Hari Krishna
About Hari Krishna
Hari Krishna is an Associate Scientific Manager at Innoplexus, specializing in ontology development for biological pathways and biomarker identification. He holds a PhD in Bioinformatics from Pondicherry University and has extensive experience in data delivery projects and multi-omics analysis.
Work at Innoplexus
Currently, Hari Krishna serves as an Associate Scientific Manager at Innoplexus, a position he has held since 2018. In this role, he is responsible for validating and developing Ontology for biological pathways. His work involves extensive data delivery projects, focusing on treatment guidelines, key opinion leaders, and drug sentiment analysis. His contributions are integral to the company's efforts in advancing scientific research and data analytics in the life sciences sector.
Education and Expertise
Hari Krishna completed his Doctor of Philosophy (PhD) in Bioinformatics at Pondicherry University from 2013 to 2018. He also earned a Master of Science (MS) in the same field from the same institution. His educational background equips him with a strong foundation in bioinformatics, machine learning, and data analysis, which he applies in his current and previous roles.
Background in Research and Academia
Before joining Innoplexus, Hari Krishna held various academic and research positions. He worked as a Junior Research Fellow at the Indian Institute of Technology, Madras from 2010 to 2012, and later served as an Assistant Professor at Vignan's Foundation of Science, Technology & Research from 2017 to 2018. Additionally, he was a Research Fellow at Pondicherry University from 2013 to 2017, where he developed a pipeline to identify novel biomarkers based on correlation networks of gene expression.
Achievements in Biomarker Identification
Hari Krishna has made significant contributions to the field of biomarker identification. He has developed a pipeline for target-based drug repurposing using a machine learning approach in metabolomics. His work also includes applying machine learning models specifically for biomarker identification and being involved in the development of a multi-omics pipeline for analyzing complex datasets such as Microarray and NGS.