I am a PhD Candidate in Biomedical Informatics at Stanford University School of Medicine. I graduated from the Indian Institute of Technology, Kharagpur under the Dual Degree Programme - Bachelor (Honours) and Master of Technology, in Biotechnology and Biochemical Engineering. During my stay at IIT, I had qualified for the prestigious Google Summer of Code Program for 3 successive years. I have contributed to Drupal, an open-source content management platform, and Genome Informatics - Reactome Project, a knowledgebase of biological pathways.

I am interested to research at the intersection of Biosciences, Big Data and the Web. After my graduation, I had joined the Digital Enterprise Research Institute (DERI), Ireland under its Health Care and Life Sciences Unit. I was responsible for the development of user-driven platforms, facilitating intuitive data exploration, for the EU FP7 GRANATUM Project, Linked TCGA Project and the Ireland's Open Data Initiative ( I was a part of the team, which won the Best Paper Award at CSHALS 2014 and the Semantic Web Challenge Award (Big Data Prize) at ISWC 2013.

Honors & Awards

  • NIH/NLM Travel Award, 21st Pacific Symposium on Biocomputing (January 2016)
  • Best Project Award (Graphic Design), Stanford Biomedical Informatics Program (September 2015)
  • Best Paper Award, 7th Conference on Semantics in Healthcare and Life Sciences (February 2014)
  • Semantic Web Challenge Award (Big Data Prize), 12th International Semantic Web Conference (October 2013)
  • Best Project Award, 10th Summer School on Ontology Engineering and the Semantic Web (July 2013)
  • Best Poster Award, 10th Summer School on Ontology Engineering and the Semantic Web (July 2013)
  • Google Summer of Code Student (Genome Informatics - Reactome), Google Inc. (August 2012)
  • Honourable Mention in Technology, Indian Institute of Technology (IIT), Kharagpur (April 2012)
  • Best Outgoing Student (Technology), Meghnad Saha Hall of Residence, IIT Kharagpur (April 2012)
  • Google Summer of Code Student (Genome Informatics - Reactome), Google Inc. (August 2011)
  • Google Summer of Code Student (Drupal), Google Inc. (August 2010)
  • Xavierite Super Award, St. Xavier’s High School, Ahmedabad (February 2007)

All Publications

  • A Systematic Analysis on Term Reuse and Term Overlap across Biomedical Ontologies Semantic Web - Interoperability, Usability, Applicability Kamdar, M. R., Tudorache, T., Musen, M. A. 2016
  • PRISM: A DATA-DRIVEN PLATFORM FOR MONITORING MENTAL HEALTH. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Kamdar, M. R., Wu, M. J. 2016; 21: 333-344


    Neuropsychiatric disorders are the leading cause of disability worldwide and there is no gold standard currently available for the measurement of mental health. This issue is exacerbated by the fact that the information physicians use to diagnose these disorders is episodic and often subjective. Current methods to monitor mental health involve the use of subjective DSM-5 guidelines, and advances in EEG and video monitoring technologies have not been widely adopted due to invasiveness and inconvenience. Wearable technologies have surfaced as a ubiquitous and unobtrusive method for providing continuous, quantitative data about a patient. Here, we introduce PRISM-Passive, Real-time Information for Sensing Mental Health. This platform integrates motion, light and heart rate data from a smart watch application with user interactions and text entries from a web application. We have demonstrated a proof of concept by collecting preliminary data through a pilot study of 13 subjects. We have engineered appropriate features and applied both unsupervised and supervised learning to develop models that are predictive of user-reported ratings of their emotional state, demonstrating that the data has the potential to be useful for evaluating mental health. This platform could allow patients and clinicians to leverage continuous streams of passive data for early and accurate diagnosis as well as constant monitoring of patients suffering from mental disorders.

    View details for PubMedID 26776198

  • An Ebola virus-centered knowledge base. Database : the journal of biological databases and curation Kamdar, M. R., Dumontier, M. 2015; 2015


