Isoform Function Prediction Using Deep Neural Network

  title={Isoform Function Prediction Using Deep Neural Network},
  author={Sara Ghazanfari and Ali Rasteh and Seyed Abolfazl Motahari and Mahdieh Soleymani Baghshah},
Isoforms are mRNAs produced from the same gene site in the phe-nomenon called Alternative Splicing. Studies have shown that more than 95% of human multi-exon genes have undergone alternative splicing. Although there are few changes in mRNA sequence, They may have a sys-tematic effect on cell function and regulation. It is widely reported that isoforms of a gene have distinct or even contrasting functions. Most studies have shown that alternative splicing plays a significant role in human health… 

Figures and Tables from this paper



DIFFUSE: predicting isoform functions from sequences and expression profiles via deep learning

This study presents a novel approach, DIFFUSE (Deep learning-based prediction of IsoForm FUnctions from Sequences and Expression), to predict isoform functions, and adopts a hybrid framework by first using a deep neural network to predict the functions of isoforms from their genomic sequences and then refining the prediction using a conditional random field (CRF) based on co-expression relationship.

DeepIsoFun: a deep domain adaptation approach to predict isoform functions

A novel deep learning method is proposed, DeepIsoFun, that combines multiple instance learning with domain adaptation and is trained on a deep neural network architecture so that it can adapt to different expression distributions associated with different gene ontology terms.

High-resolution functional annotation of human transcriptome: predicting isoform functions by a novel multiple instance-based label propagation method

By integrating multiple human RNA-seq data sets, the first systematic prediction of isoform functions is carried out, enabling high-resolution functional annotation of human transcriptome and identifying the apoptosis regulation function of the famous ‘TP53’ gene.

Functional Annotation of Human Protein Coding Isoforms via Non-convex Multi-Instance Learning

This paper proposes a novel approach to differentiate the functions of PCIs by integrating sparse simplex projection---that is, a nonconvex sparsity-inducing regularizer---with the framework of multi-instance learning (MIL).

DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier

This work has developed a novel method to predict protein function from sequence that uses deep learning to learn features from protein sequences as well as a cross-species protein–protein interaction network.

Understanding mechanisms underlying human gene expression variation with RNA sequencing

It is demonstrated that eQTLs near genes generally act by a mechanism involving allele-specific expression, and that variation that influences the inclusion of an exon is enriched within and near the consensus splice sites.

ProtVec: A Continuous Distributed Representation of Biological Sequences

By only providing sequence data for various proteins into this model, information about protein structure can be determined with high accuracy, and this so-called embedding model needs to be trained only once and can be used to ascertain a diverse set of information regarding the proteins of interest.

Subcellular localization of adenosine kinase in mammalian cells: The long isoform of AdK is localized in the nucleus.