Manuscript Topics

Technological advances in multi-omics (genomics, transcriptomics, and proteomics) have led to a deluge of molecular data from a rapidly growing number of biological samples. The rapid increase in data dimension is a challenge for traditional analysis methods. For this purpose, deep learning naturally appears as one of the main drivers of progress. They are able to extract useful patterns hidden in the large-scale data and make effective use of these patterns to perform accurate predictions on unseen data. In recent years, bioinformatics has already induced significant new developments of general interest in deep learning, for example in the context of learning with structured data, graph inference, semi-supervised learning, and novel combinations of optimization and learning algorithms.

In this special issue, we will explore the potential of applying deep learning and related computational techniques to mine and model a significant amount of biological sequence data for structure and functional analysis. Possible research topics include but are not limited to:

• Modelling and analysis of gene expression data;
• Prediction and analysis of gene regulatory elements;
• Reconstruction and inference of biological networks;
• Prediction of protein function, protein-protein interactions and interaction sites;
• Identification of essential genes and biomarkers for disease diagnosis and prognosis.

Keywords:
Artificial intelligence; Biological sequence analysis; Deep learning; Biological sequence structure prediction; Functional site identification.

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