&lt;title&gt;Recent developments of the SNNS neural network simulator&lt;/title&gt;

Zell, Andreas; Mache, Niels; Sommer, Tilman; Korb, Thomas

doi:10.1117/12.45009

Cited by 10 publications

(3 citation statements)

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“…The LDA-based models were employed for feature selection. The non-linear model is based on neural networks (NN) as implemented in the SNNS package [ 40 ]. Multiple NN architectures were evaluated.…”

Section: Methodsmentioning

confidence: 99%

MutaCYP: Classification of missense mutations in human cytochromes P450

Fechter

Porollo

2014

BMC Med Genomics

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BackgroundCytochrome P450 monooxygenases (CYPs) represent a large and diverse family of enzymes involved in various biological processes in humans. Individual genome sequencing has revealed multiple mutations in human CYPs, and many missense mutations have been associated with variety of diseases. Since 3D structures are not resolved for most human CYPs, there is a need for a reliable sequence-based prediction that discriminates benign and disease causing mutations.MethodsA new prediction method (MutaCYP) has been developed for scoring de novo missense mutations to have a deleterious effect. The method utilizes only five features, all of which are sequence-based: predicted relative solvent accessibility (RSA), variance of predicted RSA among the residues in close sequence proximity, Z-score of Shannon entropy for a given position, difference in similarity scores and weighted difference in size between wild type and new amino acids. The method is based on a single neural network.ResultsMutaCYP achieves MCC = 0.70, Q2 = 88.52%, Recall = 93.40% with Precision = 91.09%, and AUC = 0.909. Comparative evaluation with other existing methods indicates that MutaCYP outperforms SIFT and PolyPhen-2. Predictions by MutaCYP appear to be orthogonal to predictions by the evaluated methods. Potential issues on reliability of annotations of mutations in the existing databases are discussed.ConclusionsA new accurate method, MutaCYP, for classification of missense mutations in human CYPs is presented. The prediction model consists of only five sequence-based features, including a real-valued predicted relative solvent accessibility. The method is publicly available at http://research.cchmc.org/MutaSense/.

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Section: Methodsmentioning

confidence: 99%

MutaCYP: Classification of missense mutations in human cytochromes P450

Fechter

Porollo

2014

BMC Med Genomics

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“…The Stuttgart Neural Network Simulator (SNNS) [23] was used for neural network analysis. All attribute values were normalized by the value two standard deviations higher than the respective means observed in the training set, in order to constrain all values to lie in the range between 0 and ~1.…”

Section: Methodsmentioning

confidence: 99%

Tandem machine learning for the identification of genes regulated by transcription factors

et al. 2005

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BackgroundThe identification of promoter regions that are regulated by a given transcription factor has traditionally relied upon the identification and distributions of binding sites recognized by the factor. In this study, we have developed a tandem machine learning approach for the identification of regulatory target genes based on these parameters and on the corresponding binding site information contents that measure the affinities of the factor for these cognate elements.ResultsThis method has been validated using models of DNA binding sites recognized by the xenobiotic-sensitive nuclear receptor, PXR/RXRα, for target genes within the human genome. An information theory-based weight matrix was first derived and refined from known PXR/RXRα binding sites. The promoter region of candidate genes was scanned with the weight matrix. A novel information density-based clustering algorithm was then used to identify clusters of information rich sites. Finally, transformed data representing metrics of location, strength and clustering of binding sites were used for classification of promoter regions using an ensemble approach involving neural networks, decision trees and Naïve Bayesian classification. The method was evaluated on a set of 24 known target genes and 288 genes known not to be regulated by PXR/RXRα. We report an average accuracy (proportion of correctly classified promoter regions) of 71%, sensitivity of 73%, and specificity of 70%, based on multiple cross-validation and the leave-one-out strategy. The performance on a test set of 13 genes showed that 10 were correctly classified.ConclusionWe have developed a machine learning approach for the successful detection of gene targets for transcription factors with high accuracy. The method has been validated for the transcription factor PXR/RXRα and has the potential to be extended to other transcription factors.

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“…Neural Network. We used the JavaNNS neural network simulator [23] to build a backpropagation neural network. The number of input nodes equaled the size of the feature.…”

Section: Experimental Set-upmentioning

confidence: 99%