Neha Periwal PhD.
Dr. Neha Periwal is a Researcher at Vgenomics. She has begun her journey on her path to industrial research in the field of bioinformatics in 2023. During her fellowship at Jamia Hamdard, she gained work experience and exposure in utilizing both machine learning and deep learning techniques which can be applied to parse datasets in multiple omics fields.
She has applied her expertise to study various immune pathways that are affected during viral infections in her published and yet-to-be published papers. Overall she brings 5+ years of research experience and a fresh perspective to the team.
Research articles
Dr. Periwal’s current research revolves around the use of bioinformatics techniques to investigate immune pathways affected during viral infections.
Antiviral activity of the human endogenous retrovirus‐R envelope protein against SARS‐CoV‐2
Coronavirus‐induced disease‐19 (COVID‐19), caused by SARS‐CoV‐2, is still a major global health challenge. Human endogenous retroviruses (HERVs) represent retroviral elements that were integrated into the ancestral human genome. HERVs are important in embryonic development as well as in the manifestation of diseases, including cancer, inflammation,…
iIL13Pred: improved prediction of IL-13 inducing peptides using popular machine learning classifiers
Background Inflammatory mediators play havoc in several diseases including the novel Coronavirus disease 2019 (COVID-19) and generally correlate with the severity of the disease. Interleukin-13 (IL-13), is a pleiotropic cytokine that is known to be associated with airway inflammation in asthma and reactive airway diseases, in neoplastic and autoimm…
In silico analysis of SARS-CoV-2 genomes: Insights from SARS encoded non-coding RNAs
The recent pandemic caused by Severe Acute Respiratory Syndrome Coronavirus-2 has resulted in enormous deaths around the world. Clues from genomic sequences of parent and their mutants can be obtained to understand the evolving pathogenesis of this virus. Apart from the viral proteins, virus-encoded microRNAs (miRNAs) have been shown to play a vita…
Novel insights into host responses to Japanese Encephalitis Virus infection: Reanalysis of public transcriptome and microRNAome datasets
Purpose Japanese encephalitis (JE), caused by the Japanese encephalitis virus (JEV), is the principal cause of viral encephalitis in South-East Asian and Western Pacific countries; accounting for 68,000 cases, and up to 20,400 fatalities, annually across the world. Despite being a high-risk condition, there is no specific treatment for JE. Given ra…
Human blood plasma proteins modeling and binding affinities with Δ9-tetrahydrocannabinol active metabolites: In silico approach
Tetrahydrocannabinol (THC) is a key psychotropic constituent of cannabis sativa. It is also known as Δ9-tetrahydrocannabinol (Δ9-THC). Previous study suggested that owing to its high lipophilicity, it piles up in adipose tissue and it is disseminated into blood stream for prolonged time. Research suggests that numerous diseases such as multiple scl…
Time series analysis of SARS-CoV-2 genomes and correlations among highly prevalent mutations
The efforts of the scientific community to tame the recent SARS-CoV-2 pandemic seems to have been diluted by the emergence of new viral strains. Therefore, it becomes imperative to study and understand the effect of mutations on viral evolution, fitness and pathogenesis. In this regard, we performed a time-series analysis on 59541 SARS-CoV-2 genomi…
Replication of Dengue Virus in K562-Megakaryocytes Induces Suppression in the Accumulation of Reactive Oxygen Species
Dengue virus can infect human megakaryocytes leading to decreased platelet biogenesis. In this article, we report a study of Dengue replication in human K562 cells undergoing PMA-induced differentiation into megakaryocytes. PMA-induced differentiation in these cells recapitulates steps of megakaryopoiesis including gene activation, expression of CD…
A novel binary k -mer approach for classification of coding and non-coding RNAs across diverse species
Classification among coding (CDS) and non-coding RNA (ncRNA) sequences is a challenge and several machine learning models have been developed for the same. Since the frequency of curated coding sequences is many-folds as compared to that of the ncRNAs, we devised a novel approach to work with the complete datasets from fifteen diverse species. In o…
In silico characterization of mutations circulating in SARS-CoV-2 structural proteins
SARS-CoV-2 has recently emerged as a pandemic that has caused more than 2.4 million deaths worldwide. Since the onset of infections, several full-length sequences of viral genome have been made available which have been used to gain insights into viral dynamics. We utilised a meta-data driven comparative analysis tool for sequences (Meta-CATS) algo…
