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Cancer genomes exhibit a large number of different alterations that affect many genes in a diverse manner. It is widely believed that these alterations follow combinatorial patterns that have a strong connection with the underlying molecular interaction networks and functional pathways. A better understanding of the generative mechanisms behind the mutation rules and their influence on gene communities is of great importance for the process of driver mutations discovery and for identification of network modules related to cancer development and progression. We developed a new method for cancer mutation pattern analysis based on a constrained form of correlation clustering. Correlation clustering is an agnostic learning method that can be used for general community detection problems in which the number of communities or their structure is not known beforehand. The resulting algorithm, named $C^3$, leverages mutual exclusivity of mutations, patient coverage, and driver network concentration principles; it accepts as its input a user determined combination of heterogeneous patient data, such as that available from TCGA (including mutation, copy number, and gene expression information), and creates a large number of clusters containing mutually exclusive mutated genes in a particular type of cancer. The cluster sizes may be required to obey some useful soft size constraints, without impacting the computational complexity of the algorithm. To test $C^3$, we performed a detailed analysis on TCGA breast cancer and glioblastoma data and showed that our algorithm outperforms the state-of-the-art CoMEt method in terms of discovering mutually exclusive gene modules and identifying driver genes. Our $C^3$ method represents a unique tool for efficient and reliable identification of mutation patterns and driver pathways in large-scale cancer genomics studies., Comment: 22 pages, 4 figures

Based on the comparison of a regular-sized (n=25) and an oversized (n=54) class of college students taught by the same instructor, significant superior performance was found in favor of the large class. This disparity persisted even when a switching regression model (with endogenous switching) was performed. Only 39% of the learning disparity can be explained by characteristics of the student, including a 29.5% performance gap that is attributed to the potential "overachiever" trait of small class students. In addition, it was found that overall grade point average and performances in previous college economics courses did not have any significant impact on the students' current scores. Contrary to most of the findings in recent literature, high school economic education did not have any positive impact on the students' performance. In fact, it had a slightly negative effect. No evidence of gender difference was found for the large class, but a fairly significant lower performance was found for females in the small class. The student questionnaire is attached. Three tables are included. (Contains 28 references.) (Author/SLD)