Neural Networks for molecular biological classification
Taxonomic classification, i.e., the identification and assignment to groups of biological organisms with the same origin and characteristics, is a common task in genetics. Nowadays, taxonomic classification is mainly based on genome similarity search to large genome databases. In this process, the classification quality depends heavily on the database since representative relatives have to be known already. Many genomic sequences cannot be classified at all or only with a high misclassification rate. Here we present several programs using Deep Neural Networks to precisely classify DNA, RNA and protein sequences.
Prof. Manja Marz
Friedrich Schiller University Jena, Germany
Manja Marz is Professor of Bioinformatics at Friedrich-Schiller University Jena. Since 2015 she is the group leader of the associated research group „Non-coding RNAs in Aging: The Regulation of Aging“ at the Leibniz Institute on Aging – Fritz Lipmann Institute. Manja Marz’s research interests are divided into several areas: High Throughput Sequencing Analysis, Identification and Annotation of Non-coding RNAs, Bioinformatic Analysis and System Biology of Viruses, Comparative Genomics and Algorithmic Bioinformatics and Phylogenetic Analysis.