Mehmet Ahsen

Articles

• Han, W., Wang, X., Ahsen, M., & Wattal, S. (2021). The Societal Impact of Sharing Economy Platform Self-Regulations? An Empirical Investigation. Information Systems Research, INFORMS.
• Craig, A., Garcia-Lezana, T., Ruiz de Galarreta, Marina, ., Villacorta-Martin, C., Kozlova, E., Martins-Filho, S., von Felden, J., Ahsen, M., Bresnahan, E., Hernandez-Meza, G., & others, . (2021). Transcriptomic characterization of cancer-testis antigens identifies MAGEA3 as a driver of tumor progression in hepatocellular carcinoma. PLoS genetics, Public Library of Science San Francisco, CA USA, 17 (6), e1009589.
• Creason, A., Haan, D., Dang, K., Chiotti, K., Inkman, M., Lamb, A., Yu, T., Hu, Y., Norman, T., Buchanan, A., Ahsen, M., & others, . (2021). A community challenge to evaluate RNA-seq, fusion detection, and isoform quantification methods for cancer discovery. Cell systems, Cell Press, 12 (8), 827--838.
• Kim, S., Arun, A., Ahsen, M., Vogel, R., & Stolovitzky, G. (2021). The Fermi--Dirac distribution provides a calibrated probabilistic output for binary classifiers. Proceedings of the National Academy of Sciences, National Academy of Sciences, 118 (34).
• Manica, M., Bunne, C., Mathis, R., Cadow, J., Ahsen, M., Stolovitzky, G., & Mart\'\inez, Mar\'\ia Rodr\'\iguez, . (2021). COSIFER: a Python package for the consensus inference of molecular interaction networks. Bioinformatics, Oxford University Press, 37 (14), 2070--2072.
• Roy, S., Kiral, I., Mirmomeni, M., Mummert, T., Braz, A., Tsay, J., Tang, J., Asif, U., Schaffter, T., Ahsen, M., & others, . (2021). Evaluation of artificial intelligence systems for assisting neurologists with fast and accurate annotations of scalp electroencephalography data. EBioMedicine, Elsevier, 66 103275.
• Von Felden, J., Garcia-Lezana, T., Dogra, N., Gonzalez-Kozlova, E., Ahsen, M., Craig, A., Gifford, S., Wunsch, B., Smith, J., Kim, S., & others, . (2021). Unannotated small RNA clusters associated with circulating extracellular vesicles detect early stage liver cancer. Gut, BMJ Publishing Group.
• Zhang, Z., Genc, Y., Wang, D., Ahsen, M., & Fan, X. (2021). Effect of ai explanations on human perceptions of patient-facing ai-powered healthcare systems. Journal of Medical Systems, Springer US, 45 (6), 1--10.
• Schaffter, T., Buist, D., Lee, C., Nikulin, Y., Ribli, Dezs\Ho, ., Guan, Y., Lotter, W., Jie, Z., Du, H., Wang, S., Ahsen, M., & others, . (2020). Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms. JAMA network open, American Medical Association, 3 (3), e200265--e200265.
• Ahsen, M., Vogel, R., & Stolovitzky, G. (2020). R/PY-SUMMA: An R/Python Package for Unsupervised Ensemble Learning for Binary Classification Problems in Bioinformatics. Journal of Computational Biology, Mary Ann Liebert, Inc., publishers 140 Huguenot Street, 3rd Floor New~….
• Diaz, J., Ahsen, M., Schaffter, T., Chen, X., Realubit, R., Karan, C., Califano, A., Losic, B., & Stolovitzky, G. (2020). The transcriptomic response of cells to a drug combination is more than the sum of the responses to the monotherapies. Elife, eLife Sciences Publications Limited, 9 e52707.
• Kerns, S., Fachal, L., Dorling, L., Barnett, G., Baran, A., Peterson, D., Hollenberg, M., Hao, K., Narzo, A., Ahsen, M., & others, . (2020). Radiogenomics Consortium Genome-Wide Association Study Meta-analysis of Late Toxicity after Prostate Cancer Radiotherapy JNCI: Journal of the National Cancer Institute.
• Losic, B., Craig, A., Villacorta-Martin, C., Martins-Filho, S., Akers, N., Chen, X., Ahsen, M., von Felden, J., Labgaa, I., D'Avola, D., & others, . (2020). Intratumoral heterogeneity and clonal evolution in liver cancer. Nature communications, Nature Publishing Group, 11 (1), 1--15.
• Tanevski, J., Nguyen, T., Truong, B., Karaiskos, N., Ahsen, M., Zhang, X., Shu, C., Xu, K., Liang, X., Hu, Y., & others, . (2020). Gene selection for optimal prediction of cell position in tissues from single-cell transcriptomics data. Life science alliance, Life Science Alliance, 3 (11).
• Ahsen, M., Ayvaci, M., & Raghunathan, S. (2019). When algorithmic predictions use human-generated data: A bias-aware classification algorithm for breast cancer diagnosis. Information Systems Research, INFORMS, 30 (1), 97--116.
• Ahsen, M., & Vidyasagar, M. (2019). An approach to one-bit compressed sensing based on probably approximately correct learning theory. The Journal of Machine Learning Research, JMLR. org, 20 (1), 408--430.
