Application of Computational Techniques for Bioinformatics and Health Informatics
Accurately establishing the connection between a protein sequence and its function remains a focal point within the field of protein engineering, especially in the context of predicting the effects of mutations. From this, there has been a continued drive to build accurate and reliable predictive models via machine learning that allow for the virtual screening of many protein mutant sequences, measuring the relationship between sequence and ‘fitness’ or ‘activity’, commonly known as a Sequence-Activity-Relationship (SAR). Work focus is on providing an efficient software solution to find the optimal arrangement of structural and or physiochemical properties to encode their specific mutant library dataset.
In the domain of health informatics, goal is to provide reliable assistive deep learning based tools to assist the human-machine interaction (Brain Computer Interaction).
Parasnath Dubey, Sandhya Dr, "Application of Computational Techniques for Bioinformatics and Health Informatics" (2022). Technical Collection. 90.