bioinformatics

Topics: spatial transcriptomics, spatial domain identification, causal inference, gene intervention Skills: Programming Languages: Python (PyTorch preferred) Machine Learning: causal inference, representation learning, clustering Data Analysis: spatial transcriptomics preprocessing and evaluation (ARI, cross-slice generalization) Bioinformatics Knowledge (preferred): spatial transcriptomics, scRNA-seq, gene perturbation analysis Difficulty: Advanced Size: Large (350 hours) Mentors: Lijinghua Zhang (contact person) Project Idea Description Spatial domain identification is a core task in spatial transcriptomics (ST), aiming to segment tissue sections into biologically meaningful regions based on spatially resolved gene expression profiles.

Topics: computational pathology, spatial transcriptomics, gene expression prediction, mixture-of-experts, multimodal learning Skills: Programming Languages: Python; experience with PyTorch preferred Machine Learning: CNNs / vision encoders, mixture-of-experts, multimodal representation learning Data Analysis: handling large-scale histology image patches and gene expression matrices Bioinformatics Knowledge (preferred): familiarity with spatial transcriptomics or scRNA-seq data Difficulty: Advanced Size: Large (350 hours) Mentors: Ziheng Duan (contact person) Project Idea Description Histology imaging is one of the most widely available data modalities in biomedical research and clinical practice, capturing rich morphological information about tissues and disease states.

Topics: spatial transcriptomics, spatial domain identification, representation learning, model robustness Skills: Programming Languages: Python; PyTorch experience preferred Machine Learning: representation learning, clustering, robustness and stability analysis Data Analysis: spatial transcriptomics preprocessing and evaluation (ARI, clustering metrics) Bioinformatics Knowledge (preferred): familiarity with spatial transcriptomics or scRNA-seq data Difficulty: Advanced Size: Large (350 hours) Mentors: Ziheng Duan (contact person) Project Idea Description Spatial domain identification is a fundamental task in spatial transcriptomics (ST), aiming to partition tissue sections into biologically meaningful regions based on spatially resolved gene expression profiles.

Topics: bioinformatics, spatial transcriptomics, gene expression generation, retrieval-augmented generation, large models Skills: Programming Languages: Proficient in Python, and familiarity with machine learning libraries such as PyTorch. Data Analysis: Experience with spatial transcriptomics datasets and statistical modeling.

Hello! I’m Qianru! I have been contributing to the BenchmarkST: Cross-Platform, Multi-Species Spatial Transcriptomics Gene Imputation Benchmarking project under the mentorship of Ziheng Duan. My project aims to provide a standardized, easily accessible evaluation framework for gene imputation in spatial transcriptomics.

Hello there! I’m Qianru, and this is my mid-term blog post for the 2024 Google Summer of Code. I am working on the BenchmarkST project, focusing on benchmarking gene imputation methods in spatial transcriptomics.

Hello! My name is Qianru, and I will be working on a project to improve spatial transcriptomics during Google Summer of Code 2024. My project, Benchmarking Gene Imputation Methods for Spatial Transcriptomics, is mentored by Ziheng Duan and Cormac Flanagan.

Topics: bioinformatics, spatial transcriptomics, gene imputation, benchmarking, cross-platform/species analysis Skills: Programming Languages: Proficient in Python and/or R, commonly used in bioinformatics. Data Analysis: Experience with statistical data analysis and machine learning models.