• Ahsen, M., Chu, Y., Grishin, A., Grishina, G., Stolovitzky, G., Pandey, G., & Bunyanovich, S. (2019). NeTFactor, a framework for identifying transcriptional regulators of gene expression-based biomarkers. Nature Scientific Reports.
• Ahsen, M., Vogel, R., & Stolovitzky, G. (2019). Unsupervised Evaluation and Weighted Aggregation of Ranked Classification Predictions Journal of Machine Learning Research, 20 (166), 1--40.
• Choobdar, S., Ahsen, M., Crawford, J., Tomasoni, M., Fang, T., Lamparter, D., Lin, J., Hescott, B., Hu, X., Mercer, J., & others, . (2019). Assessment of network module identification across complex diseases. Nature Methods, Nature Publishing Group, 16 (9), 843--852.
• Davis, S., Button-Simons, K., Bensellak, T., Ahsen, M., Checkley, L., Foster, G., Su, X., Moussa, A., Mapiye, D., Khoo, S., & others, . (2019). Leveraging crowdsourcing to accelerate global health solutions. Nature biotechnology, Nature Publishing Group, 37 (8), 848--850.
• Menden, M., Wang, D., Mason, M., Szalai, B., Bulusu, K., Guan, Y., Yu, T., Kang, J., Jeon, M., Wolfinger, R., Ahsen, M., & others, . (2019). Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. Nature communications, Nature Publishing Group, 10 (1), 2674.
• Murillo, O., Thistlethwaite, W., Rozowsky, J., Subramanian, S., Lucero, R., Shah, N., Jackson, A., Srinivasan, S., Chung, A., Laurent, C., Ahsen, M., & others, . (2019). exRNA Atlas Analysis Reveals Distinct Extracellular RNA Cargo Types and Their Carriers Present across Human Biofluids. Cell, Cell Press, 177 (2), 463--477.
• Smith, J., Wunsch, B., Dogra, N., Ahsen, M., Lee, K., Yadav, K., Weil, R., Pereira, M., Patel, J., Duch, E., & others, . (2018). Integrated nanoscale deterministic lateral displacement arrays for separation of extracellular vesicles from clinically-relevant volumes of biological samples. Lab on a Chip, Royal Society of Chemistry, 18 (24), 3913--3925.
• Fourati, S., Talla, A., Mahmoudian, M., Burkhart, J., Klen, R., Henao, R., Yu, T., Ayd\in, Zafer, ., Yeung, K., Ahsen, M., & others, . (2018). A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection. Nature communications, Nature Publishing Group, 9 (1), 4418.
• Singh, N., Ahsen, M., Challapalli, N., Kim, H., White, M., & Vidyasagar, M. (2018). Inferring genome-wide interaction networks using the phi-mixing coefficient, and applications to lung and breast cancer. IEEE Transactions on Molecular, Biological and Multi-Scale Communications, IEEE, 4 (3), 123--139.
• Pandey, G., Pandey, O., Rogers, A., Ahsen, M., Hoffman, G., Raby, B., Weiss, S., Schadt, E., & Bunyavanich, S. (2018). A nasal brush-based classifier of asthma identified by machine learning analysis of nasal RNA sequence data. Scientific reports, Nature Publishing Group, 8 (1), 8826.
• Ahsen, M., & Vidyasagar, M. (2017). Error bounds for compressed sensing algorithms with group sparsity: A unified approach. Applied and Computational Harmonic Analysis, Academic Press, 43 (2), 212--232.
• Ahsen, M., Boren, T., Singh, N., Misganaw, B., Mutch, D., Moore, K., Backes, F., McCourt, C., Lea, J., Miller, D., & others, . (2017). Sparse feature selection for classification and prediction of metastasis in endometrial cancer. BMC genomics, BioMed Central, 18 (3), 233.
• Ahsen, M., Challapalli, N., & Vidyasagar, M. (2017). Two new approaches to compressed sensingexhibiting both robust sparse recovery and the grouping effect. The Journal of Machine Learning Research, JMLR. org, 18 (1), 1745--1768.
• Ayvaci, M., Ahsen, M., Raghunathan, S., & Gharibi, Z. (2017). Timing the Use of Breast Cancer Risk Information in Biopsy Decision-Making. Production and Operations Management, 26 (7), 1333--1358.
• Ahsen, M., \"Ozbay, Hitay, ., & Niculescu, S. (2016). Analysis of a gene regulatory network model with time delay using the secant condition. IEEE life sciences letters, IEEE, 2 (2), 5--8.
• Ahsen, M., \"Ozbay, Hitay, ., & Niculescu, S. (2015). A secant condition for cyclic systems with time delays and its application to gene regulatory networks. IFAC-PapersOnLine, Elsevier, 48 (12), 171--176.
• Misganaw, B., Ahsen, M., Singh, N., Baggerly, K., Unruh, A., White, M., & Vidyasagar, M. (2015). Optimized Prediction of Extreme Treatment Outcomes in Ovarian Cancer. Cancer Informatics, 14 45--55.
• Ahsen, M., \"Ozbay, H, ., & Niculescu, S. (2014). On the analysis of a dynamical model representing gene regulatory networks under negative feedback. International Journal of Robust and Nonlinear Control, 24 (11), 1609--1627.
• Ahsen, M., & Vidyasagar, M. (2013). Mixing coefficients between discrete and real random variables: Computation and properties. IEEE Transactions on Automatic Control, IEEE, 59 (1), 34--47.

Book Chapters

• Ahsen, M., \"Ozbay, Hitay, ., & Niculescu, S. (2017). Stability and Robustness Analysis of a Class of Cyclic Biological Systems. Time Delay Systems ( pp. 155--168). Springer, Cham.
• Ahsen, M., \"Ozbay, Hitay, ., & Niculescu, S. (2015). Basic Tools from Systems and Control Theory. Analysis of Deterministic Cyclic Gene Regulatory Network Models with Delays ( pp. 13--23). Birkh\"auser, Cham.
• Ahsen, M., \"Ozbay, Hitay, ., & Niculescu, S. (2015). Deterministic ODE-Based Model with Time Delay. Analysis of Deterministic Cyclic Gene Regulatory Network Models with Delays ( pp. 43--51). Birkh\"auser, Cham.
• Ahsen, M., \"Ozbay, Hitay, ., & Niculescu, S. (2015). Functions with Negative Schwarzian Derivatives. Analysis of Deterministic Cyclic Gene Regulatory Network Models with Delays ( pp. 25--42). Birkh\"auser, Cham.
• Ahsen, M., \"Ozbay, Hitay, ., & Niculescu, S. (2015). Gene Regulatory Networks Under Positive Feedback. Analysis of Deterministic Cyclic Gene Regulatory Network Models with Delays ( pp. 73--85). Birkh\"auser, Cham.

Books and Monographs

• Ahsen, M., \"Ozbay, Hitay, ., & Niculescu, S. (2015). Analysis of deterministic cyclic gene regulatory network models with delays. Springer.

