BOFdat Step 3: Identifying species-specific metabolic end goals. حسب Jean-Christophe Lachance (6619307)

Antibody challenge outcomes. حسب Andrea Blasco (7439102)

Mechanomics Code - JVT حسب Carlo Vittorio Cannistraci (5854046)

"…The functions were tested respectively in: MATLAB 2018a or youger, Python 3.9.4, R 4.0.3.…"

Code حسب Baoqiang Chen (21099509)

"…We divided the dataset into training and test sets, using 70% of the genes for training and 30% for testing. We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …"

Core data حسب Baoqiang Chen (21099509)

"…We divided the dataset into training and test sets, using 70% of the genes for training and 30% for testing. We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …"

Expression vs genomics for predicting dependencies حسب Broad DepMap (5514062)

"…Cell lines missing mutation or expression data were dropped. Remaining NA values were imputed to zero. Features types are indicated by the column matrix suffixes:</p><p dir="ltr">_Exp: expression</p><p dir="ltr">_Hot: hotspot mutation</p><p dir="ltr">_Dam: damaging mutation</p><p dir="ltr">_OtherMut: other mutation</p><p dir="ltr">_CN: copy number</p><p dir="ltr">_GSEA: ssGSEA score for an MSigDB gene set</p><p dir="ltr">_MethTSS: Methylation of transcription start sites</p><p dir="ltr">_MethCpG: Methylation of CpG islands</p><p dir="ltr">_Fusion: Gene fusions</p><p dir="ltr">_Cell: cell tissue properties</p><p dir="ltr"><br></p><p dir="ltr">NormLRT.csv: the normLRT score for the given perturbation</p><p dir="ltr"><br></p><p dir="ltr">RFAdditionScore.csv: similar to ENAdditionScore, but using a random forest model.…"