Photo of Priyank Shukla

Priyank Shukla

Dr

  • Northlands Road, Magee Campus

    BT48 7JL Londonderry

    United Kingdom

  • Source: Scopus
  • Calculated based on no. of publications stored in Pure and citations from Scopus
20102020

Research output per year

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Personal profile

Biography

Dr Priyank Shukla was appointed to his current post as a Lecturer in Stratified Medicine (Bioinformatics) at Ulster University in July 2016 and is a member of the Northern Ireland Centre for Stratified Medicine research group.

Dr Shukla earned his BSc in Biotechnology (2001-2004) from Bareilly College (MJPR University, Bareilly, India) and MSc in Bioinformatics (2004-2006) from University Institute of Engineering & Technology (CSJM University, Kanpur, India). He then joined Laboratory of Genomics at Department of Histology Embryology and Applied Biology, University of Bologna for three months as a Visiting Researcher to co-work on a bioinformatics project aimed to full parsing of Genbank database.

He completed a PhD in Computer Science (area of research: Machine Learning and Bioinformatics) at Bologna Biocomputing Group under the supervision of Professor Rita Casadio, where he developed Machine Learning based methods for prediction of disulphide bonding states of cysteine residues in proteins.

Dr Shukla undertook a Postdoctoral Scientist (Bioinformatics) position at Department of Biomedical Sciences, University of Veterinary Medicine, Vienna, Austria (2010-2016), where he was responsible for consulting, data analysis and data management of Next Generation Sequencing (NGS) based projects of Jak-Stat Signalling Consortium. His research focused on applying NGS approaches to understand Epithelial to Mesenchymal Transition (EMT) and Jak-Stat Signalling – linking infection, inflammation and cancer.

Research Interests

Dr Shukla aims in developing Machine Learning – specifically Artificial Neural Networks (ANN), Hidden Markov Models (HMM) and Support Vector Machines (SVM) based computational methods for insilico biomarker discovery and patients’ stratification in inflammatory diseases and cancer via exploiting high-throughput omics data (genomics, transcriptomics, proteomics & metabolomics).

Teaching Interests

Undergraduate Teaching

  • BIO337: Mathematical and Computational Methods – 2
  • BIO535: Insilico Genomics Proteomics & Metabolomics Analysis Methods
  • BIO540: Clinical Research Project
  • BIO541: Biomedical Informatics (Module Coordinator)

Postgraduate Teaching

  • BIO831: Biomedical Informatics (Module Coordinator)
  • BIO833: Research Project

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