Tiangen Chang

Path2Space predicts spatial gene expression and tumor microenvironment from histopathology slides, identifying new breast cancer subgroups and improving biomarker prediction.

This paper develops a machine learning framework, EXPRESSO, to predict treatment response from tumor transcriptomics across multiple cancer types and therapies, showing improved performance over existing methods.

This paper introduces TIME_ACT, a transcriptomic signature predicting immune checkpoint blockade response across cancers, validated using histopathology and multiple cohorts.

The paper develops a deep learning model to predict translation initiation and termination sites from RNA sequences, revealing new insights into translation control and identifying novel open reading frames.

This paper introduces LogiCAR designer, a computational method using single-cell transcriptomics to identify logic-gated antigen combinations for CAR therapy, showing improved efficacy and safety compared to existing approaches.

This study identifies M19-6 as a small molecule that disrupts the eIF4F complex, reducing melanoma growth and metastasis while sparing melanocytes.

The paper infers tumor immune status and predicts immunotherapy response from blood scRNA-Seq data in HNSCC patients.

This study maps the SCLC tumor microenvironment and identifies cancer-associated fibroblasts as key drivers of neuroendocrine state plasticity and poor prognosis.

The paper develops a mechanistic model of plant carbon nitrogen interaction to predict and optimize crop yield through targeted genetic manipulations.