ASCL2-X Synergy
Cross-source consensus on ASCL2-X Synergy from 1 sources and 6 claims.
1 sources · 6 claims
Uses
How it works
Comparisons
Highlighted claims
- The paper applies the ranking framework to second-order ASCL2-X combinations after ETC-1922159 treatment. — Machine learning discoveries of ASCL2-X synergy in ETC-1922159 treated colorectal cancer cells
- The study focuses on down-regulated ASCL2-X pairs and distinguishes lower-ranked candidate synergies from higher-ranked combinations. — Machine learning discoveries of ASCL2-X synergy in ETC-1922159 treated colorectal cancer cells
- TGFBR3-ASCL2 was the only prioritized TGFB-family pair. — Machine learning discoveries of ASCL2-X synergy in ETC-1922159 treated colorectal cancer cells
- The SLC family produced the largest set of candidate ASCL2 synergies. — Machine learning discoveries of ASCL2-X synergy in ETC-1922159 treated colorectal cancer cells
- The prioritized hypotheses include ASCL2 relationships with multiple cytokine, receptor, transporter, transcription factor, noncoding RNA, autophagy, apoptosis, and Rho-GAP family members. — Machine learning discoveries of ASCL2-X synergy in ETC-1922159 treated colorectal cancer cells
- The WNT10B-ASCL2 pair is interpreted as possibly connected to ASCL2 activation and PORCN-WNT inhibition. — Machine learning discoveries of ASCL2-X synergy in ETC-1922159 treated colorectal cancer cells