about
CPPsite 2.0: a repository of experimentally validated cell-penetrating peptides.More Is Better: Recent Progress in Multi-Omics Data Integration MethodsTumor homing peptides as molecular probes for cancer therapeutics, diagnostics and theranostics.PEPstrMOD: structure prediction of peptides containing natural, non-natural and modified residues.Prediction of anticancer molecules using hybrid model developed on molecules screened against NCI-60 cancer cell lines.A Web Server and Mobile App for Computing Hemolytic Potency of PeptidesA Platform for Designing Genome-Based Personalized Immunotherapy or Vaccine against Cancer.A web-based resource for designing therapeutics against Ebola VirusDesigning of promiscuous inhibitors against pancreatic cancer cell lines.Gene expression-based biomarkers for discriminating early and late stage of clear cell renal cancer.Prediction of Immunomodulatory potential of an RNA sequence for designing non-toxic siRNAs and RNA-based vaccine adjuvants.Hemolytik: a database of experimentally determined hemolytic and non-hemolytic peptides.Managing Drug Resistance in Cancer: Role of Cancer Informatics.Prioritization of anticancer drugs against a cancer using genomic features of cancer cells: A step towards personalized medicine.Identification and characterization of novel protein-derived arginine-rich cell-penetrating peptides.In silico approaches for designing highly effective cell penetrating peptides.Computational approach for designing tumor homing peptides.SATPdb: a database of structurally annotated therapeutic peptidesParaPep: a web resource for experimentally validated antiparasitic peptide sequences and their structuresVaccineDA: Prediction, design and genome-wide screening of oligodeoxynucleotide-based vaccine adjuvants.Computer-Aided Virtual Screening and Designing of Cell-Penetrating Peptides.CancerDR: cancer drug resistance database.PCMdb: pancreatic cancer methylation database.Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data.Computational Prediction of the Immunomodulatory Potential of RNA Sequences.Deep Learning based multi-omics integration robustly predicts survival in liver cancer.In silico models for designing and discovering novel anticancer peptides.In Silico Approach for Prediction of Antifungal Peptides.Deep Learning data integration for better risk stratification models of bladder cancer.Computer-aided prediction of antigen presenting cell modulators for designing peptide-based vaccine adjuvants.Peptide Toxicity PredictionAssociation of APOL1 Risk Genotype and Air Pollution for Kidney DiseaseMultimodal Meta-Analysis of 1,494 Hepatocellular Carcinoma Samples Reveals Significant Impact of Consensus Driver Genes on PhenotypesAssociation of the V122I Hereditary Transthyretin Amyloidosis Genetic Variant With Heart Failure Among Individuals of African or Hispanic/Latino AncestryNatural language processing of electronic health records is superior to billing codes to identify symptom burden in hemodialysis patientsMachine Learning in Glomerular Diseases: Promise for Precision MedicineRate of Correction of Hypernatremia and Health Outcomes in Critically Ill Patients
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description
investigador
@es
researcher
@en
name
Kumardeep Chaudhary
@en
type
label
Kumardeep Chaudhary
@en
prefLabel
Kumardeep Chaudhary
@en
P2456
P31
P496
0000-0002-4117-6403