Recognizing disease-associated nsSNP mutations in human genome through a Bayes-guided artificial neural-network model built on BioUnit and protein structure predictions

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DAMpred is a method to predict what gene mutations can cause human diseases and what mutations do not do so. Starting with a protein sequence and specified non-synonymous single nucleotide polymorphisms (nsSNPs), DAMpred calculates the probability of the mutations to be deleterious or neutral to human health. The calculation is built on a deep-learning model that integrates three sources of information from sequence profiles, biological assembly and 3D structure model (by I-TASSER), which is trained through a novel Bayes-guided artificial neural network (BANN) algorithm (>> more about DAMpred ...).

DAMpred On-line (View an example of output)

DAMpred Downloads

References

Lijun Quan, Hongjie Wu, Qiang Lyu, Yang Zhang. DAMpred: Recognizing disease-associated nsSNPs through Bayes-guided neural-network model built on low-resolution structure prediction of proteins and protein-protein interactions. J Mol Biol, in press (2019).

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