Machine learning analysis of single cell data from Wilms' tumors
ניתוח נתוני סינגל-סל מגידולי ויילמס ע"י כלים מלמידת מכונה
הרקע לפרויקט:
Machine learning plays a transformative role in analyzing single-cell data, helping researchers uncover meaningful patterns from vast and complex datasets. In single-cell studies, machine learning algorithms can cluster cells based on similarities in their molecular profiles, revealing hidden cell types and unique states within mixed populations. It also excels at identifying relationships and patterns that would be difficult to detect manually, such as finding key genes driving cell behavior or predicting how cells might respond to different treatments. These insights enable scientists to better understand cellular processes and diseases, ultimately leading to advances in targeted therapies and personalized medicine.
מטרת הפרויקט:
This project will leverage cutting-edge machine learning algorithms to analyze single-cell data from Wilms tumors, a pediatric kidney cancer. By applying advanced clustering and predictive models, we aim to map the cellular landscape of Wilms tumors at an unprecedented level of detail, identifying specific cell types, subtypes, and cellular states. Machine learning will be used to pinpoint key genes and pathways that drive tumor growth, differentiation, and response to treatments. The insights gained could reveal novel biomarkers and therapeutic targets, paving the way for more effective, personalized treatments for young patients with Wilms tumors.
תכולת הפרויקט:
Reading of relevant literature, data preprocessing, PCA and other dimensionality reduction techniques, gene expression and regulatory analysis, and implementation of advanced downstream models.
קורסי קדם:
מומלץ: ביולוגיה חישובית/ביואינפורמטיקה או ניורו גנומיקה (אפשר גם במקביל)
דרישות נוספות:
רקע בסיסי בפייתון או R
מקורות:
תאריך עדכון אחרון : 20/11/2024
