AI-Driven Transcriptome Prediction in Human Pathology: From Molecular Insights to Clinical Applications

作者：Xiao‐Ya Chen, Huinan Xu, Shengjie Yu, Hu Wan, Zhongjin Zhang, Xue Wang, Yue Yuan, Mingyue Wang, Liang Chen, Xiumei Lin, Yinlei Hu, Pengfei Cai · 发表于：Biology · 年份：2025 · DOI：10.3390/biology14060651 · 被引用次数：11 · 研究领域：Cancer Genomics and Diagnostics、AI in cancer detection、Gene expression and cancer classification

Gene expression regulation underpins cellular function and disease progression, yet its complexity and the limitations of conventional detection methods hinder clinical translation. In this review, we define "predict" as the AI-driven inference of gene expression levels and regulatory mechanisms from non-invasive multimodal data (e.g., histopathology images, genomic sequences, and electronic health records) instead of direct molecular assays. We systematically examine and analyze the current approaches for predicting gene expression and diagnosing diseases, highlighting their respective advantages and limitations. Machine learning algorithms and deep learning models excel in extracting meaningful features from diverse biomedical modalities, enabling tools like PathChat and Prov-GigaPath to improve cancer subtyping, therapy response prediction, and biomarker discovery. Despite significant progress, persistent challenges-such as data heterogeneity, noise, and ethical issues including privacy and algorithmic bias-still limit broad clinical adoption. Emerging solutions like cross-modal pretraining frameworks, federated learning, and fairness-aware model design aim to overcome these barriers. Case studies in precision oncology illustrate AI's ability to decode tumor ecosystems and predict treatment outcomes. By harmonizing multimodal data and advancing ethical AI practices, this field holds immense potential to propel personalized medicine forward, although further innovation is n...