Leimarembi Devi Naorem

The paper shows that cell-free chromatin particles from cancer patients can transform mouse fibroblasts into cancer stem cells capable of forming tumors and expressing germline markers.

The paper develops a hybrid model for predicting both linear and conformational B-cell epitopes using machine learning and BLAST, achieving an AUC of 0.83 on an independent dataset.

This paper develops a hybrid model combining motif-based and machine learning approaches to predict exosomal proteins with high accuracy, achieving AUROC of 0.85 on an independent dataset.

This paper develops methods to predict IL-5 inducing peptides using alignment-based and alignment-free approaches, achieving high performance with a hybrid model.

The paper identifies nine apoptotic pathway genes associated with prognosis in papillary thyroid carcinoma and develops risk prediction models with high accuracy.

This paper identifies 15 gene signatures for predicting lymph node metastasis in TNBC using TCGA data, but finds poor cross-platform validation and develops a webserver for prediction.