Evaluation of hierarchical models for integrative genomic analyses.

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While several studies performing independent analyses of each dataset have led to significant results, a better understanding of complex biological mechanisms requires an integrative analysis of different sources of data.\nRESULTS: Flexible modeling approaches, based on penalized likelihood methods and expectation-maximization (EM) algorithms, are studied and tested under various biological relationship scenarios between the different molecular features and their effects on a clinical outcome. The models are applied to genomic datasets from two cancer types in the Cancer Genome Atlas project: glioblastoma multiforme and ovarian serous cystadenocarcinoma. The integrative models lead to improved model fit and predictive performance. They also provide a better understanding of the biological mechanisms underlying patients' survival.\nAVAILABILITY AND IMPLEMENTATION: Source code implementing the integrative models is freely available at https://github.com/mgt000/IntegrativeAnalysis along with example datasets and sample R script applying the models to these data. The TCGA datasets used for analysis are publicly available at https://tcga-data.nci.nih.gov/tcga/tcgaDownload.jsp\nCONTACT: marie.denis@cirad.fr or mgt26@georgetown.edu\nSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", "claims": [{"id": "claim-target-result", "statement": "The integrative models based on penalized likelihood methods and expectation-maximization improved model fit and predictive performance; no numerical result was reported in the abstract.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:26545823", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The integrative models lead to improved model fit and predictive performance."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The integrative models based on penalized likelihood methods and expectation-maximization improved model fit and predictive performance; no numerical result was reported in the abstract.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:26545823"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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