Year-2023Year-2023http://repository.iiitd.edu.in/xmlui/handle/123456789/13182026-04-10T22:01:07Z2026-04-10T22:01:07ZComputational creativity using embeddingsPandey, ayanArvind, HirenThakkar, DevBagler, Ganesh (Advisor)http://repository.iiitd.edu.in/xmlui/handle/123456789/16282024-05-28T22:00:28Z2023-11-29T00:00:00ZComputational creativity using embeddings
Pandey, ayan; Arvind, Hiren; Thakkar, Dev; Bagler, Ganesh (Advisor)
2023-11-29T00:00:00ZPrediction of disease genes using transcription factor binding patternVimal, AnandKumar, Vibhor (Advisor)Chandra, Omkar (Advisor)http://repository.iiitd.edu.in/xmlui/handle/123456789/16272024-07-08T11:00:06Z2023-10-29T00:00:00ZPrediction of disease genes using transcription factor binding pattern
Vimal, Anand; Kumar, Vibhor (Advisor); Chandra, Omkar (Advisor)
This study presents a novel approach leveraging machine learning techniques to analyze epigenomic and transcription factor binding patterns for the identification of key genes, including non-coding genes, associated with rare diseases. Utilizing comprehensive gene lists from the "Rare_Diseases_GeneRIF_Gene_Lists" along with data sets pertaining to "Disease_Perturbations_from_GEO_up" and “Disease_Perturbations_from_GEO_down" downloaded from Enrichr database. The efficacy of our method was demonstrated using the Area Under the Curve (AUC) plots for each disease-specific gene set, providing a quantitative measure of our model's performance. These AUC plots not only underscored the accuracy of our predictions but also revealed distinct epigenetic and transcriptional signatures characteristic of various rare diseases. Our findings associated novel genes of rare diseases and pave the way for further investigations into targeted therapies. This work highlights the potential of machine learning in transforming our understanding of rare genetic disorders and in aiding the discovery of novel therapeutic targets.
2023-10-29T00:00:00ZPredicting prediction of probiotic species in the gut using statistical and machine learningSharma, Aditya AryanHarshalGhosh, Tarini Shankar (Advisor)http://repository.iiitd.edu.in/xmlui/handle/123456789/16252024-05-28T22:00:20Z2023-11-29T00:00:00ZPredicting prediction of probiotic species in the gut using statistical and machine learning
Sharma, Aditya Aryan; Harshal; Ghosh, Tarini Shankar (Advisor)
2023-11-29T00:00:00ZDevelopment of chemsulesPankajbhai, Rathod KunalTyagi, Sarthakhttp://repository.iiitd.edu.in/xmlui/handle/123456789/16172025-12-18T07:57:12Z2023-11-29T00:00:00ZDevelopment of chemsules
Pankajbhai, Rathod Kunal; Tyagi, Sarthak
Chemsules introduces a revolutionary approach to carcinogenicity assessment, anchored by the precision-driven Metabokiller machine learning model. Catering to a diverse user base, including researchers, scientists, industry professionals, students, and enthusiasts, the platform offers a seamless web interface for compound analysis. Metabokiller's comprehensive evaluation covers electrophilicity, proliferation induction, oxidative stress, genomic instability, epigenome alterations, and anti-apoptotic response, providing a holistic understanding of compound carcinogenicity. The website features intuitive input methods, including SMILES string entry and graphical structure depiction. Ensuring data integrity and user confidentiality, the platform's scalability is showcased through an API for further development. This user-centric approach aims to democratize access to predictive insights, fostering advancements in compound carcinogenicity understanding.
2023-11-29T00:00:00Z