A data science approach for the classification of low-grade and high-grade ovarian serous carcinomas.
Saved in:
| Title: | A data science approach for the classification of low-grade and high-grade ovarian serous carcinomas. |
|---|---|
| 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 |
|
Full text is not displayed to guests.
Login for full access.
|
|
Be the first to leave a comment!
