Cargando…
Leveraging Machine Learning Models for Peptide-Protein Interaction Prediction
Peptides play a pivotal role in a wide range of biological activities through participating in up to 40% protein-protein interactions in cellular processes. They also demonstrate remarkable specificity and efficacy, making them promising candidates for drug development. However, predicting peptide-p...
Autores principales: | Song, Yin, Mi, Xuenan, Shukla, Diwakar |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cornell University
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10635286/ https://www.ncbi.nlm.nih.gov/pubmed/37961736 |
Ejemplares similares
-
Leveraging explanations in interactive machine learning: An overview
por: Teso, Stefano, et al.
Publicado: (2023) -
Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets
por: Fu, Ci, et al.
Publicado: (2021) -
Leveraging Scheme for Cross-Study Microbiome Machine Learning Prediction and Feature Evaluations
por: Song, Kuncheng, et al.
Publicado: (2023) -
Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques
por: Aamir, Sanam, et al.
Publicado: (2022) -
BIOPHYSICAL PREDICTION OF PROTEIN-PEPTIDE INTERACTIONS AND SIGNALING NETWORKS USING MACHINE LEARNING
por: Cunningham, Joseph M., et al.
Publicado: (2020)