A5101806898- Explainable Artificial Intelligence (XAI)
- Adversarial Robustness in Machine Learning
- Pancreatic and Hepatic Oncology Research
- Cancer Cells and Metastasis
- COVID-19 diagnosis using AI
- Cancer Genomics and Diagnostics
- Advanced Malware Detection Techniques
- RNA modifications and cancer
- Cancer, Hypoxia, and Metabolism
- Epigenetics and DNA Methylation
- Ferroptosis and cancer prognosis
- AI in cancer detection
- Maritime Transport Emissions and Efficiency
- Cancer Immunotherapy and Biomarkers
- Multi-Criteria Decision Making
- Machine Learning in Materials Science
- Imbalanced Data Classification Techniques
- Machine Learning in Healthcare
- Thyroid Cancer Diagnosis and Treatment
- Security and Verification in Computing
- Anomaly Detection Techniques and Applications
- Technology Assessment and Management
- Time Series Analysis and Forecasting
Iowa State University
2024-2025
Fudan University Shanghai Cancer Center
2025
Shanghai Medical College of Fudan University
2025
Shanghai Cancer Institute
2025
Sichuan University
2022
First Affiliated Hospital of Shantou University Medical College
2021
Shantou University
2021
Abstract Machine unlearning is a cutting‐edge technology that embodies the privacy legal principle of right to be forgotten within realm machine learning (ML). It aims remove specific data or knowledge from trained models without retraining scratch and has gained significant attention in field artificial intelligence recent years. However, development research associated with inherent vulnerabilities threats, posing challenges for researchers practitioners. In this article, we provide first...
The DNA damage response (DDR) has a major impact on the development and progression of pancreatic ductal adenocarcinoma (PDAC). Investigating biomarkers linked to DDR may facilitate prognostic assessment prediction immunological characteristics for patients with PDAC. single-cell RNA sequencing (scRNA-seq) dataset GSE212966 was obtained from GEO database, whereas bulk RNA-seq data were sourced Cancer Genome Atlas (TCGA) Genotype-Tissue Expression (GTEx) databases. Least absolute shrinkage...
Despite the recent progress in deep neural networks (DNNs), it remains challenging to explain predictions made by DNNs. Existing explanation methods for DNNs mainly focus on post-hoc explanations where another explanatory model is employed provide explanations. The fact that can fail reveal actual original reasoning process of raises need build with built-in interpretability. Motivated this, many self-explaining have been proposed generate not only accurate but also clear and intuitive...
While increased glycolysis has been identified as a cancer marker and attracted much attention in thyroid (THCA), the prognostic role of it remains to be further elucidated. Here we aimed determine specific glycolysis-associated risk model predict THCA patients' survival. We also explored interaction between this signature tumor immune microenvironment performed drug screening identify drugs targeting signature. Six genes (CHST6, POM121C, PPFIA4, STC1, TGFBI, FBP2) comprised model, which was...
Despite the recent progress in deep neural networks (DNNs), it remains challenging to explain predictions made by DNNs. Existing explanation methods for DNNs mainly focus on post-hoc explanations where another explanatory model is employed provide explanations. The fact that can fail reveal actual original reasoning process of raises need build with built-in interpretability. Motivated this, many self-explaining have been proposed generate not only accurate but also clear and intuitive...
With the increasing number of financial transactions, fraud has become increasingly serious for institutions and public. The core idea this model is to integrate multiple neural network structures utilize their respective advantages improve performance detection. Firstly, we employed convolutional with interpretable blocks (CNNIB) (CNN) extract key features from data capture patterns in cases. Secondly, introduced autoencoder generative adversarial (AE-GAN) perform feature analysis on...