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DEGnext: classification of differentially expressed genes from RNA-seq data using a convolutional neural network with transfer learning by Tulika Kakati; Dhruba K. Bhattacharyya; Jugal K. Kalita; Trina M. Norden-Krichmar is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is DEGnext: classification of differentially expressed genes from RNA-seq data using a convolutional neural network with transfer learning about?

## Abstract ## Background A limitation of traditional differential expression analysis on small datasets involves the possibility of false positives and false negatives due to sample variation. Considering the recent advances in deep learning (DL) based models, we wanted to expand the state-of-the-art in disease biomarker prediction from RNA-seq data using DL. However, application of DL to RNA-seq data is challenging due to absence of appropriate labels and smaller sample size as compared to number of genes. Deep learning coupled with transfer learning can improve prediction performance on novel data by incorporating patterns learned from other related data. With the emergence of new disease datasets, biomarker prediction would be facilitated by having a generalized model that can transfer the knowledge of trained feature maps to the new dataset. To the best of our knowledge, there is no Convolutional Neural Network (CNN)-based model coupled with transfer learning to predict the significant upregulating (UR) and downregulating (DR) genes from both trained and untrained datasets. ## Results We implemented a CNN model, DEGnext, to predict UR and DR genes from gene expression data obt

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Author

Tulika Kakati; Dhruba K. Bhattacharyya; Jugal K. Kalita; Trina M. Norden-Krichmar

Publisher

Springer Science and Business Media LLC

Published

2022

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)