A method for predicting linear and conformational B-cell epitopes in an antigen from its primary sequence.

B-cell is an essential component of the immune system that plays a vital role in providing the immune response against any pathogenic infection by producing antibodies. Existing methods either predict linear or conformational B-cell epitopes in an antigen. In this study, a single method was developed for predicting both types (linear/conformational) of B-cell epitopes.

A random forest model for predicting exosomal proteins using evolutionary information and motifs.

Non-invasive diagnostics and therapies are crucial to prevent patients from undergoing painful procedures. Exosomal proteins can serve as important biomarkers for such advancements. In this study, we attempted to build a model to predict exosomal proteins.

A web server for predicting and scanning of IL-5 inducing peptides using alignment-free and alignment-based method.

Interleukin-5 (IL-5) can act as an enticing therapeutic target due to its pivotal role in several eosinophil-mediated diseases. The aim of this study is to develop a model for predicting IL-5 inducing antigenic regions in a protein with high precision. All models in this study have been trained, tested and validated on experimentally validated 1907 IL-5 inducing and 7759 non-IL-5 inducing peptides obtained from IEDB.

ViralVacDB: A manually curated repository of viral vaccines.

Over the years, numerous vaccines have been developed against viral infections; however, a complete database that provides comprehensive information on viral vaccines has been lacking. In this review, along with our freely accessible database ViralVacDB, we provide details of the viral vaccines, their type, routes of administration and approving agencies. This repository systematically covers additional information such as disease name, adjuvant, manufacturer, clinical status, age and dosage against 422 viral vaccines, including 145 approved vaccines and 277 in clinical trials.

Identification of potential key genes related to idiopathic male infertility using RNA-sequencing data: an approach.

Among reproductive health problems, idiopathic infertility affects married couples. The current diagnosis of male infertility focuses on the concentration, motility, and morphology of sperm in the ejaculate. Since the molecular mechanism of idiopathic infertility is unknown, identification of Differentially Expressed Genes (DEGs) among the control and idiopathic infertile male can shed light on diagnosis and treatment.

Collection, compilation and analysis of bacterial vaccines.

Background: Bacterial diseases are one of the leading causes of millions of fatalities worldwide, mainly due to antimicrobial resistance. The discovery of chicken cholera vaccine in 1879 revolutionized our fight against bacterial infections. Bacterial vaccines are proven to be highly effective in preventing many infectious diseases.

ToxinPred2: an improved method for predicting toxicity of proteins.

Proteins/peptides have shown to be promising therapeutic agents for a variety of diseases. However, toxicity is one of the obstacles in protein/peptide-based therapy. The current study describes a web-based tool, ToxinPred2, developed for predicting the toxicity of proteins.

In silico tools and databases for designing cancer immunotherapy.

Immunotherapy is a rapidly growing therapy for cancer which have numerous benefits over conventional treatments like surgery, chemotherapy, and radiation. Overall survival of cancer patients has improved significantly due to the use of immunotherapy. It acts as a novel pillar for treating different malignancies from their primary to the metastatic stage.

Prognostic biomarkers for predicting papillary thyroid carcinoma patients at high risk using nine genes of apoptotic pathway.

Aberrant expressions of apoptotic genes have been associated with papillary thyroid carcinoma (PTC) in the past, however, their prognostic role and utility as biomarkers remains poorly understood. In this study, we analysed 505 PTC patients by employing Cox-PH regression techniques, prognostic index models and machine learning methods to elucidate the relationship between overall survival (OS) of PTC patients and 165 apoptosis related genes. It was observed that nine genes (ANXA1, TGFBR3, CLU, PSEN1, TNFRSF12A, GPX4, TIMP3, LEF1, BNIP3L) showed significant association with OS of PTC patients.

Computer-aided prediction of inhibitors against STAT3 for managing COVID-19 associated cytokine storm.

Background: Proinflammatory cytokines are correlated with the severity of disease in patients with COVID-19. IL6-mediated activation of STAT3 proliferates proinflammatory responses that lead to cytokine storm promotion. Thus, STAT3 inhibitors may play a crucial role in managing the COVID-19 pathogenesis.

ChAlPred: A web server for prediction of allergenicity of chemical compounds.

