Uddin, M. R. | bioRxiv Science

bioRxiv2019

eMED-DNA: An in silico operating system for clinical medical data storage within the human genome

The propitious developments in molecular biology and next generation sequencing have enabled the possibility for DNA storage technologies. However, the full application and power of our genomic revolution have not been fully utilized in clinical medicine given a lack of transition from research to real world clinical practice. This has identified an increasing need for an operating system which allows for the transition from research to clinical use. We present eMED-DNA, an in silico operating system for archiving and managing all forms of electronic health records (EHRs) within ones own copy of the sequenced genome to aid in the application and integration of genomic medicine within real world clinical practice. We incorporated an efficient and sophisticated in-DNA file management system for the lossless management of EHRs within a genome. This represents the first in silico integrative system which would bring closer the utopian ideal for integrating genotypic data with phenotypic clinical data for future medical practice.

bioinformatics↗

bioRxiv2019

SAINT: Self-Attention AugmentedInception-Inside-Inception Network Improves ProteinSecondary Structure Prediction

MotivationProtein structures provide basic insight into how they can interact with other proteins, their functions and biological roles in an organism. Experimental methods (e.g., X-ray crystallography, nuclear magnetic resonance spectroscopy) for predicting the secondary structure (SS) of proteins are very expensive and time consuming. Therefore, developing efficient computational approaches for predicting the secondary structure of protein is of utmost importance. Advances in developing highly accurate SS prediction methods have mostly been focused on 3-class (Q3) structure prediction. However, 8-class (Q8) resolution of secondary structure contains more useful information and is much more challenging than the Q3 prediction. ResultsWe present SAINT, a highly accurate method for Q8 structure prediction, which incorporates self-attention mechanism (a concept from natural language processing) with the Deep Inception-Inside-Inception (Deep3I) network in order to effectively capture both the short-range and long-range interactions among the amino acid residues. SAINT offers a more interpretable framework than the typical black-box deep neural network methods. Through an extensive evaluation study, we report the performance of SAINT in comparison with the existing best methods on a collection of benchmark datasets, namely, TEST2016, TEST2018, CASP12 and CASP13. Our results suggest that self-attention mechanism improves the prediction accuracy and outperforms the existing best alternate methods. SAINT is the first of its kind and offers the best known Q8 accuracy. Thus, we believe SAINT represents a major step towards the accurate and reliable prediction of secondary structures of proteins. AvailabilitySAINT is freely available as an open source project at https://github.com/SAINTProtein/SAINT.

bioinformatics↗