ArticleCancers2019
A Toolbox for Functional Analysis and the Systematic Identification of Diagnostic and Prognostic Gene Expression Signatures Combining Meta-Analysis and Machine Learning.
Article in Cancers, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- The Construction and Validation of a new Predictive Model for Overall Survival of Clear Cell Renal Cell Carcinoma Patients with Bone Metastasis Based on Machine Learning Algorithm.Technology in cancer research & treatmentTrial
- Development of a Machine Learning-Based Predictive Model for Lung Metastasis in Patients With Ewing Sarcoma.Frontiers in medicine · 2022Article
- Evaluation of the Role of p53 Tumour Suppressor Posttranslational Modifications and TTC5 Cofactor in Lung Cancer.International journal of molecular sciences · 2021Article
- Artificial Intelligence Identifies an Urgent Need for Peripheral Vascular Intervention by Multiplexing Standard Clinical Parameters.Biomedicines · 2021Article
- Plasma Metabolome Profiling for the Diagnosis of Catecholamine Producing Tumors.Frontiers in endocrinology · 2021Article
- Applications of Bioinformatics in Cancer.Cancers · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
The identification of biomarker signatures is important for cancer diagnosis and prognosis. However, the detection of clinical reliable signatures is influenced by limited data availability, which may restrict statistical power. Moreover, methods for integration of large sample cohorts and signature identification are limited. We present a step-by-step computational protocol for functional gene expression analysis and the identification of diagnostic and prognostic signatures by combining meta-analysis with machine learning and survival analysis. The novelty of the toolbox lies in its all-in-one functionality, generic design, and modularity. It is exemplified for lung cancer, including a comprehensive evaluation using different validation strategies. However, the protocol is not restricted to specific disease types and can therefore be used by a broad community. The accompanying R package vignette runs in ~1 h and describes the workflow in detail for use by researchers with limited bioinformatics training.
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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.