Integrated Systems Genomic Approaches for Characterizing Uncharacterized Proteins

The application of systems genomic approaches to uncharacterized proteins have begun yielding tremendous knowledge. This is largely due to advances in next-generation sequencing approaches that have brought great success in not only identifying candidate genes, but also in identifying founder mutations responsible for such candidates.

A well-known facet of characterising the uncharacterized, or hypothetical, proteins is through the development of annotation and curation-based approaches integrating machine learning heuristics. Many classification scoring schemes, annotation algorithms have been developed keeping under this purview and yet, there is a want for better dissemination of identifying the function of such uncharacterised proteins. This Research Topic will aim to gather articles in such contexts and bridge the gap between the characterised and uncharacterised protein function.

Themes of interest include, but are not limited to:
• Bioinformatics and Machine Learning Algorithms for Protein Function;
• Systems Biology Integration;
• Hypothetical Proteins to Pseudogenes;
• Problems and Challenges in Annotating the Uncharacterised Proteins;
• Emerging Approaches to Classify the Candidate Proteins.