    Ebola virus (EBOV), of the family Filoviridae viruses, is a NIAID category A, lethal human pathogen. It is responsible for causing Ebola virus disease (EVD) that is a severe hemorrhagic fever and has a cumulative death rate of 41% in the ongoing epidemic in West Africa. There is an ever-increasing need to consolidate and make available all the knowledge that we possess on EBOV, even if it is conflicting or incomplete. This would enable biomedical researchers to understand the molecular mechanisms underlying this disease and help develop tools for efficient diagnosis and effective treatment. In this article, we present our approach for the development of an Ebola virus-centered Knowledge Base (Ebola-KB) using Linked Data and Semantic Web Technologies. We retrieve and aggregate knowledge from several open data sources, web services and biomedical ontologies. This knowledge is transformed to RDF, linked to the Bio2RDF datasets and made available through a SPARQL 1.1 Endpoint. Ebola-KB can also be explored using an interactive Dashboard visualizing the different perspectives of this integrated knowledge. We showcase how different competency questions, asked by domain users researching the druggability of EBOV, can be formulated as SPARQL Queries or answered using the Ebola-KB Dashboard.Database URL:

    View details for DOI 10.1093/database/bav049

    View details for PubMedID 26055098

  • ReVeaLD: A user-driven domain-specific interactive search platform for biomedical research JOURNAL OF BIOMEDICAL INFORMATICS Kamdar, M. R., Zeginis, D., Hasnain, A., Decker, S., Deus, H. F. 2014; 47: 112-130


    Bioinformatics research relies heavily on the ability to discover and correlate data from various sources. The specialization of life sciences over the past decade, coupled with an increasing number of biomedical datasets available through standardized interfaces, has created opportunities towards new methods in biomedical discovery. Despite the popularity of semantic web technologies in tackling the integrative bioinformatics challenge, there are many obstacles towards its usage by non-technical research audiences. In particular, the ability to fully exploit integrated information needs using improved interactive methods intuitive to the biomedical experts. In this report we present ReVeaLD (a Real-time Visual Explorer and Aggregator of Linked Data), a user-centered visual analytics platform devised to increase intuitive interaction with data from distributed sources. ReVeaLD facilitates query formulation using a domain-specific language (DSL) identified by biomedical experts and mapped to a self-updated catalogue of elements from external sources. ReVeaLD was implemented in a cancer research setting; queries included retrieving data from in silico experiments, protein modeling and gene expression. ReVeaLD was developed using Scalable Vector Graphics and JavaScript and a demo with explanatory video is available at A set of user-defined graphic rules controls the display of information through media-rich user interfaces. Evaluation of ReVeaLD was carried out as a game: biomedical researchers were asked to assemble a set of 5 challenge questions and time and interactions with the platform were recorded. Preliminary results indicate that complex queries could be formulated under less than two minutes by unskilled researchers. The results also indicate that supporting the identification of the elements of a DSL significantly increased intuitiveness of the platform and usability of semantic web technologies by domain users.

    View details for DOI 10.1016/j.jbi.2013.10.001

    View details for Web of Science ID 000333004500012

    View details for PubMedID 24135450

  • Functional Characterization of Two Structurally Novel Diacylglycerol Acyltransferase2 Isozymes Responsible for the Enhanced Production of Stearate-Rich Storage Lipid in Candida tropicalis SY005. PloS one Dey, P., Chakraborty, M., Kamdar, M. R., Maiti, M. K. 2014; 9 (4)


    Diacylglycerol acyltransferase (DGAT) activity is an essential enzymatic step in the formation of neutral lipid i.e., triacylglycerol in all living cells capable of accumulating storage lipid. Previously, we characterized an oleaginous yeast Candida tropicalis SY005 that yields storage lipid up to 58% under a specific nitrogen-stress condition, when the DGAT-specific transcript is drastically up-regulated. Here we report the identification, differential expression and function of two DGAT2 gene homologues--CtDGAT2a and CtDGAT2b of this C. tropicalis. Two protein isoforms are unique with respect to the presence of five additional stretches of amino acids, besides possessing three highly conserved motifs known in other reported DGAT2 enzymes. Moreover, the CtDGAT2a and CtDGAT2b are characteristically different in amino acid sequences and predicted protein structures. The CtDGAT2b isozyme was found to be catalytically 12.5% more efficient than CtDGAT2a for triacylglycerol production in a heterologous yeast system i.e., Saccharomyces cerevisiae quadruple mutant strain H1246 that is inherently defective in neutral lipid biosynthesis. The CtDGAT2b activity rescued the growth of transformed S. cerevisiae mutant cells, which are usually non-viable in the medium containing free fatty acids by incorporating them into triacylglycerol, and displayed preferential specificity towards saturated acyl species as substrate. Furthermore, we document that the efficiency of triacylglycerol production by CtDGAT2b is differentially affected by deletion, insertion or replacement of amino acids in five regions exclusively present in two CtDGAT2 isozymes. Taken together, our study characterizes two structurally novel DGAT2 isozymes, which are accountable for the enhanced production of storage lipid enriched with saturated fatty acids inherently in C. tropicalis SY005 strain as well as in transformed S. cerevisiae neutral lipid-deficient mutant cells. These two genes certainly will be useful for further investigation on the novel structure-function relationship of DGAT repertoire, and also in metabolic engineering for the enhanced production of lipid feedstock in other organisms.

