ReviewProceedings. Mathematical, physical, and engineering sciences2014
Machine learning methods in the computational biology of cancer.
Review in Proceedings. Mathematical, physical, and engineering sciences, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Low expression of OXCT1 promote colorectal cancer liver metastasis by upregulating CDK8 and β-catenin via H3 acetylation.Genes & diseases · 2026Article
- Molecular signatures and machine learning driven stress biomarkers for rainbow trout aquaculture and climate adaptation.Scientific reports · 2025Article
- Cancer-associated fibroblast-secreted FGF7 as an ovarian cancer progression promoter.Journal of translational medicine · 2024Article
- Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications.Cancers · 2023Review
- Sparse Group Lasso: Optimal Sample Complexity, Convergence Rate, and Statistical Inference.IEEE transactions on information theory · 2022Article
- Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer.World journal of gastrointestinal oncology · 2022Review
- Article
- RNA-seq assistant: machine learning based methods to identify more transcriptional regulated genes.BMC genomics · 2018Article
- Efficient least angle regression for identification of linear-in-the-parameters models.Proceedings. Mathematical, physical, and engineering sciences · 2017Article
- Machine learning based detection of age-related macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence tomography (OCT) images.Biomedical optics express · 2016Article
- Optimized Prediction of Extreme Treatment Outcomes in Ovarian Cancer.Cancer informatics · 2015Article
Corrections and comments
- Erratum issued
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The objectives of this Perspective paper are to review some recent advances in sparse feature selection for regression and classification, as well as compressed sensing, and to discuss how these might be used to develop tools to advance personalized cancer therapy. As an illustration of the possibilities, a new algorithm for sparse regression is presented and is applied to predict the time to tumour recurrence in ovarian cancer. A new algorithm for sparse feature selection in classification problems is presented, and its validation in endometrial cancer is briefly discussed. Some open problems are also presented.
Indexed as
Identifiers
What OpenQuestion holds
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.