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Dr. Arvind Mer obtained his Bachelor of Technology (specialization in biotechnology) degree in 2007. He completed a Master of Technology degree in Computational and Systems biology from Jawaharlal Nehru University, India, in 2009. He was awarded the Helmholtz Graduate School International PhD fellowship at the Max Delbrück Center for Molecular Medicine, Germany. In 2014, Dr. Mer completed his PhD in bioinformatics and joined Karolinska Institute, Sweden for postdoctoral training. In 2016, he started working as postdoctoral fellow at the Princess Margaret Cancer Centre, University Health Network, Toronto, and subsequently became an affiliate scientist at the institute. He joined the University of Ottawa in 2021. Dr. Mer’s lab is using cutting-edge machine learning and artificial intelligence methods for genomic data mining and advancing personalized medicine.
High throughput omics technologies are revolutionizing our knowledge of molecular mechanism and disease biology. These profiling methods produce a large amount of data. Analyzing and mining this data for biological knowledge is very challenging. Our group is developing machine learning and artificial intelligence methods for high throughput omics data analysis.
Personalized medicine in cancer: In personalized medicine, therapeutic strategies are tailored based on the patient's genomic profile. The promise of personalized cancer medicine is to provide a precise and rapid treatment decision. The key challenge in cancer personalized medicine is to identify biomarkers that can predict drug response from the genomic profile of a tumor. Our research program aims to address these challenges using machine learning and artificial intelligence methods.
Single cell sequencing data analysis:
Next-generation single cell sequencing technology provides a higher resolution information about individual cells. This allows us to characterize the heterogeneity of biological samples, understand cellular mechanisms and identify the sources of drug resistance in disease. We are developing machine learning based computational methods for single cell level sequencing data analysis.
Drug development is very expensive and time consuming. It takes approximately 12 years and 2.6 billion dollars to develop a new drug. Discovering new uses for approved or investigational compounds (drug repurposing) could decrease both the time-frame and the costs associated with drug development. We are using pharmacogenomic datasets, chemoinformatics and machine learning to find novel uses of existing drugs.
Students and postdoctoral fellows who are interested in the fields of computational biology, machine learning, omics data analysis, bioinformatics, cancer genomics are encouraged to contact Dr. Arvind Mer .
Complete list of publications at Google scholar
- Mer AS, Heath E, Tonekaboni S, Garcia-Prat L, Shlush L, Voisin V, Bader G, Lupien M, Dick J, Minden M, Schimmer A, Haibe-Kains B, Biological and therapeutic implications of a unique subtype of NPM1 mutated AML, Nature Communications, Jan. 2021. PMID:33594052
- Mer AS, Ba-alawi W, Smirnov P, Wang XY, Brew B, Ortmann J, Tsao MS, Cescon D, Goldenberg A, Haibe-Kains B, Integrative Pharmacogenomics Analysis of Patient Derived Xenografts, Cancer Research, May 2019. PMID:31142512
- Mer AS, Lindberg L, Nilsson C, Klevebring D, Wang M, Grönberg H, Lehmann S, Rantalainen M, Expression levels of long non-coding RNAs are prognostic for AML outcome, Journal of Hematology & Oncology 11(1), 2018. PMID: 29625580
- Mer AS, Klevebring D, Gronberg H, Rantalainen M, Study design requirements for RNA sequencing-based breast cancer diagnostics, Scientific Reports, (6:20200), 2016