Transfer learning

In this paper we examine the relevance of transfer learning in deep learning context, we review different techniques that are used when executing and improving transfer learning tasks and we review the uses of transfer learning in cases such as computer vision, where learning requires a lot of data, computing resources and time and therefore this problem can be solved with transfer learning. After examining the uses and methods of transfer learning, experiments that were done in other research papers as well as experiments done for this paper, a conclusion became apparent that transfer learning can decrease the amount of data required, speed up the training process as well as increase the accuracy of neural networks

Galaxy Dnpatterntools for Computational Analysis of Nucleosome Positioning Sequence Patterns

Nucleosomes are basic units of DNA packing in eukaryotes. Their structure is well conserved from yeast to human and consists of the histone octamer core and 147 bp DNA wrapped around it. Nucleosomes are bound to a majority of the eukaryotic genomic DNA, including its regulatory regions. Hence, they also play a major role in gene regulation. For the latter, their precise positioning on DNA is essential. In the present paper, we describe Galaxy dnpatterntools—software package for nucleosome DNA sequence analysis and mapping. This software will be useful for computational biologists practitioners to conduct more profound studies of gene regulatory mechanisms