1
|
Jiang Y, Wang C, Zhou S. Artificial intelligence-based risk stratification, accurate diagnosis and treatment prediction in gynecologic oncology. Semin Cancer Biol 2023; 96:82-99. [PMID: 37783319 DOI: 10.1016/j.semcancer.2023.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 08/27/2023] [Accepted: 09/25/2023] [Indexed: 10/04/2023]
Abstract
As data-driven science, artificial intelligence (AI) has paved a promising path toward an evolving health system teeming with thrilling opportunities for precision oncology. Notwithstanding the tremendous success of oncological AI in such fields as lung carcinoma, breast tumor and brain malignancy, less attention has been devoted to investigating the influence of AI on gynecologic oncology. Hereby, this review sheds light on the ever-increasing contribution of state-of-the-art AI techniques to the refined risk stratification and whole-course management of patients with gynecologic tumors, in particular, cervical, ovarian and endometrial cancer, centering on information and features extracted from clinical data (electronic health records), cancer imaging including radiological imaging, colposcopic images, cytological and histopathological digital images, and molecular profiling (genomics, transcriptomics, metabolomics and so forth). However, there are still noteworthy challenges beyond performance validation. Thus, this work further describes the limitations and challenges faced in the real-word implementation of AI models, as well as potential solutions to address these issues.
Collapse
Affiliation(s)
- Yuting Jiang
- Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE and State Key Laboratory of Biotherapy, West China Second Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan 610041, China; Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Chengdi Wang
- Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE and State Key Laboratory of Biotherapy, West China Second Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan 610041, China; Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Shengtao Zhou
- Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE and State Key Laboratory of Biotherapy, West China Second Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan 610041, China; Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
| |
Collapse
|
2
|
Xu H, Jia J, Jeong HH, Zhao Z. Deep learning for detecting and elucidating human T-cell leukemia virus type 1 integration in the human genome. PATTERNS (NEW YORK, N.Y.) 2023; 4:100674. [PMID: 36873907 PMCID: PMC9982299 DOI: 10.1016/j.patter.2022.100674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 11/02/2022] [Accepted: 12/13/2022] [Indexed: 02/12/2023]
Abstract
Human T-cell leukemia virus type 1 (HTLV-1), a retrovirus, is the causative agent for adult T cell leukemia/lymphoma and many other human diseases. Accurate and high throughput detection of HTLV-1 virus integration sites (VISs) across the host genomes plays a crucial role in the prevention and treatment of HTLV-1-associated diseases. Here, we developed DeepHTLV, the first deep learning framework for VIS prediction de novo from genome sequence, motif discovery, and cis-regulatory factor identification. We demonstrated the high accuracy of DeepHTLV with more efficient and interpretive feature representations. Decoding the informative features captured by DeepHTLV resulted in eight representative clusters with consensus motifs for potential HTLV-1 integration. Furthermore, DeepHTLV revealed interesting cis-regulatory elements in regulation of VISs that have significant association with the detected motifs. Literature evidence demonstrated nearly half (34) of the predicted transcription factors enriched with VISs were involved in HTLV-1-associated diseases. DeepHTLV is freely available at https://github.com/bsml320/DeepHTLV.
Collapse
Affiliation(s)
- Haodong Xu
- Center for Precision Health, School of Biomedical Informatics, UTHealth Science Center at Houston, Houston, TX 77030, USA
| | - Johnathan Jia
- Center for Precision Health, School of Biomedical Informatics, UTHealth Science Center at Houston, Houston, TX 77030, USA.,MD Anderson UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Hyun-Hwan Jeong
- Center for Precision Health, School of Biomedical Informatics, UTHealth Science Center at Houston, Houston, TX 77030, USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, UTHealth Science Center at Houston, Houston, TX 77030, USA.,MD Anderson UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA.,Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, USA
| |
Collapse
|
4
|
Yu L, Zhang Y, Xue L, Liu F, Chen Q, Luo J, Jing R. Systematic Analysis and Accurate Identification of DNA N4-Methylcytosine Sites by Deep Learning. Front Microbiol 2022; 13:843425. [PMID: 35401453 PMCID: PMC8989013 DOI: 10.3389/fmicb.2022.843425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Accepted: 02/21/2022] [Indexed: 11/13/2022] Open
Abstract
DNA N4-methylcytosine (4mC) is a pivotal epigenetic modification that plays an essential role in DNA replication, repair, expression and differentiation. To gain insight into the biological functions of 4mC, it is critical to identify their modification sites in the genomics. Recently, deep learning has become increasingly popular in recent years and frequently employed for the 4mC site identification. However, a systematic analysis of how to build predictive models using deep learning techniques is still lacking. In this work, we first summarized all existing deep learning-based predictors and systematically analyzed their models, features and datasets, etc. Then, using a typical standard dataset with three species (A. thaliana, C. elegans, and D. melanogaster), we assessed the contribution of different model architectures, encoding methods and the attention mechanism in establishing a deep learning-based model for the 4mC site prediction. After a series of optimizations, convolutional-recurrent neural network architecture using the one-hot encoding and attention mechanism achieved the best overall prediction performance. Extensive comparison experiments were conducted based on the same dataset. This work will be helpful for researchers who would like to build the 4mC prediction models using deep learning in the future.
