Complex Disease Prediction using Deep Learning from Medical Image Data
Medical image processing is a research domain where advance computer-aided algorithms are used for disease prognosis and treatment planning. The deep learning algorithm is a machine learning technique that does not relies on feature extraction unlike classical neural network algorithms. The coupling of machine learning algorithms with high-performance computing gives promising results in medical image analysis like fusion, segmentation, registration and classification. This project focues the applications of deep learning algorithms in cancer diagnosis specifically in the CT/MR brain and abdomen images, mammogram images, histopathological images and also in the detection of diabetic retinopathy. The overview of deep learning algorithms in cancer diagnosis, challenges and future scope is also highlighted in this work.
Systems biology models to identify the influence of SARS-CoV-2 infections to different types of human diseases
We carried out the transcriptomic analytical framework to delve into the SARS-CoV-2 impacts on different. We analyzed both gene expression microarray and RNA-Seq datasets from SARS-CoV-2 and different affected tissue. With neighborhood-based benchmarks and multilevel network topology, we obtained dysfunctional signaling and ontological pathways, gene-disease (disease some) association network, and protein-protein interaction network (PPIN), uncovered essential shared infection recurrence connectivities with biological insights underlying between SARS-CoV-2 and different complex disease. We will find common DEGs for SARS-CoV-2 and different diseases and common DEGs with bimolecular networks revealed hub protein. We will suggest potential drugs for diseases.
Developing Effective Models from DNA/RNA/Protein data for different types of problems
Understanding the effects of genetic variation is a fundamental problem in biology that requires methods to analyze both physical and functional consequences of sequence changes at systems-wide and mechanistic scales. Protein interaction networks map which proteins physically interact to achieve a systems view, while genetic interaction networks inform on the phenotypic consequences of perturbing these protein interactions. Until recently, understanding the molecular mechanisms that underlie these interactions often required biophysical methods to determine the structures of the proteins involved. Here, we will use large-scale genetic datasets and deep learning approaches to model protein structures and their interactions and discuss the integration of structural data from different sources