Clustered Multi-Task Learning: A Convex Formulation

In multi-task learning several related tasks are considered simultaneously, with the hope that by an appropriate sharing of information across tasks, each task may benefit from the others. In the context of learning linear functions for supervised classification or regression, this can be achieved by including a priori information about the weight vectors associated with the tasks, and how they are expected to be related to each other. In this paper, we assume that tasks are clustered into groups, which are unknown beforehand, and that tasks within a group have similar weight vectors. We design a new spectral norm that encodes this a priori assumption, without the prior knowledge of the partition of tasks into groups, resulting in a new convex optimization formulation for multi-task learning. We show in simulations on synthetic examples and on the IEDB MHC-I binding dataset, that our approach outperforms well-known convex methods for multi-task learning, as well as related non convex methods dedicated to the same problem

A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization

We present a general approach for collaborative filtering (CF) using spectral regularization to learn linear operators from "users" to the "objects" they rate. Recent low-rank type matrix completion approaches to CF are shown to be special cases. However, unlike existing regularization based CF methods, our approach can be used to also incorporate information such as attributes of the users or the objects -- a limitation of existing regularization based CF methods. We then provide novel representer theorems that we use to develop new estimation methods. We provide learning algorithms based on low-rank decompositions, and test them on a standard CF dataset. The experiments indicate the advantages of generalizing the existing regularization based CF methods to incorporate related information about users and objects. Finally, we show that certain multi-task learning methods can be also seen as special cases of our proposed approach

Smarcb1-Mutant Intracranial Meningiomas: A Distinct Subtype Of Nf2-Mutant Tumors

Meningiomas are the most common central nervous system primary tumors. Mutations in the tumor suppressor gene Neurofibromin 2 (NF2), located on chromosome 22 (Chr22), are present in 40-50% of sporadic meningiomas. In a subset of non-NF2-mutant meningiomas, recent studies have identified tumorigenesis driver mutations in the genes TRAF7, AKT1, KLF4, and SMO. However, the genetic basis for the marked clinical and histological heterogeneity that exists among NF2-mutant/Chr22-loss meningiomas remains to be established. In this study, we utilized next-generation sequencing techniques to identify and screen a large cohort of NF2-mutant/Chr22-loss meningiomas for concurrent co-driver mutations in novel genes that may contribute to the observed clinical and histological heterogeneity of these tumors. We identified 25 NF2-mutant/Chr22-loss meningiomas that harbored concurrent somatic mutations in the SMARCB1 gene. SMARCB1 codes for a component of the SWI/SNF complex and is involved in epigenetic modification via nucleosome modulation and chromatin remodeling. SMARCB1 is also known to interact with GLI1, an important effector of the Hedgehog pathway, and EZH2, a member of the Polycomb-group proteins that is capable of epigenetically silencing gene expression via histone methylation. SMARCB1 somatic mutations have previously been associated with the highly aggressive malignant rhabdoid tumors and germline variants have previously been identified as the driver mutation responsible for familial schwannomatosis. Notably, a subset of patients with familial schwannomatosis develop multiple meningiomas as part of their disease course. We performed RNA expression analysis to determine gene expression differences in SMARCB1-mutant meningiomas versus non-SMARCB1-mutant meningiomas. Unsupervised hierarchical clustering demonstrated that the expression profiles of SMARCB1-mutant meningiomas cluster similarly to other NF2-mutant/Chr22 loss meningiomas. However, within this superfamily, SMARCB1-mutant meningiomas form distinct subgroups. This differential clustering is due, at least in part, to increased GLI1 and EZH2 expression suggesting that SMARCB1-mutant tumors represent a distinct genetic subtype of NF2-mutant meningiomas. Genomic-clinical correlates for SMARCB1-mutant tumors showed no significant differences in this cohort in age at time of surgery (median = 64 years), gender predilection (80% female), or low-grade histologic subtype when compared to the natural history literature. However, the SMARCB1-mutant meningiomas were predominantly midline tumors (71%) with increased propensity to being high- grade lesions (36%), despite remarkable chromosomal stability. These clinical findings strongly paralleled our genetic data suggesting that increased expression of GLI and EZH2 may, respectively, contribute to the midline tumor location and serve as a substitute for widespread chromosomal instability in high-grade lesions. These results demonstrate that SMARCB1-mutant meningiomas represent a genetically and phenotypically distinct sub-group of NF2-mutant meningiomas and partially contribute to the observed clinical heterogeneity of convexity lesions. This study also suggests potential targets for therapeutic interventions that warrant future investigation

Media consumption and creation in attitudes toward and knowledge of inflammatory bowel disease: web-based survey

BACKGROUND: Inflammatory bowel disease (IBD) is a chronic gastrointestinal condition affecting over 5 million people globally and 1.6 million in the United States but currently lacks a precisely determined cause or cure. The range of symptoms IBD patients experience are often debilitating, and the societal stigmas associated with some such symptoms can further degrade their quality of life. Better understanding the nature of this public reproach then is a critical component for improving awareness campaigns and, ultimately, the experiences of IBD patients. OBJECTIVE: The objective of this study was to explore and assess the public's awareness and knowledge of IBD, as well as what relationship, if any, exists between the social stigma surrounding IBD, knowledge of the disease, and various media usage, including social media. METHODS: Utilizing a Web-based opt-in platform, we surveyed a nationally representative sample (n=1200) with demographics mirroring those of the US Census figures across baseline parameters. Using constructed indices based on factor analysis, we were able to build reliable measures of personal characteristics, media behaviors, and perceptions and knowledge of IBD. RESULTS: Among the American public, IBD is the most stigmatized of seven diseases, including genital herpes and human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS). Additionally, IBD knowledge is generally low with 11.08% (133/1200) of the sample indicating no familiarity with the disease and 85.50% (1026/1200) of participants inaccurately answering two-thirds of the IBD index questions with which their knowledge was assessed. Increased knowledge of IBD is associated with lower levels of stigma. However, social media use is currently related to lower levels of IBD knowledge (P<.05). Furthermore, findings indicate that participants who most frequently engaged in producing social media content are less knowledgeable about IBD (P<.10), highlighting the potential for a dangerous cycle should they be contributing to a Web-based IBD dialogue. CONCLUSIONS: Greater efforts must be taken to stymie IBD misinformation across all media, but especially in social media channels, to increase IBD knowledge and reduce stigma surrounding IBD. These findings pave the way for further research qualitatively examining the pervasiveness of specific IBD messages found in today's social media landscape and their impact on enacted stigmas so as to better equip providers and patient advocacy organizations with impactful communication solutions