Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data.
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| Title: | Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data. |
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| Authors: | Smith AM; Unlearn.AI, Inc., San Francisco, CA, USA. drams@unlearn.ai., Walsh JR; Unlearn.AI, Inc., San Francisco, CA, USA., Long J; Computational Sciences, Worldwide Research & Development, Pfizer Inc., Cambridge, MA, USA., Davis CB; Oncology Global Product Development, Pfizer Inc., San Diego, CA, USA., Henstock P; Business Technology, Pfizer Inc., Cambridge, MA, USA., Hodge MR; Inflammation and Immunology, Worldwide Research & Development, Pfizer Inc., Cambridge, MA, USA., Maciejewski M; Inflammation and Immunology, Worldwide Research & Development, Pfizer Inc., Cambridge, MA, USA., Mu XJ; Oncology Research & Development, Worldwide Research & Development, Pfizer Inc., San Diego, CA, USA., Ra S; Computational Sciences, Worldwide Research & Development, Pfizer Inc., Cambridge, MA, USA., Zhao S; Computational Sciences, Worldwide Research & Development, Pfizer Inc., Cambridge, MA, USA., Ziemek D; Inflammation and Immunology, Worldwide Research & Development, Pfizer Pharma GmbH., Berlin, Germany., Fisher CK; Unlearn.AI, Inc., San Francisco, CA, USA. |
| Source: | BMC bioinformatics [BMC Bioinformatics] 2020 Mar 20; Vol. 21 (1), pp. 119. Date of Electronic Publication: 2020 Mar 20. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: BioMed Central Country of Publication: England NLM ID: 100965194 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2105 (Electronic) Linking ISSN: 14712105 NLM ISO Abbreviation: BMC Bioinformatics Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1471-2105 |
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| DOI: | 10.1186/s12859-020-3427-8 |
