Computational Techniques to Annotate Macro-Molecules for Personalized Medicine

Personalized medicine is one of the broader promises of the Bioinformatics field. It enables a doctor to analyze a patient's genetic profile and prescribe the best available drug therapy and dosage that is specific to that patient. A major step towards personalized medicine is the precise annotation of an avalanche of molecular sequences that remain uncharacterized. This Research Topic will focus on the use of computational techniques to fulfil the promise of personalized medicine, by integrating heterogeneous types of data: including multi-omics data, protein interaction networks, protein structural information, homolog data, etc. Of special interest are models and methods that target and enhance protein function understanding, which will help the scientific community in designing new drugs and therapies.

We welcome manuscripts in the following areas:
• Design of machine learning models for protein function prediction by utilizing molecular data;
• Data analytics and mining of molecular data for annotating proteins;
• Computational techniques aimed at providing better insights for protein functions, including their biomolecular interactions, signaling networks, and structures etc.;
• Processing and function of biologically important macromolecules and complexes;
• Algorithms to understand structure function relationship of proteins with an emphasis on molecular basis of disease.

Keywords: Machine learning, Protein function, microscopic protein images, gene sequences

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.