    View details for DOI 10.1371/journal.pone.0094472

    View details for PubMedID 24732323

  • LinkedPPI: Enabling Intuitive, Integrative Protein-Protein Interaction Discovery 4th Workshop on Linked Science co-located with 13th International Semantic Web Conference Kazemzadeh, L., Kamdar, M. R., Beyan, O. D., Decker, S., Barry, F. 2014: 48–59
  • A Roadmap for navigating the Life Sciences Linked Open Data Cloud 4th Joint International Semantic Technology (JIST) Conference Hasnain, A., Sana e Zainab, S., Kamdar, M. R., Mehmood, Q., Deus, H. F., Mehdi, M., Decker, S. 2014
  • Open Data Ireland: Data Audit Report Open Data Ireland Support Project Cyganiak, R., Kamdar, M., Maali, F., Lee, D., Decker, S. 2014
  • Linked Biomedical Dataspace: Lessons Learned Integrating Data for Drug Discovery 13th International Semantic Web Conference (ISWC) Hasnain, A., Kamdar, M. R., Hasapis, P., Zeginis, D., Warren, C., Deus, H. F., Ntalaperas, D., Tarabanis, K., Mehdi, M., Decker, S. 2014: 114–130
  • Big linked cancer data: Integrating linked TCGA and PubMed Web Semantics: Science, Services and Agents on the World Wide Web Saleem, M., Kamdar, M. R., Iqbal, A., Sampath, S., Deus, H. F., Ngomo, A. N. 2014
  • The Reactome pathway knowledgebase. Nucleic acids research Croft, D., Mundo, A. F., Haw, R., Milacic, M., Weiser, J., Wu, G., Caudy, M., Garapati, P., Gillespie, M., Kamdar, M. R., Jassal, B., Jupe, S., Matthews, L., May, B., Palatnik, S., Rothfels, K., Shamovsky, V., Song, H., Williams, M., Birney, E., Hermjakob, H., Stein, L., D'Eustachio, P. 2014; 42 (Database issue): D472-7


    Reactome ( is a manually curated open-source open-data resource of human pathways and reactions. The current version 46 describes 7088 human proteins (34% of the predicted human proteome), participating in 6744 reactions based on data extracted from 15 107 research publications with PubMed links. The Reactome Web site and analysis tool set have been completely redesigned to increase speed, flexibility and user friendliness. The data model has been extended to support annotation of disease processes due to infectious agents and to mutation.

    View details for DOI 10.1093/nar/gkt1102

    View details for PubMedID 24243840

  • Identification of an Extracellular Antifungal Protein from the Endophytic Fungus Colletotrichum sp DM06 PROTEIN AND PEPTIDE LETTERS Dey, P., Kamdar, M. R., Mandal, S. M., Maiti, M. K. 2013; 20 (2): 173-179


    An extracellular antifungal protein of 28 kDa (exAFP-C28) was identified from an endophytic fungus Colletotrichum sp. DM-06. After purification, the MIC value of exAFP-C28 against Candida albicans, a well-known human pathogenic fungus was found to be 32 μg/mL that unaffected the human red blood cells. The antifungal activity associated with exAFP-C28 was manifested by the increased membrane permeability of C. albicans cells followed by disruption. Proteomics and bioinformatics analyses revealed that several peptide fragments of exAFP-C28 have identity with the bacterial 50S ribosomal protein L10, and a stretch of 55 amino acids of two peptide fragments corresponding to the Nterminus of L10 protein is capable of forming amphipathic helix required for membrane penetration. Taken together, our results suggest that the exAFP-C28 protein from Colletotrichum sp. DM-06 is a promising therapeutic agent in controlling candidiasis disease in animals including humans.

    View details for Web of Science ID 000316859400008

    View details for PubMedID 22894154

  • Fostering Serendipity through Big Linked Data 12th International Semantic Web Conference (ISWC) Saleem, M., Kamdar, M. R., Iqbal, A., Sampath, S., Deus, H. F., Ngomo, A. N. 2013