ArticleInternational journal of molecular sciences2020
Flexible Data Trimming Improves Performance of Global Machine Learning Methods in Omics-Based Personalized Oncology.
Article in International journal of molecular sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- Bioinformatics in Russia: history and present-day landscape.Briefings in bioinformatics · 2024Review
- Machine learning for predicting accuracy of lung and liver tumor motion tracking using radiomic features.Quantitative imaging in medicine and surgery · 2023Article
- Article
- Uniformly shaped harmonization combines human transcriptomic data from different platforms while retaining their biological properties and differential gene expression patterns.Frontiers in molecular biosciences · 2023Article
- Transcriptomic Harmonization as the Way for Suppressing Cross-Platform Bias and Batch Effect.Biomedicines · 2022Review
- Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer.Frontiers in genetics · 2022Review
- Recent Trends in Cancer Genomics and Bioinformatics Tools Development.International journal of molecular sciences · 2021Article
- Machine Learning Applicability for Classification of PAD/VCD Chemotherapy Response Using 53 Multiple Myeloma RNA Sequencing Profiles.Frontiers in oncology · 2021Article
- System, Method and Software for Calculation of a Cannabis Drug Efficiency Index for the Reduction of Inflammation.International journal of molecular sciences · 2020Article
- Cancer gene expression profiles associated with clinical outcomes to chemotherapy treatments.BMC medical genomics · 2020Article
- Bioinformatics Methods in Medical Genetics and Genomics.International journal of molecular sciences · 2020Article
- Editorial: Next Generation Sequencing Based Diagnostic Approaches in Clinical Oncology.Frontiers in oncology · 2020Article
- Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer.Frontiers in oncology · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
(1) Background: Machine learning (ML) methods are rarely used for an omics-based prescription of cancer drugs, due to shortage of case histories with clinical outcome supplemented by high-throughput molecular data. This causes overtraining and high vulnerability of most ML methods. Recently, we proposed a hybrid global-local approach to ML termed floating window projective separator (FloWPS) that avoids extrapolation in the feature space. Its core property is data trimming, i.e., sample-specific removal of irrelevant features. (2) Methods: Here, we applied FloWPS to seven popular ML methods, including linear SVM,
Indexed as
Identifiers
What Socratic 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 Socratic graph.