Thus using minimum variance multiplier will be a more sensitive method for mutation prediction with multi-copy sequences.
This means that even though a prediction method is tested on mutations unseen during training, different mutations of the same protein, and even the same residue, can be found in both training and test folds.
We implemented four standard machine learning algorithms: random forest, Bayesian additive regression tree, support vector machine and logistic regression, and compared their performance to each other and to standard methods for somatic mutation prediction.
Taken together, our prediction method identified several ERGs with mutation alteration profiles characteristic of classical TSGs.
Thus, if a prediction method cannot generalise well for mutations in previously unseen non-homologous proteins, it is unlikely to achieve a good performance under this evaluation.
Although these methods are far from perfect for mutation prediction, they do show the prospects for molecular diagnostics in ADPKD.
To further validate these results we implemented HMM based statistical prediction method to identify the functionally significant point mutations using PANTHER server.
However, there is no prediction method available designed specifically for analysis of missense mutations in CYPs, despite the importance of these enzymes in human health and their direct clinical relevance.
A new prediction method (MutaCYP) has been developed for scoring de novo missense mutations to have a deleterious effect.
To develop methods for optimal somatic mutation detection and to identify factors influencing somatic mutation prediction accuracy, we validated predictions from three somatic mutation detection algorithms, MuTect, JointSNVMix2 and SomaticSniper, by Sanger sequencing.
