ArticleProceedings. International Conference on Computational Science and Computational Intelligence2018
Optimizing support vector machine analysis in low density biological data sets.
Article in Proceedings. International Conference on Computational Science and Computational Intelligence, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Making Sense of the Highly Variable Effects of Alcohol on Bone.Clinical reviews in bone and mineral metabolism · 2021Article
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
We explore the effectiveness of Support Vector Machines (SVM) for classification in a sparse data set. Non-human primate models are utilized to analyze Alcohol Use Disorders (AUDs); however, the resulting data have a limited sample size. The challenge of low sample numbers and low replicates are explored using a variety of optimization strategies for feature extraction, including correlation, entropy, density, linear support vector machines for regression (SVR), backward SVR, and forward SVR. We investigate these approaches against the backdrop of the relationship between alcohol consumption and tibial bone mineral density. The results indicate that machine learning (ML) can effectively be used in cases of low and diverse biological data sets. The best relevance feature ranking strategies are correlation, SVR forward, and SVR backward.
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.