Feature selection and predictive model in complex diseases
Drug repurposing methods based on artificial intelligence algorithms
Innovative designs and statistical methods development in clinical trials
1. General Project of the National Natural Science Foundation of China (81773550), Study on Feature Extraction and Application of Dynamic Multi-Mode Data Based on Deep Neural Network (2018/01-2021/12), host.
2. General Project of National Natural Science Foundation of China (81573256) research on Multi-scale High-dimensional Data Variable Screening and Prediction Model Based on Structural Group Sparse Algorithm (2016/01-2019/12), hosted.
3. Youth Project of National Natural Science Foundation of China (81102201) research on P-P Curve Model and Analysis Method Based on Multi-Stage Design of Anti-tumor New Drug (2012/01-2014/12), hosted.
4. A selective project funded by returnees from Heilongjiang Province: A Multi-time Point Prediction Model and Verification of Chemotherapy Sensitivity of Cervical Cancer Based on Sparse Algorithm of Structure Group (2018/01-202/12), hosted.
5. General Project of The National Natural Science Foundation of China (81473072) research on The Analysis Method and Application of Omics Data Fusion Based on Network Deconvolution and Bayesian Model (2015/01-2018/12), participated.
1. Cao L, Yang J, Rong Z, Li L, Xia B, You C, Lou G, Jiang L, Du C, Meng H, Wang W, Wang M, Li K, Hou Y. A novel attention-guided convolutional network for the detection of abnormal cervical cells in cervical cancer screening. Medical Image Analysis. 2021.
2. Sun Y, Hou Y, Lv N, Liu Q, Lin N, Zhao S, Chu X, Chen X, Cheng G, Li P. Circulating lncRNA BC030099 Increases in Preeclampsia Patients. Molecular Therapy-Nucleic Acids. 2019; 14:562-6.
3. Zhao W, Zhao F, Yang K, Lu Y, Zhang Y, Wang W, Xie H, Deng K, Yang C, Rong Z, Hou Y, Li K. An immunophenotyping of renal clear cell carcinoma with characteristics and a potential therapeutic target for patients insensitive to immune checkpoint blockade. Journal of cellular biochemistry. 2019; 120(8): 13330-13341.
4. Yang C, Zhang M, Cai Y, Rong Z, Wang C, Xu Z, Xu H, Song W, Hou Y, Lou G. Platelet-derived growth factor-D expression mediates the effect of differentiated degree on prognosis in epithelial ovarian cancer. Journal of Cellular Biochemistry. 2019;120(5):6920-5.
5. Lu X, Li Y, Xia B, Bai Y, Zhang K, Zhang X, Xie H, Sun F, Hou Y, Li K. Selection of small plasma peptides for the auxiliary diagnosis and prognosis of epithelial ovarian cancer by using UPLC/MS-based nontargeted and targeted analyses. International Journal of Cancer. 2019;144(8):2033-42.
6. Zhang F, Zhang Y, Ke C, Li A, Wang W, Yang K, Liu H, Xie H, Deng K, Zhao W, Yang C, Lou G, Hou Y, Li K. Predicting ovarian cancer recurrence by plasma metabolic profiles before and after surgery. Metabolomics. 2018;14(5).
7. Wang C, Yang C, Wang W, Xia B, Li K, Sun F, Hou Y. A Prognostic Nomogram for Cervical Cancer after Surgery from SEER Database. Journal of Cancer. 2018;9(21):3923-8.
8. Deng K, Yang C, Tan Q, Song W, Lu M, Zhao W, Lou G, Li Z, Li K, Hou Y. Sites of distant metastases and overall survival in ovarian cancer: A study of 1481 patients. Gynecologic Oncology. 2018;150(3):460-5.
9. Ke C, Hou Y, Zhang H, Fan L, Ge T, Guo B, Zhang F, Yang K, Wang J, Lou G, Li K. Large-scale profiling of metabolic dysregulation in ovarian cancer. International Journal of Cancer. 2015;136(3):516-26.
10. Hou Y, Yin M, Sun F, Zhang T, Zhou X, Li H, Zheng J, Chen X, Li C, Ning X, Lou G, Li K. A metabolomics approach for predicting the response to neoadjuvant chemotherapy in cervical cancer patients. Molecular Biosystems. 2014;10(8):2126-33.