Measurement of Conditional Relatedness Between Genes Using Fully Convolutional Neural Network.

ABSTRACT: Measuring conditional relatedness, the degree of relation between a pair of genes in a certain condition, is a basic but difficult task in bioinformatics, as traditional co-expression analysis methods rely on co-expression similarities, well known with high false positive rate. Complement with prior-knowledge similarities is a feasible way to tackle the problem. However, classical combination machine learning algorithms fail in detection and application of the complex mapping relations between similarities and conditional relatedness, so a powerful predictive model will have enormous benefit for measuring this kind of complex mapping relations. To this need, we propose a novel deep learning model of convolutional neural network with a fully connected first layer, named fully convolutional

SUBMITTER: Wang Y 

PROVIDER: S-EPMC6818468 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature