Ye, F., Hou, S., Fan, Y., Zhang, Y., Qian, Y., Sun, S., Peng, Q., Ju, M., Song, W., & Loparo, K. A.(2020).$\alpha$-Satellite: An AI-Driven System and Benchmark Datasets for Dynamic COVID-19 Risk Assessment in the United States.IEEE Journal of Biomedical and Health Informatics,24(10),2755-2764.
Sui, Y., Hess-Dunning, A., Sankaran, R., & Zorman, C.(2020).Inkjet-Printed Hydrogen Peroxide Sensor With Sensitivity Enhanced by Plasma Activated Inorganic Metal Salt Inks.Journal of Microelectromechanical Systems,29(5),1026-1031.
Yan, C., Nakane, K., WAng, X., Fu, Y., Lu, H., Fan, X., Feldman, M., Madabhushi, A., & Xue, Z.(2020).Automated gleason grading on prostate biopsy slides by statistical representations of homology profile.Computer Methods and Programs in Biomedicine,194
Chandramouli, S., Leo, P., Lee, G., Elliott, R., Davis, C., Zhu, G., Fu, P., Epstein, J., Veltri, R., & Madabhushi, A.(2020).Computer Extracted Features from Initial H&E Tissue Biopsies Predict Disease Progression for Prostate Cancer Patients on Active Surveillance.Cancers,12(9).
Shiradkar, R., Panda, A., Leo, P., Janowczyk, A., Farre, X., Janaki, N., Li, L., Pahwa, S., Mahran, A., Buzzy, C., Fu, P., Elliott, R., MacLennan, G., Ponsky, L., Gulani, V., & Madabhushi, A.(2020).Correction to: T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learningderived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology.European Radiology.
Shiradkar, R., Panda, A., Leo, P., Janowczyk, A., Farre, X., Janaki, N., Li, L., Pahwa, S., Mahran, A., Buzzy, C., Fu, P., Elliott, R., MacLennan, G., Ponsky, L., Gulani, V., & Madabhushi, A.(2020).T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learningderived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology.European Radiology.
Parizy, E., Bahrami, H., & Loparo, K. A.(2020).A Decentralized Three-Level Optimization Scheme for Optimal Planning of a Prosumer Nano-Grid.IEEE Transactions on Power Systems,35(5),3421-3432.
Shen, J., Zhou, T., Lai, J., Li, P., & Moh, S.(2020).Secure and Efficient Data Sharing in Dynamic Vehicular Networks.IEEE Internet of Things Journal,7(9),8208-8217.
Chen, Y., Janowczyk, A., & Madabhushi, A.(2020).Quantitative Assessment of the Effects of Compression on Deep Learning in Digital Pathology Image Analysis.JCO Clinical Cancer Informatics.
Zhang, J., Chen, L., Ye, F., Guo, G., Chen, R., Vanasse, A., & Wang, S.(2020).Survival neural networks for time-to-event prediction in longitudinal study.Knowledge and Information Systems,62(9),3727-3751.
Sajadi, A., Clark, K., & Loparo, K. A.(2020).Statistical Steady-State Stability Analysis for Transmission System Planning for Offshore Wind Power Plant Integration.Clean Technologies,2(3),311-332.
Qian, M., Almasan, A., & Gurkan Cavusoglu, E.(2020).Abstract 5499: Cell cycle-dependent, comprehensive mathematical modeling of the role of DNA repair in response to radiotherapy for prostate cancer.AACR Meeting and June,; Philadelphia, PA.
Algohary, A., Shiradkar, R., Pahwa, S., Purysko, A., Verma, S., Moses, D., Shnier, R., Haynes, A., Delprado, W., Thompson, J., Tirumani, S., Mahran, A., Rastinehad, A., Ponsky, L., Stricker, P., & Madabhushi, A.(2020).Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study.Cancers,12(8).
Feeny, A., Chung, M., Madabhushi, A., Attia, Z., Cikes, M., Firouznia, M., Friedman, P., Kalscheur, M., Kapa, S., Narayan, S., Noseworthy, P., Passman, R., Perez, M., Peters, N., Piccini, J., Tarakji, K., Thomas, S., Trayanova, N., Turakhia, M., & Wang, P.(2020).Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology.Circulation - Arrhythmia and Electrophysiology,13(8).
Algohary, A., Shiradkar, R., Pahwa, S., Purysko, A., Verma, S., Moses, D., Shnier, R., Haynes, A., Delprado, W., Thompson, J., Tirumani, S., Mahran, A., Rastinehad, A., Ponsky, L., Stricker, P., & Madabhushi, A.(2020).Combination of peri-tumoral and intra-tumoral radiomic features on bi-parametric mri accurately stratifies prostate cancer risk: A multi-site study.Cancers,12(8),1-14.
Zamani, H., Chan, S., Smith, C., & Mohseni, P.(2020).A Neurochemical Recording Microsystem with Analog Background Current Subtraction and 400V/s FSCV Sensing Using a 1st -Order ?SM.IEEE International Midwest Symposium on Circuits and Systems (MWSCAS).
Theeranaew, W., Kim, H., Loparo, K. A., Kim, J. A., & Shaikh, A. A.(2020).Hyperventilation Increases the Randomness of Ocular Palatal Tremor Waveforms.The Cerebellum.
Alvarez-Jimenez, C., Antunes, J., Talasila, N., Bera, K., Brady, J., Gollamudi, J., Marderstein, E., Kalady, M., Purysko, A., Willis, J., Stein, S., Friedman, K., Paspulati, R., Delaney, C., Romero, E., Madabhushi, A., & Viswanath, S.(2020).Radiomic Texture and Shape Descriptors of the Rectal Environment on Post-Chemoradiation T2-Weighted MRI are Associated with Pathologic Tumor Stage Regression in Rectal Cancers: A Retrospective, Multi-Institution Study.Cancers,12(8).
Chen, H., Cheng, F., & Li, J.(2020).iDrug: Integration of drug repositioning and drug-target prediction via cross-network embedding.PLoS Computational Biology,16(7).
Theeranaew, W., Thurtell, M., Loparo, K. A., & Shaikh, A. A.(2020).Gabapentin and memantine increases randomness of oscillatory waveform in ocular palatal tremor.Journal of Computational Neuroscience.
