Ph.D, MSc

Dr. Nidheesh M. currently serves as Assistant Professor at the School of Biotechnology. He received his masters in Computer Science from Amrita Vishwa Vidyapeetaham, Amritapuri in 2005.


Publication Type: Journal Article

Year of Publication Publication Type Title


Journal Article

Nidheesh Melethadathil, Dr. Bipin G. Nair, Dr. Shyam Diwakar, and Jaap Heringa, “Mining inter-relationships in online scientific articles and its visualization: Natural language processing for systems biology modeling”, International Journal of Online Engineering, vol. 15, no. 2, pp. 39-59, 2019.[Abstract]

With the rapid growth in the numbers of scientific publications in domains such as neuroscience and medicine, visually interlinking documents in online databases such as PubMed with the purpose of indicating the context of a query results can improve the multi-disciplinary relevance of the search results. Translational medicine and systems biology rely on studies relating basic sciences to applications, often going through multiple disciplinary domains. This paper focuses on the design and development of a new scientific document visualization platform, which allows inferring translational aspects in biosciences within published articles using machine learning and natural language processing (NLP) methods. From online databases, this software platform effectively extracted relationship connections between multiple subdomains within neuroscience derived from abstracts related to user query. In our current implementation, the document visualization platform employs two clustering algorithms namely Suffix Tree Clustering (STC) and LINGO. Clustering quality was improved by mapping top-ranked cluster labels derived from an UMLS-Metathesaurus using a scoring function. To avoid non-clustered documents, an iterative scheme, called auto-clustering was developed and this allowed mapping previously uncategorized documents during the initial grouping process to relevant clusters. The efficacy of this document clustering and visualization platform was evaluated by expert-based validation of clustering results obtained with unique search terms. Compared to normal clustering, auto-clustering demonstrated better efficacy by generating larger numbers of unique and relevant cluster labels. Using this implementation, a Parkinson's disease systems theory model was developed and studies based on user queries related to neuroscience and oncology have been showcased as applications. © 2019 Kassel University Press GmbH.

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Publication Type: Conference Paper

Year of Publication Publication Type Title


Conference Paper

Nidheesh Melethadathil, Priya Chellaiah, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Classification and Clustering for Neuroinformatics: Assessing the efficacy on reverse-mapped NeuroNLP data using standard ML techniques”, in Proceedings of the Fourth International Conference on Advances in Computing, Communications and Informatics (ICACCI-2015), Kochi, India, 2015.[Abstract]

NeuroinformaticsNatural Language Processing (NeuroNLP) relies on clustering and classification for information categorization of biologically relevant extraction targets and for interconnections to knowledge-related patterns in event and text mined datasets. The accuracy of machine learning algorithms depended on quality of text-mined data while efficacy relied on the context of the choice of techniques. Although developments of automated keyword extraction methods have made differences in the quality of data selection, the efficacy of the Natural Language Processing (NLP) methods using verified keywords remain a challenge. In this paper, we studied the role of text classification and document clustering algorithms on datasets, where features were obtained by mapping to manually verified MESH terms published by National Library of Medicine (NLM). In this study, NLP data classification involved comparing 8techniques and unsupervised learning was performed with 6 clustering algorithms. Most classification techniques except meta-based algorithms namely stacking and vote, allowed 90% or higher training accuracy. Test accuracy was high (=>95%) probably due to limited test dataset. Logistic Model Trees had 30-fold higher runtime compared to other classification algorithms including Naive Bayes, AdaBoost, Hoeffding Tree. Grouped error rate in clustering was 0-4%. Runtime-wise, clustering was faster than classification algorithms on MESH-mapped NLP data suggesting clustering methods as adequate towards Medline-related datasets and text-mining big data analytic systems. © 2015 IEEE.

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