http://repository.iiitd.edu.in/xmlui/handle/123456789/1261 2026-04-11T12:02:27Z 2026-04-11T12:02:27Z Raj, Abhijit Sengupta, Debarka (Advisor) http://repository.iiitd.edu.in/xmlui/handle/123456789/1268 2023-05-27T22:00:32Z 2020-07-01T00:00:00Z

Pathomap : gene-organ relationships a literature-based investigation, visualization & analysis Raj, Abhijit; Sengupta, Debarka (Advisor) Genetics has brought huge breakthroughs in understanding human health & wellbeing, diseases and treatment through modern methods such as personalized medicine. This has been possible through substantial research in the area that reveals how genes are directly linked to our health. As genetic information is passed down in the family, genetic conditions are also hereditary. It is therefore very important to understand the pathogenic role of genes. As new research progresses at a tremendous rate, a lot of insights can also be drawn from the volumes of literature already published by the scientists. Text mining and NLP have become indispensable tools to analyse large amount of textual data such as scientific literature and derive insights from it. Information preserving NLP methods distill from a vast corpus, the relevant pieces of information such as geneorgan relationships, pathogenic role of genes and more. Ambitious efforts are being made to map the human body at the cellular level to understand variations in cells and how they lead to diseases. In this study, we aim to investigate gene-organ relationships through existing literature. We exploit visualization extensively as a tool to accelerate our understanding of this data. We introduce PathoMap - a novel tool to visualize any organ-related data on the human body. It is the first Python package to plot such organ-specific information. In the context of gene-organ relationships, we use PathoMap to draw conclusions in both healthy and pathological conditions. We hope that our visualization tool, PathoMap will be widely adopted and used in a range of studies to visualize organ-related data.

2020-07-01T00:00:00Z Walia, Anjali Raghava, Gajendra Pal Singh (Advisor) http://repository.iiitd.edu.in/xmlui/handle/123456789/1267 2023-05-27T22:00:29Z 2020-06-01T00:00:00Z

Computational analysis and compilation of leukemia biomarkers Walia, Anjali; Raghava, Gajendra Pal Singh (Advisor) LeukemiaBD, is a Database of Biomarker data for Leukemia. The aim of this database is to serve as a comprehensive repository catering to all relevant information related to Leukemia. The database currently has manually curated information about the leukemia biomarkers. It comprises an interactive interface to query each biomarker via an easy searching and browsing facility. Individual information about each biomarker comprises the associated type of leukemia, patient cohort, regulation of biomarker, type of biomarker, type of biomolecule, source, etc. We believe that LeukemiaBD will be a valuable resource for the scientific community to explore the leukemia biomarker. LeukemiaBD is built on responsive templates which are compatible with smartphones and various other gadgets (mobile, iPhone, iPad, tablets etc.). Database URL : webs.iiitd.edu.in/raghava/leukemiabd

2020-06-01T00:00:00Z Kalra, Siddhant Ahuja, Gaurav (Advisor) http://repository.iiitd.edu.in/xmlui/handle/123456789/1266 2023-05-27T22:00:28Z 2020-05-01T00:00:00Z

Cancersmell:molecular basis of chemosensation in cancer cells Kalra, Siddhant; Ahuja, Gaurav (Advisor)

2020-05-01T00:00:00Z Sharma, Divya Sengupta, Debarka (Advisor) http://repository.iiitd.edu.in/xmlui/handle/123456789/1265 2023-05-27T22:00:26Z 2020-01-01T00:00:00Z

Automated discovery of abstracts reporting gene-disease relationship Sharma, Divya; Sengupta, Debarka (Advisor) Text classification is a construction problem of models which can classify new documents into predefined classes. It is important before text mining that we know what is the most important data that we require for our research. Text mining has become an essential tool for biomedical research. Our project aims to identify the gene-disease relationship using natural language processing techniques and word embeddings. Assignment of high-dimensional vectors (embeddings) to words in a text corpus in a way that preserves their syntactic and semantic relationships is one of the most fundamental techniques in natural language processing (NLP). We present a completely generic model based on statistical word embeddings, which shows the gene similarity and proves the gene-disease relationship using word analogies.

2020-01-01T00:00:00Z