Search for: causal-modeling
Article Neural Networks ; Volume 143 , 2021 , Pages 66-73 ; 08936080 (ISSN) ; Salehkaleybar, S ; Zhang, K ; Sharif University of Technology
Elsevier Ltd 2021
We propose two nonlinear regression methods, namely, Adversarial Orthogonal Regression (AdOR) for additive noise models and Adversarial Orthogonal Structural Equation Model (AdOSE) for the general case of structural equation models. Both methods try to make the residual of regression independent from regressors, while putting no assumption on noise distribution. In both methods, two adversarial networks are trained simultaneously where a regression network outputs predictions and a loss network that estimates mutual information (in AdOR) and KL-divergence (in AdOSE). These methods can be formulated as a minimax two-player game; at equilibrium, AdOR finds a deterministic map between inputs...
PhD Thesis in Electrical Engineering – Biomedical Engineering:Auditory "Change Detection" Analysis using Integrated Event-Related Potentials and fMRI in Chronic Tinnitus Subjects, Ph.D. Dissertation Sharif University of Technology ; Jahed, Mehran ; Mahmoudian, Saeid
Tinnitus is commonly referred to the symptom of “ringing in the ear”, and it is scientifically described as the perception of sound in the absence of an acoustic event. This symptom is powerful enough to negatively affect sleep patterns and concentration. The symptoms affect more than forty-five million people only in the US and 10 to 20% of the world population. Management and treatment of subjective tinnitus is an ongoing focus of research activities. Ample evidence suggests that the mechanism of tinnitus involves maladaptive plasticity in both classic and non-classic auditory pathway. The non-classical pathway is referred to multi-modal sensory inputs to the auditory system, limbic...
Article Nature Methods ; Volume 13, Issue 4 , 2016 , Pages 310-322 ; 15487091 (ISSN) ; Heiser, L.M ; Cokelaer, T ; Linger, M ; Nesser, N. K ; Carlin, D. E ; Zhang, Y ; Sokolov, A ; Paull, E. O ; Wong, C. K ; Graim, K ; Bivol, A ; Wang, H ; Zhu, F ; Afsari, B ; Danilova, L. V ; Favorov, A. V ; Lee, W. S ; Taylor, D ; Hu, C. W ; Long, B. L ; Noren, D. P ; Bisberg, A. J ; Mills, G. B ; Gray, J. W ; Kellen, M ; Norman, T ; Friend, S ; Qutub, A. A ; Fertig, E. J ; Guan, Y ; Song, M ; Stuart, J. M ; Spellman, P. T ; Koeppl, H ; Stolovitzky, G ; Saez Rodriguez, J ; Mukherjee, S ; Afsari, B ; Al-Ouran, R ; Anton, B ; Arodz, T ; Askari Sichani, O ; Bagheri, N ; Berlow, N ; Bisberg, A. J ; Bivol, A ; Bohler, A ; Bonet, J ; Bonneau, R ; Budak, G ; Bunescu, R ; Caglar, M ; Cai, B ; Cai, C ; Carlin, D. E ; Carlon, A ; Chen, L ; Ciaccio, M. F ; Cokelaer, T ; Cooper, G ; Coort, S ; Creighton, C. J ; Daneshmand, S. M. H ; De La Fuente, A ; Di Camillo, B ; Danilova, L. V ; Dutta-Moscato, J ; Emmett, K ; Evelo, C ; Fassia, M. K. H ; Favorov, A. V ; Fertig, E. J ; Finkle, J. D ; Finotello, F ; Friend, S ; Gao, X ; Gao, J ; Garcia Garcia, J ; Ghosh, S ; Giaretta, A ; Graim, K ; Gray, J. W ; Großeholz, R ; Guan, Y ; Guinney, J ; Hafemeister, C ; Hahn, O ; Haider, S ; Hase, T ; Heiser, L. M ; Hill, S. M ; Hodgson, J ; Hoff, B ; Hsu, C. H ; Hu, C. W ; Hu, Y ; Huang, X ; Jalili, M ; Jiang, X ; Kacprowski, T ; Kaderali, L ; Kang, M ; Kannan, V ; Kellen, M ; Kikuchi, K ; Kim, D. C ; Kitano, H ; Knapp, B ; Komatsoulis, G ; Koeppl, H ; Krämer, A ; Kursa, M. B ; Kutmon, M ; Lee, W. S ; Li, Y ; Liang, X ; Liu, Z ; Liu, Y ; Long, B. L ; Lu, S ; Lu, X ; Manfrini, M ; Matos, M. R. A ; Meerzaman, D ; Mills, G. B ; Min, W ; Mukherjee, S ; Müller, C. L ; Neapolitan, R. E ; Nesser, N. K ; Noren, D. P ; Norman, T ; Oliva, B ; Opiyo, S. O ; Pal, R ; Palinkas, A ; Paull, E. O ; Planas Iglesias, J ; Poglayen, D ; Qutub, A. A ; Saez Rodriguez, J ; Sambo, F ; Sanavia, T ; Sharifi-Zarchi, A ; Slawek, J ; Sokolov, A ; Song, M ; Spellman, P. T ; Streck, A ; Stolovitzky, G ; Strunz, S ; Stuart, J. M ; Taylor, D ; Tegnér, J ; Thobe, K ; Toffolo, G. M ; Trifoglio, E ; Unger, M ; Wan, Q ; Wang, H ; Welch, L ; Wong, C. K ; Wu, J. J ; Xue, A. Y ; Yamanaka, R ; Yan, C ; Zairis, S ; Zengerling, M ; Zenil, H ; Zhang, S ; Zhang, Y ; Zhu, F ; Zi, Z ; Sharif University of Technology
Nature Publishing Group 2016
It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was...