ArticleProceedings of the 2014 6th International Advanced Research Workshop on In Silico Oncology and Cancer Investigation : the CHIC Project Workshop (IARWISOCI) : Athens, Greece, 3-4 November 2014. International Advanced Research Workshop on...2014
Machine Learning Predictions of Cancer Driver Mutations.
Article in Proceedings of the 2014 6th International Advanced Research Workshop on In Silico Oncology and Cancer Investigation : the CHIC Project Workshop (IARWISOCI) : Athens, Greece, 3-4 November 2014. International Advanced Research Workshop on..., 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Computational algorithms for in silico profiling of activating mutations in cancer.Cellular and molecular life sciences : CMLS · 2019Review
- BRCA2, EGFR, and NTRK mutations in mismatch repair-deficient colorectal cancers with MSH2 or MLH1 mutations.Oncotarget · 2017Article
- Applications of Support Vector Machine (SVM) Learning in Cancer Genomics.Cancer genomics & proteomicsReview
- Cancer testis antigen XAGE-1 is a promising marker for the diagnosis and treatment of ovarian cancer.Journal of medicine and lifeArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
A method to predict the activation status of kinase domain mutations in cancer is presented. This method, which makes use of the machine learning technique support vector machines (SVM), has applications to cancer treatment, as well as numerous other diseases that involve kinase misregulation.
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.