ArticleScientific reports2021
The structure-based cancer-related single amino acid variation prediction.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 19 citations in OpenAlex.
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- Predicting Mutational Effects on CaJournal of the American Chemical Society · 2024Article
- An ensemble-based deep learning model for detection of mutation causing cutaneous melanoma.Scientific reports · 2023Article
- A Peptide Vaccine Design Targeting KIT Mutations in Acute Myeloid Leukemia.Pharmaceuticals (Basel, Switzerland) · 2023Article
- Determinants of DNMT2/TRDMT1 preference for substrates tRNA and DNA during the evolution.RNA biology · 2023Article
- Recognition of a Novel Gene Signature for Human Glioblastoma.International journal of molecular sciences · 2022Article
- Predicting Anticancer Drug Resistance Mediated by Mutations.Pharmaceuticals (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single amino acid variation (SAV) is an amino acid substitution of the protein sequence that can potentially influence the entire protein structure or function, as well as its binding affinity. Protein destabilization is related to diseases, including several cancers, although using traditional experiments to clarify the relationship between SAVs and cancer uses much time and resources. Some SAV prediction methods use computational approaches, with most predicting SAV-induced changes in protein stability. In this investigation, all SAV characteristics generated from protein sequences, structures and the microenvironment were converted into feature vectors and fed into an integrated predicting system using a support vector machine and genetic algorithm. Critical features were used to estimate the relationship between their properties and cancers caused by SAVs. We describe how we developed a prediction system based on protein sequences and structure that is capable of distinguishing if the SAV is related to cancer or not. The five-fold cross-validation performance of our system is 89.73% for the accuracy, 0.74 for the Matthews correlation coefficient, and 0.81 for the F1 score. We have built an online prediction server, CanSavPre ( http://bioinfo.cmu.edu.tw/CanSavPre/ ), which is expected to become a useful, practical tool for cancer research and precision medicine.
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