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DYNAMIC BEHAVIORAL MODELING FOR STRONGLY NONLINEAR DOHERTY PAS USING REAL-VALUED TIME-DELAY RECURRENT RBF MODEL

Journal of Electronics(China) 2012年第1期29卷 39-45页

作者： Hui Ming Liu Taijun Ye Yan Zhang Haili Shen Dongya Li Liang department of information scienc eand engineering Ningbo UniversityNingbo 315211China Zhejiang College of Textile&Fashion Ningbo 315211China department of information science and engineering Yunnan UniversityKunming 650091China

This paper proposes a Real-Valued Time-Delay Recurrent Radial Basis Function (RVTDRRBF) model suitable for dynamic modeling of the strongly nonlinear behaviors of the Doherty Power Amplifiers (DPAs). This model has four Tapped Delay Lines (TDLs), which account for the memory effect of the DPA. The structure of the RVTDRRBF model is simpler than the traditional FeedForward Neural Networks (FFNNs) model. Weights and centers of the proposed model can be resolved by the Orthogonal Least Square (OLS) and Singular Value De-composition (SVD) algorithm. A three-carrier Wideband Code Division Multiple Access (WCDMA) signal is taken as the test signal. The simulation results in frequency-domain and time-domain for a DPA with 51 dBm output illustrate a good agreement between the RVTDRRBF model and measurement data. Moreover, comparing the Normalized Mean Square Error (NMSE) of RVTDRRBF model, memory polynomial model and RVTDRBF model, it can be noticed that the proposed RVTDRRBF model is more accurate than the RVTDRBF model and the memory polynomial model in modeling the strong dynamic nonlinearity of the DPAs.

关键词： Radial Basis Function (RBF) Power Amplifiers (PAs) Memory polynomial Doherty

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Prediction of Driver Gene Matching in Lung Cancer NOG/PDX Models Based on Artificial Intelligence

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engineering 2022年第8期8卷 102-114页

作者： Yayi He Haoyue Guo Li Diao Yu Chen Junjie Zhu Hiran C.Fernando Diego Gonzalez Rivas Hui Qi Chunlei Dai Xuzhen Tang Jun Zhu Jiawei Dai Kan He Dan Chan Yang Yang School of Medicin5 Tongji UniversityShanghai 200092China department of Medical Oncology Shanghai Pulmonary HospitalSchool of Medicin5Tongji UniversityShanghai 200433China department of Automation School of Electronic Information and Electrical Engineerin7Shanghai Jiao Tong UniversityShanghai 200240China Spine Center Orthopedic DepartmentShanghai Changzheng HospitalShanghai 200003China department of Thoracic Surgery Shanghai Pulmonary HospitalSchool of Medicin5Tongji UniversityShanghai 200433China School of Materials science and Engineerin7 Tongji UniversityShanghai 201804China department of Thoracic Surgery Allegheny General HospitalPittsburg8PA 15212USA department of Thoracic Surgery and Minimally Invasive Thoracic Surgery Unit(UCTMI) Coruna University HospitalCoruna 15006Spain Oncology and Immunology BU Research Service DivisionWuXi Appte3Shanghai 200131China SJTU–Yale Joint Center for Biostatistics and Data scienc5 Department of Bioinformatics and BiostatisticsSchool of Life Sciences and BiotechnologyShanghai Jiao Tong UniversityShanghai 200240China Division of Medical Oncology Department of Medicin5University of Colorado Anschutz Medical CampusAuror1CO 80045USA

Patient-derived tumor xenografts(PDXs)are a powerful tool for drug discovery and screening in ***,current studies have led to little understanding of genotype mismatches in PDXs,leading to massive economic ***,we established PDX models from 53 lung cancer patients with a genotype matching rate of 79.2%(42/53).Furthermore,17 clinicopathological features were examined and input in stepwise logistic regression(LR)models based on the lowest Akaike information criterion(AIC),least absolute shrinkage and selection operator(LASSO)-LR,support vector machine(SVM)recursive feature elimination(SVM-RFE),extreme gradient boosting(XGBoost),gradient boosting and categorical features(Cat Boost),and the synthetic minority oversampling technique(SMOTE).Finally,the performance of all models was evaluated by the accuracy,area under the receiver operating characteristic curve(AUC),and F1 score in 100 testing *** multivariable LR models revealed that age,number of driver gene mutations,epidermal growth factor receptor(EGFR)gene mutations,type of prior chemotherapy,prior tyrosine kinase inhibitor(TKI)therapy,and the source of the sample were powerful ***,Cat Boost(mean accuracy=0.960;mean AUC=0.939;mean F1 score=0.908)and the eight-feature SVM-RFE(mean accuracy=0.950;mean AUC=0.934;mean F1 score=0.903)showed the best performance among the ***,application of the SMOTE improved the predictive capability of most models,except Cat *** on the SMOTE,the ensemble classifier of single models achieved the highest accuracy(mean=0.975),AUC(mean=0.949),and F1 score(mean=0.938).In conclusion,we established an optimal predictive model to screen lung cancer patients for non-obese diabetic(NOD)/Shi-scid,interleukin-2 receptor(IL-2R)γ^(null)(NOG)/PDX models and offer a general approach for building predictive models.

关键词： Machine learning Patient-derived tumor xenografts NOG mice

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