ArticleFrontiers in genetics2019
Cascaded Wx: A Novel Prognosis-Related Feature Selection Framework in Human Lung Adenocarcinoma Transcriptomes.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review.Bioengineering (Basel, Switzerland) · 2023Review
- AI/ML advances in non-small cell lung cancer biomarker discovery.Frontiers in oncology · 2023Review
- Screening of Gene Expression Markers for Corona Virus Disease 2019 Through Boruta_MCFS Feature Selection.Frontiers in public health · 2022Article
- Diagnostic and Therapeutic Implications of microRNAs in Non-Small Cell Lung Cancer.International journal of molecular sciences · 2020Review
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9 authors.
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Abstract
Artificial neural network-based analysis has recently been used to predict clinical outcomes in patients with solid cancers, including lung cancer. However, the majority of algorithms were not originally developed to identify genes associated with patients' prognoses. To address this issue, we developed a novel prognosis-related feature selection framework called Cascaded Wx (CWx). The CWx framework ranks features according to the survival of a given cohort by training neural networks with three different high- and low-risk groups in a cascaded fashion. We showed that this approach accurately identified features that best identify the patients' prognoses, compared to other feature selection algorithms, including the Cox proportional hazards and Coxnet models, when applied to The Cancer Genome Atlas lung adenocarcinoma (LUAD) transcriptome data. The prognostic potential of the top 100 genes identified by CWx outperformed or was comparable to those identified by the other methods as assessed by the concordance index (
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