and Management of Cancers: A Systematic Review and Functional Analysis.Metabolites · 2019Review
- Horizontal and vertical integrative analysis methods for mental disorders omics data.Scientific reports · 2019Article
- Assisted graphical model for gene expression data analysis.Statistics in medicine · 2019Article
- A Selective Review of Multi-Level Omics Data Integration Using Variable Selection.High-throughput · 2019Review
- Assisted gene expression-based clustering with AWNCut.Statistics in medicine · 2018Article
- Robust network-based analysis of the associations between (epi)genetic measurements.Journal of multivariate analysis · 2018Article
- Analysis of cancer gene expression data with an assisted robust marker identification approach.Genetic epidemiology · 2017Article
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Authors and funding
4 authors.
Funding
Abstract
In multidimensional cancer omics studies, one subject is profiled on multiple layers of omics activities. In this article, the goal is to integrate multiple types of omics measurements, identify markers, and build a model for cancer outcome. The proposed analysis is achieved in two steps. In the first step, we analyze the regulation among different types of omics measurements, through the construction of linear regulatory modules (LRMs). The LRMs have sound biological basis, and their construction differs from the existing analyses by modeling the regulation of sets of gene expressions (GEs) by sets of regulators. The construction is realized with the assistance of regularized singular value decomposition. In the second step, the proposed cancer outcome model includes the regulated GEs, "residuals" of GEs, and "residuals" of regulators, and we use regularized estimation to select relevant markers. Simulation shows that the proposed method outperforms the alternatives with more accurate marker identification. We analyze the The Cancer Genome Atlas data on cutaneous melanoma and lung adenocarcinoma and obtain meaningful results.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.