Gene content overlap in <i>RNF43 G659fs</i> mutation-specific pathways. by Alexis M. Thornton (11068403)

BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data by Jean-Christophe Lachance (6619307)

“…Despite its importance, no standardized computational platform is currently available to generate species-specific biomass objective functions in a data-driven, unbiased fashion. To fill this gap in the metabolic modeling software ecosystem, we implemented BOFdat, a Python package for the definition of a <b>B</b>iomass <b>O</b>bjective <b>F</b>unction from experimental <b>dat</b>a. …”

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

Antibody challenge outcomes. by Andrea Blasco (7439102)

Example of a typical workflow to construct a network using NeKo. by Marco Ruscone (11747264)

SpatialKNifeY analysis landscape. by Shunsuke A. Sakai (13789939)

Table1_Natural and artificial selection of multiple alleles revealed through genomic analyses.xlsx by Jana Biová (11287971)

“…For more precise breeding, concrete candidate genes with exact functional variants must be discovered. …”

Table8_Natural and artificial selection of multiple alleles revealed through genomic analyses.xlsx by Jana Biová (11287971)

“…For more precise breeding, concrete candidate genes with exact functional variants must be discovered. …”

Table4_Natural and artificial selection of multiple alleles revealed through genomic analyses.xlsx by Jana Biová (11287971)

“…For more precise breeding, concrete candidate genes with exact functional variants must be discovered. …”

Table3_Natural and artificial selection of multiple alleles revealed through genomic analyses.xlsx by Jana Biová (11287971)

“…For more precise breeding, concrete candidate genes with exact functional variants must be discovered. …”

Table2_Natural and artificial selection of multiple alleles revealed through genomic analyses.xlsx by Jana Biová (11287971)

“…For more precise breeding, concrete candidate genes with exact functional variants must be discovered. …”

Table7_Natural and artificial selection of multiple alleles revealed through genomic analyses.docx by Jana Biová (11287971)

“…For more precise breeding, concrete candidate genes with exact functional variants must be discovered. …”

Table5_Natural and artificial selection of multiple alleles revealed through genomic analyses.xlsx by Jana Biová (11287971)

“…For more precise breeding, concrete candidate genes with exact functional variants must be discovered. …”

DataSheet1_Natural and artificial selection of multiple alleles revealed through genomic analyses.docx by Jana Biová (11287971)

“…For more precise breeding, concrete candidate genes with exact functional variants must be discovered. …”

Table6_Natural and artificial selection of multiple alleles revealed through genomic analyses.xlsx by Jana Biová (11287971)

“…For more precise breeding, concrete candidate genes with exact functional variants must be discovered. …”

Table1_Natural and artificial selection of multiple alleles revealed through genomic analyses.DOCX by Jana Biová (11287971)

“…For more precise breeding, concrete candidate genes with exact functional variants must be discovered. …”

MOMA compared to MFA-derived estimates, carbon yield efficiencies and CBA co-factor profile comparison across unconstrained, manually curated and experimentally constrained solutio... by Laura de Arroyo Garcia (9226096)

Code by 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 by 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 by Broad DepMap (5514062)

“…</p><p dir="ltr"><br></p><p dir="ltr">OncoKB_oncogenes.csv: A list of genes that have non-expression-based alterations listed as likely oncogenic or oncogenic by OncoKB as of 9 May 2018.…”