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

Hao Chen1, Dipan Shaw1, Jianyang Zeng2

  • 1Department of Compute Science and Engineering, University of California, Riverside, CA, USA.

Bioinformatics (Oxford, England)
|September 13, 2019
PubMed
Summary
This summary is machine-generated.

Predicting alternative splicing isoform functions is crucial. DIFFUSE, a novel deep learning approach, accurately predicts isoform functions using sequence and expression data, outperforming existing methods.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Alternative splicing generates diverse protein isoforms from a single gene, increasing genomic functional complexity.
  • Understanding specific isoform functions is essential but challenging due to limited isoform-level annotations and data integration difficulties.

Purpose of the Study:

  • To develop a novel computational approach for accurately predicting isoform functions by integrating sequence and expression data.
  • To address the scarcity of isoform-specific functional annotations using a semi-supervised learning strategy.

Main Methods:

  • Introduced DIFFUSE (Deep learning-based prediction of IsoForm FUnctions from Sequences and Expression), a hybrid deep neural network (DNN) and conditional random field (CRF) framework.
  • Employed an iterative semi-supervised learning algorithm to train the DNN and CRF models, overcoming the lack of ground truth labels.
  • Integrated genomic sequences and isoform co-expression relationships for function prediction.

Main Results:

  • DIFFUSE achieved high performance in predicting isoform and gene functions, with an average Area Under the Receiver Operating Characteristics Curve (AUC) of 0.840 and Area Under the Precision-Recall Curve (AUPRC) of 0.581 across 4184 Gene Ontology (GO) categories.
  • Demonstrated significantly superior performance compared to state-of-the-art methods.
  • Validated prediction accuracy through analysis of functional, sequence, expression, and structural similarities, and consistency with known isoform features.

Conclusions:

  • DIFFUSE provides an effective computational solution for predicting isoform functions, leveraging diverse data types.
  • The developed semi-supervised approach successfully addresses the challenge of limited isoform-level functional annotations.
  • The findings advance our ability to understand the functional landscape of alternative splicing and its contribution to genomic diversity.