A Novel Automated Framework for QSAR Modeling of Highly Imbalanced Leishmania High-Throughput Screening Data
Sign Up to like & getrecommendations! Published in 2021 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.0c01439
Abstract: In silico prediction of antileishmanial activity using quantitative structure-activity relationship (QSAR) models has been developed on limited and small datasets. Nowadays, the availability of large and diverse high-throughput screening data provides an opportunity to the… read more here.
Keywords: highly imbalanced; consensus; high throughput; model ... See more keywords
Deep Clustering via Center-Oriented Margin Free-Triplet Loss for Skin Lesion Detection in Highly Imbalanced Datasets
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Journal of Biomedical and Health Informatics"
DOI: 10.1109/jbhi.2022.3187215
Abstract: Melanoma is a fatal skin cancer that is curable and has dramatically increasing survival rate when diagnosed at early stages. Learning-based methods hold significant promise for the detection of melanoma from dermoscopic images. However, since… read more here.
Keywords: deep clustering; highly imbalanced; triplet loss; triplet ... See more keywords
Comparison between Statistical Models and Machine Learning Methods on Classification for Highly Imbalanced Multiclass Kidney Data
Sign Up to like & getrecommendations! Published in 2020 at "Diagnostics"
DOI: 10.3390/diagnostics10060415
Abstract: This study aims to compare the classification performance of statistical models on highly imbalanced kidney data. The health examination cohort database provided by the National Health Insurance Service in Korea is utilized to build models… read more here.
Keywords: classification; machine learning; highly imbalanced; performance ... See more keywords