Julia Pape-Neumann, Muna van Ermingen-Marbach, Marion Grande, Klaus Willmes, Stefan Heim
The role of phonological awareness in treatments of dyslexic primary school children
Acta neurobiologiae experimentalis
since 2017 used iontorrent ngs platform hospital diagnose treat cancer analyzing variants run requires considerable time still struggling variants appear correct metrics first found negative upon investigation machine learning algorithm ml help us classify ngs variants led us investigate ml fit ngs data develop tool routinely implemented help biologists currently one greatest challenges medicine processing significant quantity data particularly true molecular biology advantage nextgeneration sequencing ngs profiling identifying molecular tumors treatment addition bioinformatics pipelines artificial intelligence ai valuable helping analyze mutation variants generating sequencing data patient dna samples become easy perform clinical trials however analyzing massive quantities genomic transcriptomic data extracting key biomarkers associated clinical response specific therapy requires formidable combination scientific expertise biomolecular skills panel bioinformatic biostatistic tools artificial intelligence successful developing future routine diagnostics however cancer genome complexity technical artifacts make identifying real variants challenging present machine learning method classifying pathogenic single nucleotide variants snvs single nucleotide polymorphisms snps multiple nucleotide variants mnvs insertions deletions detected ngs different types tumor specimens colorectal melanoma lung glioma cancer compared ngs data different machine learning algorithms using kfold crossvalidation method neural networks deep learning measure performance different ml algorithms determine one valid model confirming ngs variant calls cancer diagnosis trained machine learning 70 data samples extracted local database data structure 7 parameters chromosome position exon variant allele frequency minor allele frequency coverage protein description validated 30 remaining data model offering best accuracy chosen implemented ngs analysis routine artificial intelligence developed r script language version 360 trained model 70 102011 variants best error rate 022 found random forest machine learning ntree 500 mtry 4 auc 099 neural networks achieved good scores final trained model neural network achieved accuracy 98 rocauc 099 validation data tested rf model interpret 2000 variants ngs database 20 variants misclassified error rate 1 errors nomenclature problems false positives adding false positives training database implementing rf model routinely error rate always 05 rf model shows excellent results oncosomatic ngs interpretation easily implemented molecular biology laboratories ai becoming increasingly important molecular biomedical analysis helpful processing medical data neural networks show good capacity variant classification future may useful predicting complex variants