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

Design and implementation of DelaySSA. The input data for DelaySSA are reaction information, initial states, parameters and delays. Combining these into the simulation function, th... حسب Ziyan Jin (21021976)

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

Antibody challenge outcomes. حسب Andrea Blasco (7439102)

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

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

"…We tested and validated the algorithm and presented the utilization of MADis in a pod pigmentation L1 gene case study with multiple CMs from natural or artificial selection. …"

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

"…We tested and validated the algorithm and presented the utilization of MADis in a pod pigmentation L1 gene case study with multiple CMs from natural or artificial selection. …"

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

"…We tested and validated the algorithm and presented the utilization of MADis in a pod pigmentation L1 gene case study with multiple CMs from natural or artificial selection. …"

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

"…We tested and validated the algorithm and presented the utilization of MADis in a pod pigmentation L1 gene case study with multiple CMs from natural or artificial selection. …"

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

"…We tested and validated the algorithm and presented the utilization of MADis in a pod pigmentation L1 gene case study with multiple CMs from natural or artificial selection. …"

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

"…We tested and validated the algorithm and presented the utilization of MADis in a pod pigmentation L1 gene case study with multiple CMs from natural or artificial selection. …"

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

"…We tested and validated the algorithm and presented the utilization of MADis in a pod pigmentation L1 gene case study with multiple CMs from natural or artificial selection. …"

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

"…We tested and validated the algorithm and presented the utilization of MADis in a pod pigmentation L1 gene case study with multiple CMs from natural or artificial selection. …"

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

"…We tested and validated the algorithm and presented the utilization of MADis in a pod pigmentation L1 gene case study with multiple CMs from natural or artificial selection. …"

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

"…We tested and validated the algorithm and presented the utilization of MADis in a pod pigmentation L1 gene case study with multiple CMs from natural or artificial selection. …"

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). …"

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). …"

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.…"

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

"…<p dir="ltr">This dataset supports the "<a href="https://doi.org/10.1101/2020.02.21.959627" rel="noreferrer" target="_blank">Gene expression has more power for predicting <i>in vitro</i> cancer cell vulnerabilities than genomics</a>" preprint by Dempster <i>et al. …"