Conference Proceedings

• Ahsen, M., Ayvaci, M., Mookerjee, R., & Stolovitzky, G. Forthcoming. When Machines Will Take Over? Algorithms for Human-Machine Collaborative Decision Making in Healthcare. HICSS.
• Wu, A., Garimella, A., Subramanyam, R., & Ahsen, M. (2022). Disaster Management Through Digital Platforms: Online Crowdfunding Communities Respond to the COVID-19 Pandemic. HICSS.
• Ahsen, M., Chun, Y., Grishin, A., Grishina, G., Stolovitzky, G., Pandey, G., & Bunyavanich, S. (2020). NeTFactor, a framework for identifying transcriptional regulators of gene expression-based biomarkers. ( pp. 1--13). Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics.
• Basu, S., Subramanyam, R., & Ahsen, M. (2019). Is a megapixel worth a few thousand words? An Empirical Assessment of Image Sentiments on Philanthropic Crowdfunding Success. Conference on the Digital Economy (CODE) 2019.
• Lin, Y., Ahsen, M., Shaw, M., & Seshadri, S. (2019). The Impacts of Patients' Sentiment Trajectory Features on Their Willingness to Share in Online Support Groups ICIS ICIS.
• von Felden, J., Craig, A., Ahsen, M., Labgaa, I., D'Avola, D., Meza, G., Allette, K., Dogra, N., Lezana, T., Tabrizian, P., & others, . (2019). RNA-sequencing of plasma exosomes reveals specific transcriptomic profiles in patients with hepatocellular carcinoma. American Association for Cancer Research.
• Losic, B., Craig, A., Martins-Filho, S., Villacorta-Martin, C., Akers, N., Chen, X., Ahsen, M., Labgaa, I., D'Avola, D., Lira, S., & others, . (2018). Deciphering the impact of immune editing on liver cancer clonal evolution using immunogenomics. AACR.
• Craig, A., Villacorta-Martin, C., Labgaa, I., Ahsen, M., Martins-Filho, S., D'AVOLA, D., Stueck, A., Ward, S., Fiel, M., Gunasekaran, G., & others, . (2017). A potential role of cancer testis antigens in hepatocellular carcinoma progression. Hepatology.
• Challapalli, N., Ahsen, M., & Vidyasagar, M. (2016). Modelling drug response and resistance in cancer: Opportunities and challenges. 2016 IEEE 55th Conference on Decision and Control (CDC) ( pp. 2488--2493).
• Craig, A., Ahsen, M., Villacorta-Martin, C., Chen, X., Labgaa, I., Stueck, A., D'Avola, D., Ward, S., Fiel, M., Gunasekaran, G., & others, . (2016). Multi-regional integrative genomic analysis reveals intra-tumor heterogeneity in a subset of hepatocellular carcinoma. Hepatology ( vol. 64, pp. 267A--268A).
• Torlak, F., Ayvaci, M., Ahsen, M., Arce, C., Vazquez, M., & Tanriover, B. (2016). Estimating waiting time for deceased donor renal transplantion in the era of new kidney allocation system. Transplantation proceedings ( 6 ed vol. 48, pp. 1916--1919).
• Ahsen, M., & Vidyasagar, M. (2015). A PAC learning approach to one-bit compressed sensing. 2015 American Control Conference (ACC) ( pp. 4228--4230).
• Ahsen, M., & Vidyasagar, M. (2015). An approach to one-bit compressed sensing based on probably approximately correct learning theory. 2015 54th IEEE Conference on Decision and Control (CDC) ( pp. 7377--7379).
• Ahsen, M., & Vidyasagar, M. (2014). Near-ideal behavior of compressed sensing algorithms. 53rd IEEE Conference on Decision and Control ( pp. 6354--6357).
• Bulut, E., Ahsen, M., & Szymanski, B. (2014). Opportunistic wireless charging for mobile social and sensor networks. 2014 IEEE Globecom Workshops (GC Wkshps) ( pp. 207--212).
• Ahsen, M., & Vidyasagar, M. (2013). On the computation of mixing coefficients between discrete-valued random variables. 2013 9th Asian Control Conference (ASCC) ( pp. 1--5).
• Ahsen, M., \"Ozbay, Hitay, ., & Niculescu, S. (2012). Stability analysis of a dynamical model representing gene regulatory networks. ( PART 1 ed vol. 10, pp. 191--196).
• Ahsen, M., Singh, N., Boren, T., Vidyasagar, M., & White, M. (2012). A new feature selection algorithm for two-class classification problems and application to endometrial cancer. 2012 IEEE 51st IEEE conference on decision and control (CDC) ( pp. 2976--2982).
• Singh, N., Ahsen, M., Mankala, S., Vidyasagar, M., & White, M. (2012). A novel application of mixing coefficients for reverse-engineering gene interaction networks. 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton) ( pp. 1461--1466).
• Singh, N., Ahsen, M., Mankala, S., Vidyasagar, M., & White, M. (2012). Inferring weighted and directed gene interaction networks from gene expression data using the phi-mixing coefficient. Proceedings 2012 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS) ( pp. 168--171).

Presentations

• Wu, A., Garimella, A., Subramanyam, R., & Ahsen, M. (2022). Disaster Management Through Digital Platforms : Online Crowdfunding Communities Respond to the COVID-19 Pandemic. Hawaii International Conference on System Sciences (HICSS.

Working Papers

• Han, W., Ahsen, M., Lin, Y., & Zhu, Y. Drivers of the cryptocurrency market - insights from analyzing tweets.
• Ahsen, M., & Garimella, A. Can Explainable AI Aid Fake News Detection?: A Series of Randomized Controlled Experiments.
• Ahsen, M., , M., Raghunathan, S., & Subramanyam, R. Contests for Predictive Algorithms: Ensembling,Interdependency, and Optimal Rewards Design.
• Ahsen, M., Ayvaci, M., & Mookerjee, R. Will Machines Take Over? Algorithms for Human-Machine Collaborative Decision Making in Healthcare.
• Basu, S., Subramanyam, R., & Ahsen, M. Is a megapixel worth a few thousand words? An Empirical Assessment of Image Sentiments on Philanthropic Crowdfunding Success.
• Lin, Y., Ahsen, M., Shaw, M., & Seshadri, S. The impacts of patients’ sentiment trajectory features on their willingness to share in online support groups.
• Wu, A., Garimella, A., Subramanyam, R., & Ahsen, M. Keeping Kids Learning: Online Crowdfunding Communities Respond to the COVID-19 Pandemic.  link >
• Wu, A., Subramanyam, R., & Ahsen, M. Evidence of Green Washing through Social Media.
• Hao, S., Xu, Y., Mukherjee, U., Seshadri, S., Ahsen, M., Bose, S., Ivanov, A., Souyris, S., & Sridhar, P. Hotspots for Emerging Epidemics: Multi-Task and Transfer Learning over Mobility Networks.