Department of Bioscience & Bioengineering

&ldquo;IIT Jodhpur is committed to impart quality education to facilitate the creation of qualified, competent and responsible human resources to meet the emerging technological challenges of the world. It is working towards creating an equitable learning platform that is positive, accessible and effective in the face of the modern societal and scientific advancements.

Flexibility in curriculum, in all its academic programs, encompassing elements of teaching, research, creativity, innovation and entrepreneurship is the most unique feature of IIT Jodhpur. The Department of Science and Technology (DST), under the National Mission on Cyber Physical Systems, has identified IIT Jodhpur as a Technology &amp; Innovation Hub in the area of Augmented Reality and Virtual Reality. The institute has also been nominated as the coordinator for the Jodhpur City and Knowledge Cluster by the Office of Principal Scientific Adviser, Government of India.

The sprawling 852-acre IIT Jodhpur campus is a majestic tribute to the deep-rooted diverse cultural heritage of the region. The campus is an architectural masterpiece, which is slated to become an international exemplar of sustainability with net-zero energy, and advanced water and waste management strategies.&rdquo;

IIT Jodhpur

AlGaN/GaN HEMT based biosensor for detection of the HER2 antigen spiked in human serum

Brain on a-Chip

Mechanochemical synthesis of AIE-TICT-ESIPT active orange-emissive chemodosimeter for selective detection of hydrogen peroxide in aqueous media and living cells, and solid-phase quantitation using a smartphone" Akhil A. Bhosle, Mainak Banerjee, Varsha Gupta, Surajit Ghosh, Achikanath C Bhasikuttan and Amrita Chatterjee.

Evolution of Potential Antimitotic Stapled Peptide from Multiple Helical Peptide Stretches of Tubulin Heterodimer Interface: Helix-Mimicking Stapled Peptide Tubulin Inhibitors

Design and Development of Benzothiazole-based Fluorescent Probes for Selective Detection of Aβ Aggregates in Alzheimer’s Disease

In Vitro and In Silico Determinants of HMG-CoA reductase inhibition potential of caffeic acid for therapeutic of hypercholesterolemia

Exosome: The Nano component Trinity as Potential Pathogenic Agent, Disease Biomarker and Neurotherapeutics

Designed Hybrid Anticancer Nuclear Localized Peptide Inhibits Aggressive Cancer Cell Proliferation

Rice (Oryza sativa) is a significant agricultural crop consumed by more than half of the global population. Its demand is expected to increase due to rising consumption and a growing global population. Moreover, the rice plant is frequently exposed to disease-causing pathogens, such as bacteria, fungi, viruses, and nematodes. Thus, cultivating disease-resistant varieties is an efficient way of disease control compared to pesticide applications. However, the rice plant has a well-defined defense system to prevent the onset of disease, including Pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI) and effector-triggered immunity (ETI). The defense system is controlled by various disease-resistance proteins, such as resistance (R) proteins and pathogen recognition receptors (PRRs). Therefore, the identification of disease-resistance proteins not only reduces the amount of pesticides used in rice fields but also increases their yield. Though some resistant proteins have been characterized, their rapid identification, precise diagnosis, and appropriate management are still lacking. However, few methods based on sequence-similarity and de novo prediction, such as Machine Learning (ML), usually have low prediction power. In this study, we built a state-of-the-art classifier based on Deep Learning (DL) for the early detection of disease-resistance proteins in rice and related species. We compared the DL-based Multi-layer Perceptron (MLP) model with the five well-established ML-based methods using a protein dataset of rice and its related species. The DL-based MLP model outperformed all of the five classifiers on 10-fold cross-validation. The accuracy, Area Under Receiving Operating Characteristic (ROC) curve (AUC), F1-score, precision, and recall were superior in the DL-based MLP model. In conclusion, the MLP model is an effective DL model for predicting disease-resistance proteins with high scores in performance metrics. This study will provide insight to the breeders in developing disease-resistant rice varieties and assist in transforming traditional rice farming practices into a new age of smart rice farming.

World University root node

bioRxiv

Deep learning-based method to identify disease-resistance proteins in Oryza sativa and relative species

Facile Method of Tubulin Purification from Goat Brain for Reconstitution of Microtubule-Associated Intracellular Function