%0 Online Multimedia %A Hakim, Adam %A Mor, Yael %A Toker, Itai %A Levine, Amir %A Neuhof, Moran %A Markovitz, Yishai %A Rechavi, Oded %D 2018 %T Additional file 4: Figure S2. of WorMachine: machine learning-based phenotypic analysis tool for worms %U https://springernature.figshare.com/articles/presentation/Additional_file_4_Figure_S2_of_WorMachine_machine_learning-based_phenotypic_analysis_tool_for_worms/5793084 %R 10.6084/m9.figshare.5793084.v1 %2 https://ndownloader.figshare.com/files/10233009 %K Caenorhabditis elegans %K Machine learning %K Deep learning %K High-throughput image analysis %K Feature extraction %K Image processing %K Phenotype analysis %X Features used to establish worm masculinity. Violin plots show the morphological features (A) and fluorescent features (B) used to determine the masculinity of him-5; [tph-1p::GFP] worms. A total of 545 pre-labeled worms of each sex were used for analysis (****p < 10–4, ***p < 10–3, *p < 0.05, two-tailed t test after α = 0.01 trimming to exclude extreme outliers with false discovery rate (FDR) correction for multiple comparisons). Only features that were significantly distinct and had plausible theoretical justification to differentiate between sexes were used for sex phenotype prediction. As can be seen in this figure, males and hermaphrodites differ in some features but not in every feature examined. (PPTX 597 kb) %I figshare