A data science approach for the classification of low-grade and high-grade ovarian serous carcinomas.
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| Title: | A data science approach for the classification of low-grade and high-grade ovarian serous carcinomas. |
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| Authors: | Lin S; School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, 85281, AZ, USA., Wang C; Department of Health Sciences Research, Mayo Clinic, Rochester, 55905, MN, USA., Zarei S; Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, 55905, MN, USA., Bell DA; Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, 55905, MN, USA., Kerr SE; Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, 55905, MN, USA., Runger GC; School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, 85281, AZ, USA.; Department of Biomedical Informatics, Arizona State University, Scottsdale, 85259, AZ, USA., Kocher JA; Department of Health Sciences Research, Mayo Clinic, Rochester, 55905, MN, USA. kocher.jeanpierre@mayo.edu. |
| Source: | BMC genomics [BMC Genomics] 2018 Nov 27; Vol. 19 (1), pp. 841. Date of Electronic Publication: 2018 Nov 27. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: BioMed Central Country of Publication: England NLM ID: 100965258 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2164 (Electronic) Linking ISSN: 14712164 NLM ISO Abbreviation: BMC Genomics Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1471-2164 |
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| DOI: | 10.1186/s12864-018-5177-9 |
