A5017388580- Gene expression and cancer classification
- AI in cancer detection
- Molecular Biology Techniques and Applications
- Machine Learning in Bioinformatics
- Tea Polyphenols and Effects
- Computational Drug Discovery Methods
- Bioinformatics and Genomic Networks
- Tryptophan and brain disorders
Jamia Millia Islamia
2015-2021
Prostate cancer is among the most common in males and its heterogeneity well known. The genomic level changes can be detected gene expression data those may serve as standard model for any random class prediction. Various techniques were implied on prostate set order to accurately predict including machine learning techniques. Large number of attributes but few numbers samples microarray leads poor training; therefore, challenging part attribute reduction or non-significant reduction. In...
Alzheimer's Disease (AD) is one of the most common causes dementia, mostly affecting elderly population. Currently, there no proper diagnostic tool or method available for detection AD. The present study used two distinct data sets AD genes, which could be potential biomarkers in diagnosis. differentially expressed genes (DEGs) curated from both datasets were machine learning classification, tissue expression annotation and co-expression analysis. Further, CNPY3, GPR84, HIST1H2AB, HIST1H2AE,...
Smoking is the leading cause of lung cancer development and several genes have been identified as potential biomarker for lungs cancer. Contributing to present scientific knowledge biomarkers two different data sets, i.e. GDS3257 GDS3054 were downloaded from NCBI׳s GEO database normalized by RMA GRMA packages (Bioconductor). Diffrentially expressed extracted using R (3.1.2); DAVID online tool was used gene annotation GENE MANIA construction regulatory network. Nine smoking independent found...