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one-class logistic regression (oclr) machine learning method  (MELTA Ltd)

 
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    MELTA Ltd one-class logistic regression (oclr) machine learning method
    One Class Logistic Regression (Oclr) Machine Learning Method, supplied by MELTA Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/machine+learning+method/one+class+logistic+regression++oclr++machine+learning+method/pmc09519161-73-12-2
    Average 90 stars, based on 1 article reviews
    one-class logistic regression (oclr) machine learning method - by Bioz Stars, 2026-09
    90/100 stars

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    other:

    Article Title: Identification and verification of the ferroptosis- and pyroptosis-associated prognostic signature for low-grade glioma
    Article Snippet: In 2018, Melta’s article published in the “Cell” journal [ ] applied one-class logistic regression (OCLR) machine learning method and extracted epigenetic and transcriptomic feature sets from non-transformed pluripotent stem cells as well as their differentiated progeny.



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    A comparison of genomic prediction performance of the naïve ensemble-average (ensemble) model vs each of the individual genomic prediction models in violin plots. The width of the violins indicates the distribution of the metric values for predictions from all combinations of the 5 RIL populations, 3 training-test ratios, and 500 random samples. The performance of genomic prediction models was measured with a) the Pearson correlation and b) MSE. The orange represents the performance of classical models (rrBLUP, BayesB, and RKHS) while the green represents machine learning models (RF, SVR, and GAT). The red is the performance of the ensemble. Box plots within the violin plots represent the median metric value (white line) and the interquartile range (black box) with whiskers extending 1.5 times the interquartile range.

    Journal: G3: Genes | Genomes | Genetics

    Article Title: Improved genomic prediction performance with ensembles of diverse models

    doi: 10.1093/g3journal/jkaf048

    Figure Lengend Snippet: A comparison of genomic prediction performance of the naïve ensemble-average (ensemble) model vs each of the individual genomic prediction models in violin plots. The width of the violins indicates the distribution of the metric values for predictions from all combinations of the 5 RIL populations, 3 training-test ratios, and 500 random samples. The performance of genomic prediction models was measured with a) the Pearson correlation and b) MSE. The orange represents the performance of classical models (rrBLUP, BayesB, and RKHS) while the green represents machine learning models (RF, SVR, and GAT). The red is the performance of the ensemble. Box plots within the violin plots represent the median metric value (white line) and the interquartile range (black box) with whiskers extending 1.5 times the interquartile range.

    Article Snippet: From the various machine learning methods, we selected RF , SVR ( Drucker et al. 1996 ), and GAT ( Velickovic et al. 2017 ) for our investigation of ensemble prediction.

    Techniques: Comparison