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Jake Crawford

Showing results (1-10 of 14) with videos related to

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Current Opinion in Biotechnology|January 22, 2020
Incorporating biological structure into machine learning models in biomedicineJake Crawford, Casey S Greene
Patterns (New York, N.Y.)|January 8, 2025
Best holdout assessment is sufficient for cancer transcriptomic model selectionJake Crawford, Maria Chikina, Casey S Greene
Bioinformatics Advances|January 29, 2024
Optimizer's dilemma: optimization strongly influences model selection in transcriptomic predictionJake Crawford, Maria Chikina, Casey S Greene
Plos Computational Biology|March 27, 2023
The effect of non-linear signal in classification problems using gene expressionBenjamin J Heil, Jake Crawford, Casey S Greene
BMC Systems Biology|March 29, 2018
Detangling PPI networks to uncover functionally meaningful clustersSarah Hall-Swan, Jake Crawford, Rebecca Newman, et al.
BMC Systems Biology|November 21, 2018
Retraction Note: detangling PPI networks to uncover functionally meaningful clustersSarah Hall-Swan, Jake Crawford, Rebecca Newman, et al.
Genome Biology|June 27, 2022
Widespread redundancy in -omics profiles of cancer mutation statesJake Crawford, Brock C Christensen, Maria Chikina, et al.
Bioinformatics (Oxford, England)|October 4, 2022
wenda_gpu: fast domain adaptation for genomic dataAriel A Hippen, Jake Crawford, Jacob R Gardner, et al.
Bioinformatics (Oxford, England)|April 10, 2020
MONET: a toolbox integrating top-performing methods for network modularizationMattia Tomasoni, Sergio Gómez, Jake Crawford, et al.
AJP Reports|June 25, 2020
Redefining Second Stage of Labor: Number of Pushing ContractionsSerin M Bok, Gabriela E Pena Carmona, Jake Crawford, et al.
Pageof 2

Showing results (1-10 of 14) with videos related to

Sort By:
Pageof 2
Current Opinion in Biotechnology|January 22, 2020
Incorporating biological structure into machine learning models in biomedicineJake Crawford, Casey S Greene
Patterns (New York, N.Y.)|January 8, 2025
Best holdout assessment is sufficient for cancer transcriptomic model selectionJake Crawford, Maria Chikina, Casey S Greene
Bioinformatics Advances|January 29, 2024
Optimizer's dilemma: optimization strongly influences model selection in transcriptomic predictionJake Crawford, Maria Chikina, Casey S Greene
Plos Computational Biology|March 27, 2023
The effect of non-linear signal in classification problems using gene expressionBenjamin J Heil, Jake Crawford, Casey S Greene
BMC Systems Biology|March 29, 2018
Detangling PPI networks to uncover functionally meaningful clustersSarah Hall-Swan, Jake Crawford, Rebecca Newman, et al.
BMC Systems Biology|November 21, 2018
Retraction Note: detangling PPI networks to uncover functionally meaningful clustersSarah Hall-Swan, Jake Crawford, Rebecca Newman, et al.
Genome Biology|June 27, 2022
Widespread redundancy in -omics profiles of cancer mutation statesJake Crawford, Brock C Christensen, Maria Chikina, et al.
Bioinformatics (Oxford, England)|October 4, 2022
wenda_gpu: fast domain adaptation for genomic dataAriel A Hippen, Jake Crawford, Jacob R Gardner, et al.
Bioinformatics (Oxford, England)|April 10, 2020
MONET: a toolbox integrating top-performing methods for network modularizationMattia Tomasoni, Sergio Gómez, Jake Crawford, et al.
AJP Reports|June 25, 2020
Redefining Second Stage of Labor: Number of Pushing ContractionsSerin M Bok, Gabriela E Pena Carmona, Jake Crawford, et al.
Pageof 2