Naorem Leimarembi Devi
Indraprastha Institute of Information Technology
9 Papers
11 Citations
Naorem Leimarembi Devi is an academic researcher from Indraprastha Institute of Information Technology. The author has contributed to research in topics: Computer science & Biology. The author has an hindex of 1, co-authored 3 publications.
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Papers
ToxinPred2: an improved method for predicting toxicity of proteins
TL;DR: A general method developed for predicting the toxicity of proteins regardless of their source of origin, and a hybrid method that combined all three approaches and achieved a maximum area under receiver operating characteristic curve around 0.99.
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ChAlPred: A web server for prediction of allergenicity of chemical compounds.
TL;DR: Raghava et al. as mentioned in this paper developed a method ChAlPred developed for predicting chemical allergens as well as for designing chemical analogs with desired allergenicity, which achieved the maximum accuracy of 83.39% and AUC of 0.93 on validation dataset.
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Computer-aided prediction of inhibitors against STAT3 for managing COVID-19 associated cytokine storm.
TL;DR: A method developed for predicting inhibitors against the IL6-mediated STAT3 signaling pathway and analysis of data indicates that rings and aromatic groups are significantly abundant in STAT3 inhibitors compared to non-inhibitors.
20
A web server for predicting and scanning of IL-5 inducing peptides using alignment-free and alignment-based method
TL;DR: Raghava et al. as discussed by the authors developed a model for predicting IL-5 inducing antigenic regions in a protein with high precision, which achieved a maximum AUC of 0.59.
8
Transcriptomics based prediction of metastasis in TNBC patients: Challenges in cross-platforms validation
TL;DR: Raghava et al. as mentioned in this paper identified genes that can act as diagnostic biomarkers for predicting lymph node metastasis in triple negative breast cancer (TNBC) patients and used logistic regression method to identify the top 15 genes (or 15 gene signatures) based on their ability to predict metastasis (AUC>0.65).