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Dr. Pawar Shrikant

Biography

Pawar is an Assistant Professor (tenure-track) in Department of Computer Science and Biology at Â鶹´«Ã½ University. His research is focused on following broad topics:

1) Identifying cancer drug targets utilizing construction of complex interactome of proteins ( & ): The approach for CaI construction and analysis. (a) The three databases BIOCARTA, PID, REACTOME and KEGG utilised for the extraction of pathways followed by disintegration into protein constituents and identification of any other pathways they are involved with. (b) The meta database STRING for finding the interactions of all the proteins pooled above. (c) The large component of the CaI constructed from pooled interactions above, coloured by 335 modules by Rosvall Algorithm with node size plotted as per degree (d) The analyses for power law, K-core, inter- and intra-modular connectivities for CaI constructed followed by the drug statuses against centrality measures, in clockwise manner. The three SPINs and GPINs of A. baumannii reflecting the degree of connectivity. SPINs are represented in blue spheres connected through blue-colored curved lines for (A) VaCAB, having vaccine candidates; (B) ViFAB, with virulent factors; and (C) KeFAB, with key factors each with their interactors. (D) GPIN with proteins represented in black spheres connected with black curved lines to form the interactome. The technical analysis of the constructed CaI. (a) Complementary Cumulative Degree Distribution (CCDF) of CaI showing Power-Law behaviour. (b) K-core analysis of CaI representing the size of each k-shell (number of proteins appearing in k-core but not in k + 1th core) from periphery (k = 1) to inner core (k-max). (c) Classification of CaI proteins (R) based on its role and region in network space, the P-Z space classified into 7 categories of hub and non-hub nodes. The latter has been assigned as ultra-peripheral (R1), peripheral (R2), non-hub connector (R3) and non-hub kinless nodes (R4) and the former has been assigned as provincial (R5), connector (R6) and kinless hubs (R7) as described by Guimera et al. Kinless hubs nodes are supposed to be important in term of functionality, which has high connection within module as well as between modules.

2) Convolutional Neural Networks: Analysis of drug resistance from deep sequencing data (). Restricted Boltzmann Machines are an effective machine learning tool for classification of genomic and structural data. They can also be used to compare resistance profiles of different protease inhibitors.

3) Prognostic Model Predicts Survival in Cancer Patients (): An optimal prognostic model by the combination of six mRNAs was established. Kaplan–Meier survival analysis revealed effective risk stratification by this model for patients in the two datasets. The area under ROC curve (AUC) was > 0.65 for training and validation datasets, indicating good sensitivity and specificity of this model. Moreover, prominent superiority of this model to investigate prognostic biomarkers was demonstrated.

4) Regression and Tree Based Classification Models: Common cancer biomarkers identified through artificial intelligence (). Identification of biomarker genes. (a) Heat map showing expression levels of top 25 cancer biomarker genes in ovarian and breast cancer types, (b) variable importance with gene ranks for all the genes, (c) mean decrease gini value for top 25 biomarker genes.

5) Deep Learning & Computer Vision: Bounding box algorithms are useful in localization of image patterns. Recently, utilization of convolutional neural networks on X-ray images has proven a promising disease prediction technique. However, pattern localization over prediction has always been a challenging task with inconsistent coordinates, sizes, resolution and capture positions of an image. In this article, we present a unique approach of SSD with a VGG-16 network as a backbone for feature detection of bounding box algorithm to predict the location of an anomaly within chest X-ray image ( & ):

Education

1. Doctor of Philosophy (Focused research: Bioinformatics): Georgia State University, Atlanta, Georgia, USA
2. Master of Science (Focused research: Computer Science General): Georgia State University, Atlanta, Georgia, USA
3. Master of Science (Focused research: Biology General): Georgia State University, Atlanta, Georgia, USA
4. Master of Science (Focused research: Bioinformatics): Western Kentucky University, Bowling Green, Kentucky, USA

• Doctoral Dissertation:
  Goal: Identifying newer bioinformatics techniques for studying drug resistance in HIV & Staphylococcus aureus. Dissertation published .
• Master’s Degree (Computer Science) Project:
  Goal: Computational optimization of defined graph-based sequence structure HIV-1 protease resistance prediction through supervised, unsupervised machine learning techniques.
• Master’s Degree (Bioinformatics) Project:
  Goal: Transcriptomic data analysis to determine the impact of antioxidant supplementation on gene expression in brains of mice infected with T. gondii. Application of linear transformations like data driven Haar-Fisz transformations on microarray datasets for identifying gene expression trends.

Recent Publications

2021: