<p>Different combinations of three types of nsSNPs are used for each of the three classification problems. The set of 878 PPI preserving mutations included both beneficial (B) and neutral (N) nsSNPs. The unlabeled set was used solely for semi-supervised learning methods.</p
Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding ...
Training and testing of conventional machine learning models on binary classification problems depen...
The increasing demand for the identification of genetic variation responsible for common diseases ha...
Many non-synonymous SNPs (nsSNPs) are associated with diseases, and numerous machine learning method...
Nonsynonymous single nucleotide polymorphisms (nsSNPs) are prevalent in genomes and are closely asso...
<p>The four known mutations were analyzed by non-synonymous single nucleotide polymorphism (nsSNP) p...
<p>(The individual DNN classifier is constructed by three hidden layers. The horizontal axis and the...
<p>Panel (a) describes key features of the SNP association approaches used. Panel (b) shows, for a s...
ROC curves for PremPDI, mCSM-NA and SAMPDI methods applied on different training and test set. More ...
A) Mutant counts per wild-type (wt) -> mutant (mut) amino acid substitutions and B) Mutant type aver...
The increasing demand for the identification of genetic variation responsible for common diseases ha...
<p>Contribution of each source database to the training datasets. The table shows the number of SNPs...
<div><p>Single nucleotide polymorphisms (SNPs) are among the most common types of genetic variation ...
<p>(A) Number of SNPs distributed per unigene; (B) Classification of different substitution types of...
Single nucleotide polymorphisms (SNPs) are among the most common types of genetic variation in compl...
Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding ...
Training and testing of conventional machine learning models on binary classification problems depen...
The increasing demand for the identification of genetic variation responsible for common diseases ha...
Many non-synonymous SNPs (nsSNPs) are associated with diseases, and numerous machine learning method...
Nonsynonymous single nucleotide polymorphisms (nsSNPs) are prevalent in genomes and are closely asso...
<p>The four known mutations were analyzed by non-synonymous single nucleotide polymorphism (nsSNP) p...
<p>(The individual DNN classifier is constructed by three hidden layers. The horizontal axis and the...
<p>Panel (a) describes key features of the SNP association approaches used. Panel (b) shows, for a s...
ROC curves for PremPDI, mCSM-NA and SAMPDI methods applied on different training and test set. More ...
A) Mutant counts per wild-type (wt) -> mutant (mut) amino acid substitutions and B) Mutant type aver...
The increasing demand for the identification of genetic variation responsible for common diseases ha...
<p>Contribution of each source database to the training datasets. The table shows the number of SNPs...
<div><p>Single nucleotide polymorphisms (SNPs) are among the most common types of genetic variation ...
<p>(A) Number of SNPs distributed per unigene; (B) Classification of different substitution types of...
Single nucleotide polymorphisms (SNPs) are among the most common types of genetic variation in compl...
Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding ...
Training and testing of conventional machine learning models on binary classification problems depen...
The increasing demand for the identification of genetic variation responsible for common diseases ha...
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