Several factors, including advances in computational algorithms, the availability of high-performance computing hardware, and the assembly of large community-based databases, have led to the extensive application of Artificial Intelligence (AI) in the biomedical domain for nearly 20 years. AI algorithms have attained expert-level performance in cancer research. However, only a few AI-based applications have been approved for use in the real world. Whether AI will eventually be capable of replacing medical experts has been a hot topic. In this article, we first summarize the cancer research status using AI in the past two decades, including the consensus on the procedure of AI based on an ideal paradigm and current efforts of the expertise and domain knowledge. Next, the available data of AI process in the biomedical domain are surveyed. Then, we review the methods and applications of AI in cancer clinical research categorized by the data types including radiographic imaging, cancer genome, medical records, drug information and biomedical literatures. At last, we discuss challenges in moving AI from theoretical research to real-world cancer research applications and the perspectives toward the future realization of AI participating cancer treatment.

Immune tolerance is one of the leading causes of chemotherapy resistance in carcinoma cases. Studies have shown that programmed cell death ligand‐1 (PD‐L1), an inhibitory molecule expressed by cancer cells, plays a significant role in immune tolerance through the induction of T cell dysfunction. The results of our RNA sequencing in previous studies revealed that microRNA‐145 (miR‐145), which is known to be down‐regulated by cisplatin in cisplatin‐resistant ovarian cancer cells, also represses gene PD‐L1 expression. However, the mechanism by which miR‐145 contributes to regulate PD‐L1 expression in cisplatin resistance of ovarian cancer is yet to be fully understood. Here, we show that cisplatin‐mediated miR‐145 down‐regulation increased PD‐L1 expression via targeting the c‐Myc transcription factor, thereby inducing T cell apoptosis in vitro. We also report that expression of miR‐145 is negatively correlated with PD‐L1 expression in human ovarian cancer tissues, malignant grades and the recurrent risks of ovarian cancer after chemotherapy. In summary, our findings suggest that the miR‐145/c‐Myc/PD‐L1 axis contributes to cisplatin resistance in ovarian cancer and support that miR‐145 might act as an adjuvant therapeutic target in chemotherapy of ovarian cancer.

Esophageal squamous cell carcinoma (ESCC) represents an aggressive malignancy often accompanied with a poor prognosis. Owing to the poor mortality and morbidity rates associated with this malignancy, a deeper understanding of the finer molecular changes that occur in ESCC is required in order to identify novel potential targets for early detection and therapy. At present the mechanism by which ESCC functions on a molecular level is not fully understood. Hence, the aim of the present study was to ascertain as to whether microRNA-384 (miR-384) influences the progression of ESCC. Bioinformatics analysis was initially conducted to identify ESCC-related differentially expressed genes and predict regulatory miRs. After the target relationship between miR-384 and LIMK1 had been verified, the expression of miR-384 and LIMK1 in the EC9706 cell line was altered in an attempt to investigate the regulatory roles of miR-384 in the expression of the LIMK1/cofilin signaling pathway-related genes, cell proliferation, invasion, cell cycle distribution and apoptosis, in addition to lymph node metastasis (LNM) and tumor growth in nude mice. Microarray-based gene expression profiling indicated that miR-384 affected the progression of ESCC through the LIMK1-mediated LIMK1/cofilin signaling pathway. Furthermore, miR-384 and Bax were observed to be poorly expressed, while LIMK1, cofilin and Bcl-2 were highly expressed in ESCC. The obtained evidences indicating that miR-384 targeted and negatively regulated LIMK1. Upregulation of miR-384 or LIMK1 inhibition was determined to block the LIMK1/cofilin signaling pathway, repress cell proliferation, invasion, cell cycle, LNM and tumor growth, while promote cell apoptosis in ESCC. Collectively, based on the key findings of the study, miR-384 could sequester LIMK1, which acts to suppress activation of the LIMK1/cofilin signaling pathway, thus ultimately inhibiting the development and progression of ESCC.

Machine learning (ML) methods have been widely explored for predicting material properties. However, due to the rapid development of ML techniques and the diversity of available models, performance comparisons between traditional and graph-based machine learning models remain limited. Therefore, we evaluate 11 conventional ML models alongside the graph neural network-based Crystal Graph Convolutional Neural Network (CGCNN) for predicting three key properties—formation energy (E_f), band gap (E_g), and energy above hull (E_h)—across a dataset comprising single perovskites, double perovskites, and their combined structures. The results demonstrate that for single perovskites, CGCNN exhibits gains of over 20% in the root mean square error (RMSE) relative to the second-best model (Gradient Boosting Regression), achieving values of 0.205 eV/atom (E_f), 0.718 eV (E_g), and 0.167 eV/atom (E_h). Prediction accuracy for double perovskites is significantly enhanced by training CGCNN on a combined dataset, particularly for E_h, where the coefficient of determination (R²) improves approximately 68.1-fold compared to models trained exclusively on double-perovskite data. Feature importance analysis via one-shot, permutation-based, and recursive feature elimination (RFE) methods reveals that optimal model performance requires retention of at least the top 20 critical features. Furthermore, feature utilization patterns of CGCNN across different prediction tasks are visualized. This work provides actionable guidelines for model selection and feature engineering in perovskite property prediction, establishing a benchmark for future ML-driven materials discovery.

Brucellar spondylitis (BS), a prevalent zoonotic disease caused by Brucella, poses a significant global health threat. Accurate and timely diagnosis of BS is crucial for effective treatment; however, no specialized deep learning model has been developed for detecting BS in MR images. In this study, we proposed Brucella Spondylitis MRI Diagnosis Network (BSMRINet), a fully automated diagnostic framework designed for the detection of BS from T2-weighted (T2W) MR images. The model was developed and validated using 582 cohorts collected from four hospitals between January 2018 and August 2023. The BSMRINet architecture comprised two key modules. The vertebral body lesion detection module was designed to detect BS in intact vertebral bodies by integrating a corner detection algorithm with a ResNet-based deep learning model. This module provided accurate identification and localization of potential lesions of Brucella and calculated intervertebral disc height (DH) values. The spine lesion detection module was specifically designed to detect BS in damaged vertebral bodies by utilizing a DenseNet architecture with modified squeeze-and-excitation (scSE) networks. This module further evaluated paravertebral injuries, including abscess formation, soft tissue swelling, and joint involvement. BSMRINet demonstrated strong robustness and generalization across both internal and external validation phases. Additionally, it outperformed two radiologists with 10 to 15 years of experience in diagnosing spinal MR images. The results suggested that BSMRINet can assist in the diagnostic process of BS and enhance the diagnostic capabilities of radiologists.