New Results
Investigate the relevance of major signaling pathways in cancer survival using a biologically meaningful deep learning model
Jiarui Feng, Heming Zhang, Fuhai Li
doi: https://doi.org/10.1101/2020.04.13.039487
Jiarui Feng
1Institute for Informatics (I2), Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
2Data science, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
Heming Zhang
1Institute for Informatics (I2), Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
3Computer Science, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
Fuhai Li
1Institute for Informatics (I2), Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
4Department of Pediatrics, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
Ph.DArticle usage
Posted April 14, 2020.
Investigate the relevance of major signaling pathways in cancer survival using a biologically meaningful deep learning model
Jiarui Feng, Heming Zhang, Fuhai Li
bioRxiv 2020.04.13.039487; doi: https://doi.org/10.1101/2020.04.13.039487
Subject Area
Subject Areas
- Biochemistry (11703)
- Bioengineering (8722)
- Bioinformatics (29127)
- Biophysics (14932)
- Cancer Biology (12048)
- Cell Biology (17359)
- Clinical Trials (138)
- Developmental Biology (9406)
- Ecology (14143)
- Epidemiology (2067)
- Evolutionary Biology (18268)
- Genetics (12220)
- Genomics (16766)
- Immunology (11841)
- Microbiology (28005)
- Molecular Biology (11552)
- Neuroscience (60808)
- Paleontology (450)
- Pathology (1864)
- Pharmacology and Toxicology (3231)
- Physiology (4939)
- Plant Biology (10384)
- Synthetic Biology (2877)
- Systems Biology (7333)
- Zoology (1642)