Journal: Journal of Cheminformatics

Article Title: Modeling of non-additive mixture properties using the Online CHEmical database and Modeling environment (OCHEM)

doi: 10.1186/1758-2946-5-4

Figure Lengend Snippet: Statistical parameters of Online Chemical Modeling Environment (OCHEM, http://ochem.eu ) models with the lowest RMSE according to the “ compounds-out ” cross-validation protocol for the prediction of binary mixture densities

Article Snippet: Using these descriptors, the results for the test set prediction calculated by LibSVM with ChemAxon descriptors were R 2 = 0.94 ± 0.02, RMSE = 0.006 ± 0.001 – the highest prediction accuracy achieved for this test.

Techniques: Biomarker Discovery

Journal: Nature Communications

Article Title: On the performance of pre-microRNA detection algorithms

doi: 10.1038/s41467-017-00403-z

Figure Lengend Snippet: Available pre-miRNA detection tools

Article Snippet: For SVM, Weka LibSVM (3.7) was used since it was fast comparing to other SVM implementations available in KNIME.

Techniques: Sampling, Selection

Journal: JMIR Mental Health

Article Title: Detecting and Measuring Depression on Social Media Using a Machine Learning Approach: Systematic Review

doi: 10.2196/27244

Figure Lengend Snippet: Summary of machine learning studies of detection of depression using text data from social media.

Article Snippet: Sadeque et al [ ], 2017 , 888 users (136 depressed/752 nondepressed), the ages of the samples were not reported , Reddit , F-measure: LibSVM u =0.40, WekaSVM v =0.30, RNN=0.34, Ensemble=0.45 , User-declared , LibSVM, RNN, Ensemble, WekaSVM , Depression lexicon y , metamap features z , 5-fold cross-validation , Observational cohort study.

Techniques: Control, Labeling

Journal: JMIR Mental Health

Article Title: Detecting and Measuring Depression on Social Media Using a Machine Learning Approach: Systematic Review

doi: 10.2196/27244

Figure Lengend Snippet: Summary of the machine learning approaches used in the depression detection studies.

Article Snippet: Sadeque et al [ ], 2017 , 888 users (136 depressed/752 nondepressed), the ages of the samples were not reported , Reddit , F-measure: LibSVM u =0.40, WekaSVM v =0.30, RNN=0.34, Ensemble=0.45 , User-declared , LibSVM, RNN, Ensemble, WekaSVM , Depression lexicon y , metamap features z , 5-fold cross-validation , Observational cohort study.

Techniques: