Machine-Learning Ranking
Cross-source consensus on Machine-Learning Ranking from 1 sources and 5 claims.
1 sources · 5 claims
How it works
Comparisons
Evidence quality
Highlighted claims
- The ranking framework used SVM-rank software from Joachims. — Machine learning discoveries of ASCL2-X synergy in ETC-1922159 treated colorectal cancer cells
- The analysis used Laplace, linear, and radial basis function ranking methods. — Machine learning discoveries of ASCL2-X synergy in ETC-1922159 treated colorectal cancer cells
- A previously developed machine-learning search engine ranks gene or protein combinations in signaling pathways. — Machine learning discoveries of ASCL2-X synergy in ETC-1922159 treated colorectal cancer cells
- Lower rank values are interpreted as stronger evidence for possible ASCL2-X synergy before treatment and joint down-regulation after drug exposure. — Machine learning discoveries of ASCL2-X synergy in ETC-1922159 treated colorectal cancer cells
- Majority voting across ranking methods was used to identify plausible unexplored ASCL2-X combinations. — Machine learning discoveries of ASCL2-X synergy in ETC-1922159 treated colorectal cancer cells