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Machine learning approach to understand drivers of gene expression profile in bone organoids
We seek highly motivated Bachler/Master students dedicated to developing multi-omics data analysis pipeline and implementing a machine learning approach to exploit gene expression profiles of bone organoids.
Keywords: Bone Organoids, Transcriptomics, NGS, Bioinformatics, Data Analysis, Machine Learning, Statistics, Data Visualization
The advancement and development of organoid technology open up a new horizon for clinical drug testing and therapeutic strategy development in various diseases. The organoids are 3D structures that mimic the tissue and cellular architecture and can be maintained long-term while retaining genetic stability. Organoids are generated from multiple types of tissues, stem cells, and even different species. Recently, most research focused on establishing healthy and diseased organoids that provide an opportunity to explore in the laboratory. In this project, we focus on developing bone organoids derived from pediatric patients' human osteoblast cells by assessing their properties and cell viability upon mechanical loading. Further, we want to explore how precisely this organoid recapitulates the cell state-specific in gene expression landscapes. To address this, we will perform transcriptomic analysis applying mechanical loading/unloading-stimulation on bone organoids and explore gene expression patterns, functional regulation of the gene, and the signaling network. This project will be conducted closely with Dr. Gian Nutal Schädli in our group.
**Your profile:**
Candidates with particular interest and prior experience in NGS-related software, analyzing multi-omics data (e.g., RNA Seq), and programming skills like R/Bioconductor, Shell scripting, machine learning, and statistics
are beneficial.
The advancement and development of organoid technology open up a new horizon for clinical drug testing and therapeutic strategy development in various diseases. The organoids are 3D structures that mimic the tissue and cellular architecture and can be maintained long-term while retaining genetic stability. Organoids are generated from multiple types of tissues, stem cells, and even different species. Recently, most research focused on establishing healthy and diseased organoids that provide an opportunity to explore in the laboratory. In this project, we focus on developing bone organoids derived from pediatric patients' human osteoblast cells by assessing their properties and cell viability upon mechanical loading. Further, we want to explore how precisely this organoid recapitulates the cell state-specific in gene expression landscapes. To address this, we will perform transcriptomic analysis applying mechanical loading/unloading-stimulation on bone organoids and explore gene expression patterns, functional regulation of the gene, and the signaling network. This project will be conducted closely with Dr. Gian Nutal Schädli in our group.
**Your profile:** Candidates with particular interest and prior experience in NGS-related software, analyzing multi-omics data (e.g., RNA Seq), and programming skills like R/Bioconductor, Shell scripting, machine learning, and statistics are beneficial.
We aim to develop a data analysis pipeline to analyze the multi-omics data and implement a machine learning approach to predict various drug responses and identify the potential biomarker and gene network/pathways target.
We aim to develop a data analysis pipeline to analyze the multi-omics data and implement a machine learning approach to predict various drug responses and identify the potential biomarker and gene network/pathways target.
Please contact me to get more detailed information about the project by email or reach out in person at HCP H 13.3, Leopold-Ruzicka-Weg 4, 8093 Zürich, Switzerland.
**Email:** amit.singh@hest.ethz.ch and giannutal.schaedli@hest.ethz.ch
Please contact me to get more detailed information about the project by email or reach out in person at HCP H 13.3, Leopold-Ruzicka-Weg 4, 8093 Zürich, Switzerland.
**Email:** amit.singh@hest.ethz.ch and giannutal.schaedli@hest.ethz.ch
Each year the IDEA League offers the students of its partner universities over 180 monthly grants for a short-term research exchange. In general, these grants are awarded based on academic merit. For more information visit http://idealeague.org/student-grant/