MiT4SL: multi-omics triplet representation learning for cancer cell line-adapted prediction of synthetic lethality
Siyu Tao(ShanghaiTech University), Jie Zheng(Nanyang Technological University), Min Wu(Fujian Medical University), Yang Yang(ShanghaiTech University), Yimiao Feng(ShanghaiTech University)
Cited by 1
Related Papers
Large-scale integration of meta-QTL and genome-wide association study discovers the genomic regions and candidate genes for yield and yield-related traits in bread wheat
|Theoretical and Applied Genetics|2021|140
Long Noncoding RNA X-Inactive Specific Transcript (XIST) Promotes Osteogenic Differentiation of Periodontal Ligament Stem Cells by Sponging MicroRNA-214-3p
|Medical Science Monitor|2020|54
Activation of the ERK/Creb/Bcl‑2 pathway protects periodontal ligament stem cells against hydrogen peroxide‑induced oxidative stress
|Molecular Medicine Reports|2019|36
Benchmarking machine learning methods for synthetic lethality prediction in cancer
|Nature Communications|2024|28
KR4SL: knowledge graph reasoning for explainable prediction of synthetic lethality
|Bioinformatics|2023|25