SLxGO, a network-independent machine learning framework that predicts SL interactions directly from semantic representations of Gene Ontology annotations encoded via BioBERT-derived embeddings, is developed, establishing a comprehensive, genome-scale resource for context-specific vulnerability mapping across the human interactome.
Genome-wide CRISPR screening has enabled the development of dependency maps in hundreds of cancer cell lines, facilitating the identification of genetic vulnerabilities associated with specific biomarkers and supporting precision oncology efforts. However, current dependency maps largely capture single-gene effec...
Narod Daldal, Hamda Ajmal, David J. Adams et al.· Genome Medicine· 0 citations
ProtScape, a multiscale graph-learning framework integrating global protein interactions, cell-type gene expression and protein language models to learn context-specific representations and infer interactomes across more than 200 cell types, substantially outperforms existing approaches in interaction reconstruction.
Alois Thomas, Lisa Fournier, Vincent Jung et al.· bioRxiv· 0 citations
Abstract Genetic interactions (GIs) underpin the functional connectivity of genes and pathways, and are important for dissecting genotype–phenotype relationships and identifying therapeutic targets for diseases. However, the scale of the human genome restricts systematic experimental interrogation of GIs. Existing comp...
Qi-Zheng Wang, Fan Yang, Meng-Jie Fu et al.· Briefings in Bioinformatics· 0 citations
A symmetric three-class predictor of DrugComb ZIP interactions was developed and compared all seven combinations of landmark expression, Hallmark pathway scores and CollecTRI regulon activities, demonstrating that the predictive value of functional context representations depends on both the type of domain shift and th...
Elizaveta Ignatova, M. Likhter· bioRxiv· 0 citations
This study proposes DeepPEP, a large language model-based framework designed to reliably transfer essential protein annotations between distantly related organisms and suggests that DeepPEP is a powerful strategy for prokaryotic essential protein prediction.
Ying Du, Zhikang Liu, Jing Wan· Journal of Microbiological M...· 0 citations
It is demonstrated that attention-based protein language models can accurately identify nucleic acid-binding proteins directly from sequence data and reveal biologically meaningful sequence determinants of binding, establishing an interpretable and scalable framework for proteome-wide characterization of protein–nuclei...
Hanjin Kim, Sung-Gwon Lee, Joo-Seong Oh et al.· Bioinformatics Advances· 0 citations
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