Computational Methods for Characterizing Cancer Mutational Heterogeneity von Vandin, Fabio

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De novo discovery of mutated driver pathways in cancer von Vandin, Fabio, Upfal, Eli, Raphael, Benjamin J

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Efficient mining of the most significant patterns with permutation testing von Pellegrina, Leonardo, Vandin, Fabio

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Algorithms for detecting significantly mutated pathways in cancer von Vandin, Fabio, Upfal, Eli, Raphael, Benjamin J

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Mutational landscape and significance across 12 major cancer types von Kandoth, Cyriac, McLellan, Michael D., Vandin, Fabio, Ye, Kai, Niu, Beifang, Lu, Charles, Xie, Mingchao, Zhang, Qunyuan, McMichael, Joshua F., Wyczalkowski, Matthew A., Leiserson, Mark D. M., Miller, Christopher A., Welch, John S., Walter, Matthew J., Wendl, Michael C., Ley, Timothy J., Wilson, Richard K., Raphael, Benjamin J., Ding, Li

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Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes von Leiserson, Mark D M, Vandin, Fabio, Wu, Hsin-Ta, Dobson, Jason R, Eldridge, Jonathan V, Thomas, Jacob L, Papoutsaki, Alexandra, Kim, Younhun, Niu, Beifang, McLellan, Michael, Lawrence, Michael S, Gonzalez-Perez, Abel, Tamborero, David, Cheng, Yuwei, Ryslik, Gregory A, Lopez-Bigas, Nuria, Getz, Gad, Ding, Li, Raphael, Benjamin J

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The mutational landscape of lethal castration-resistant prostate cancer von Grasso, Catherine S., Wu, Yi-Mi, Robinson, Dan R., Cao, Xuhong, Dhanasekaran, Saravana M., Khan, Amjad P., Quist, Michael J., Jing, Xiaojun, Lonigro, Robert J., Brenner, J. Chad, Asangani, Irfan A., Ateeq, Bushra, Chun, Sang Y., Siddiqui, Javed, Sam, Lee, Anstett, Matt, Mehra, Rohit, Prensner, John R., Palanisamy, Nallasivam, Ryslik, Gregory A., Vandin, Fabio, Raphael, Benjamin J., Kunju, Lakshmi P., Rhodes, Daniel R., Pienta, Kenneth J., Chinnaiyan, Arul M., Tomlins, Scott A.

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The Impact of Global Structural Information in Graph Neural Networks Applications von Buffelli, Davide, Vandin, Fabio

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Efficient algorithms to discover alterations with complementary functional association in cancer von Sarto Basso, Rebecca, Hochbaum, Dorit S, Vandin, Fabio

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Mining Sequential Patterns with VC-Dimension and Rademacher Complexity von Santoro, Diego, Tonon, Andrea, Vandin, Fabio

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Attention-Based Deep Learning Framework for Human Activity Recognition With User Adaptation von Buffelli, Davide, Vandin, Fabio

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Enriched power of disease-concordant twin-case-only design in detecting interactions in genome-wide association studies von Li, Weilong, Baumbach, Jan, Mohammadnejad, Afsaneh, Brasch-Andersen, Charlotte, Vandin, Fabio, Korbel, Jan O, Tan, Qihua

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CoExpresso: assess the quantitative behavior of protein complexes in human cells von Chalabi, Morteza H, Tsiamis, Vasileios, Käll, Lukas, Vandin, Fabio, Schwämmle, Veit

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Accurate computation of survival statistics in genome-wide studies von Vandin, Fabio, Papoutsaki, Alexandra, Raphael, Benjamin J, Upfal, Eli

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Differentially mutated subnetworks discovery von Hajkarim, Morteza Chalabi, Upfal, Eli, Vandin, Fabio

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Discovering significant evolutionary trajectories in cancer phylogenies von Pellegrina, Leonardo, Vandin, Fabio

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NoMAS: A Computational Approach to Find Mutated Subnetworks Associated With Survival in Genome-Wide Cancer Studies von Altieri, Federico, Hansen, Tommy V, Vandin, Fabio

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Bounding the family-wise error rate in local causal discovery using Rademacher averages von Simionato, Dario, Vandin, Fabio

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Efficient Discovery of Significant Patterns with Few-Shot Resampling von Pellegrina, Leonardo, Vandin, Fabio

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SILVAN: Estimating Betweenness Centralities with Progressive Sampling and Non-uniform Rademacher Bounds von Pellegrina, Leonardo, Vandin, Fabio

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