Background: Allergy is the abrupt reaction of the immune system that may occur after the exposure to allergens such as proteins, peptides, or chemicals. In the past, various methods have been generated for predicting allergenicity of proteins and peptides. In contrast, there is no method that can predict allergenic potential of chemicals.

Ebolabase: Zaire ebolavirus-human protein interaction database for drug-repurposing.

Ebola Virus (EBOV) is one of the deadliest pathogenic virus which causes hemorrhagic fever. Though many Ebola-human interaction studies and databases are already reported, the unavailability of an adequate model and lack of publically accessible resources requires a comprehensive study to curate the Ebola-Human-Drug interactions. In total, 270 human proteins interacted with EBOV are collected from published experimental evidence.

Understanding of Zaire ebolavirus-human protein interaction for drug repurposing.

The Ebola virus is a human aggressive pathogen causes Ebola virus disease that threatens public health, for which there is no Food Drug Administration approved medication. Drug repurposing is an alternative method to find the novel indications of known drugs to treat the disease effectively at low cost. The present work focused on understanding the host-virus interaction as well as host virus drug interaction to identify the disease pathways and host-directed drug targets.

Comprehensive analysis of dysregulated lncRNAs and their competing endogenous RNA network in triple-negative breast cancer.

The study aimed to explore the molecular mechanism underlying triple-negative breast cancer (TNBC) and to identify their potential diagnostic/prognostic biomarkers. The differentially expressed lncRNAs (DElncRNAs) were identified by meta-analysis and machine learning feature selection methods. The dysregulated lncRNA-miRNA-mRNA network was constructed based on the competing endogenous RNA (ceRNA) hypothesis.

Integrated transcriptome interactome study of oncogenes and tumor suppressor genes in breast cancer.

Breast cancer is the leading cause for mortality among women worldwide. Dysregulation of oncogenes and tumor suppressor genes is the major reason for the cause of cancer. Understanding these genes will provide clues and insights about their regulatory mechanism and their interplay in cancer.

Identification of dysregulated miRNAs in triple negative breast cancer: A meta-analysis approach.

Triple negative breast cancer (TNBC) is an aggressive subtype of breast cancer with poor clinical outcomes and lack of approved targeted therapy. Dysregulated microRNAs (miRNAs) have been considered a promising biomarker, which plays an important role in the tumorigenesis of human cancer. Due to the increase in miRNA profiling datasets of TNBC, a proper analysis is required for studying.

Integrated network analysis and machine learning approach for the identification of key genes of triple-negative breast cancer.

Triple-negative breast cancer (TNBC) has attracted more attention compared with other breast cancer subtypes due to its aggressive nature, poor prognosis, and chemotherapy remains the mainstay of treatment with no other approved targeted therapy. Therefore, the study aimed to discover more promising therapeutic targets and investigating new insights of biological mechanism of TNBC. Six microarray data sets consisting of 463 non-TNBC and 405 TNBC samples were mined from Gene Expression Omnibus.

Identification of Hub Genes and Pathways in Zika Virus Infection Using RNA-Seq Data: A Network-Based Computational Approach.

Zika virus (ZIKV), a single-strand RNA flavivirus, is transmitted primarily through Aedes aegypti. The recent outbreaks in America and unexpected association between ZIKV infection and birth defects have triggered the global attention. This vouches to understand the molecular mechanisms of ZIKV infection to develop effective drug therapy.

ZikaBase: An integrated ZIKV- Human Interactome Map database.

Re-emergence of ZIKV has caused infections in more than 1.5 million people. The molecular mechanism and pathogenesis of ZIKV is not well explored due to unavailability of adequate model and lack of publically accessible resources to provide information of ZIKV-Human protein interactome map till today.

Expression Data Analysis for the Identification of Potential Biomarker of Pregnancy Associated Breast Cancer.

Breast cancer affects every 1 of 3000 pregnant women or in the first post-partum year is referred as Pregnancy Associated Breast Cancer (PABC) in mid 30s. Even-though rare disease, classified under hormone receptor negative status which metastasis quickly to other parts by extra cellular matrix degradation. Hence it is important to find an optimal treatment option for a PABC patient.