Collapse
Affiliation(s)
- Lezheng Yu
- School of Chemistry and Materials Science, Guizhou Education University, Guiyang, China
| | - Yonglin Zhang
- Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou, China
| | - Li Xue
- School of Public Health, Southwest Medical University, Luzhou, China
| | - Fengjuan Liu
- School of Geography and Resources, Guizhou Education University, Guiyang, China
| | - Qi Chen
- Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Jiesi Luo
- Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou, China.,Department of Pharmacy, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Runyu Jing
- School of Cyber Science and Engineering, Sichuan University, Chengdu, China
| |
Collapse
|
5
|
Liang J, Cui Z, Wu C, Yu Y, Tian R, Xie H, Jin Z, Fan W, Xie W, Huang Z, Xu W, Zhu J, You Z, Guo X, Qiu X, Ye J, Lang B, Li M, Tan S, Hu Z. DeepEBV: A deep learning model to predict Epstein-Barr virus (EBV) integration sites. Bioinformatics 2021; 37:3405-3411. [PMID: 34009299 DOI: 10.1093/bioinformatics/btab388] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 03/26/2021] [Accepted: 05/17/2021] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Epstein-Barr virus (EBV) is one of the most prevalent DNA oncogenic viruses. The integration of EBV into the host genome has been reported to play an important role in cancer development. The preference of EBV integration showed strong dependence on the local genomic environment, which enables the prediction of EBV integration sites. RESULTS An attention-based deep learning model, DeepEBV, was developed to predict EBV integration sites by learning local genomic features automatically. First, DeepEBV was trained and tested using the data from the dsVIS database. The results showed that DeepEBV with EBV integration sequences plus Repeat peaks and 2 fold data augmentation performed the best on the training dataset. Furthermore, the performance of the model was validated in an independent dataset. In addition, the motifs of DNA-binding proteins could influence the selection preference of viral insertional mutagenesis. Furthermore, the results showed that DeepEBV can predict EBV integration hotspot genes accurately. In summary, DeepEBV is a robust, accurate and explainable deep learning model, providing novel insights into EBV integration preferences and mechanisms. AVAILABILITY DeepEBV is available as open-source software and can be downloaded from https://github.com/JiuxingLiang/DeepEBV.gitSupplementary information Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Jiuxing Liang
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, China; Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Zifeng Cui
- Department of Gynaecological oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, China
| | - Canbiao Wu
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, China; Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Yao Yu
- Department of Urology, The First Medical Center of Chinese PLA General Hospital, Beijing 100853 China.,School of Medicine, Nankai University, Tianjin 300071, China
| | - Rui Tian
- Center for Translational Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, China
| | - Hongxian Xie
- STech Company Bio-X Lab, Zhuhai 519000, Guangdong, China
| | - Zhuang Jin
- Department of Gynaecological oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, China
| | - Weiwen Fan
- Department of Gynaecological oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, China
| | - Weiling Xie
- Department of Gynaecological oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, China
| | - Zhaoyue Huang
- Department of Gynaecological oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, China
| | - Wei Xu
- Department of Gynaecological oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, China
| | - Jingjing Zhu
- Department of Gynaecological oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, China
| | - Zeshan You
- Department of Gynaecological oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, China
| | - Xiaofang Guo
- Department of Medical Oncology of the Eastern Hospital, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510700, China
| | - Xiaofan Qiu
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, China; Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Jiahao Ye
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, China; Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China.,School of Computer Science, South China Normal University, Guangzhou 510631, China
| | - Bin Lang
- School of Health Sciences and Sports, Macao Polytechnic Institute, China
| | - Mengyuan Li
- Department of Gynaecological oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, China
| | - Songwei Tan
- School of Pharmacy, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Zheng Hu
- Department of Gynaecological oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, China.,Department of Obstetrics and Gynaecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China
| |
Collapse
|