Dr. Amit Singh

Project: Mechanoresponsive bone organoids a platform for studying bone diseases and therapeutics - Implemented RNA sequencing analysis pipeline and downstream analysis of osteogenesis imperfecta disease and healthy bone organoids - Mathematical models for optimizing experimental design and scale-up of bone organoids - Developing Machine learning elastic net model for drug prediction of human osteogenesis imperfecta disease - Provided consultation to experimentalists on data analysis

Project: In vivo single-cell mechanomics of bone adaptation and regeneration in the aging mouse -Implemented data preprocessing and analyzing pipeline for spatial transcriptomics data of mouse bone from in vivo studies - Development of Mechanoregulation analysis during bone fracture healing under mechanical loading -Supervised and mentored Bachelor’s and Master’s students on their research projects -Supported PhD and Postdoc with genomic data analysis and interpretation for their research projects

Projects involved: Single-cell RNA sequencing analysis of bone marrow endothelial cells -Developed ETL data pipeline for single-cell RNA sequence and bulk sequencing in a mouse model -Implemented unsupervised learning methods such as non-linear dimensionality reduction techniques to explore and visualize genomic data -Implemented a graph-based clustering approach (unsupervised learning) to identify differentially expressed features -Implemented data analysis pipelines to analyze proteomic data

Project: Dynamic modelling of circadian clock of Neurospora Crassa - Developed Data preprocessing pipeline to analyze time series genomic data in a high-performance computing environment - Developed a machine learning model to predict the circadian gene expression in N.Crassa genome - Developed a predictive mathematical model of the circadian clock of N.Crassa using ordinary differential equations based on experimental time-course data - Consulting experimental groups on data analysis topics

Project involved: Global gene expression profile of mouse endothelial cell subtypes of long bone - Performed statistical analysis of big genomic data and developed data processing pipeline for the analysis of mouse endothelial cell subtypes from embryonic and early postnatal long bone - Built supervised machine learning models to predict differentially regulated genes -Designed a computing strategy for a high-performance computing environment to process genomic data

Project involved: Unraveling the signaling mechanisms of PC12 Cell differentiation -Developed supervised machine learning models to predict differentially expressed genes between different conditions to study PC12 cell differentiation -Performed functional enrichment and clustering analysis to detect functionally related genes -Developed a logic based machine learning model to predict discrete dynamics of large-scale gene-protein networks to study PC12 cell differentiation

Project involved: Medical Systems Biology of Chronic Wounds - Built supervised machine learning models to predict differentially expressed genes between different conditions to study HGF-induced keratinocyte cell migration -Developed a logic based machine learning model to predict discrete dynamics of large-scale gene-protein networks to study HGF-induced keratinocyte cell migration

Projects involved: CALSYS-Systems Biology of Cancer and Ageing -Developed a predictive mathematical model of cell cycle protein P53 -Data mining and analysis of biological data from public databases and scientific publications

Project involved: Investigation of structure and function of GNL3L protein - Performed cell culture, gene cloning, and western blot of GNL3L protein