Metric learning pairwise kernel for graph inference

Much recent work in bioinformatics has focused on the inference of various types of biological networks, representing gene regulation, metabolic processes, protein-protein interactions, etc. A common setting involves inferring network edges in a supervised fashion from a set of high-confidence edges, possibly characterized by multiple, heterogeneous data sets (protein sequence, gene expression, etc.). Here, we distinguish between two modes of inference in this setting: direct inference based upon similarities between nodes joined by an edge, and indirect inference based upon similarities between one pair of nodes and another pair of nodes. We propose a supervised approach for the direct case by translating it into a distance metric learning problem. A relaxation of the resulting convex optimization problem leads to the support vector machine (SVM) algorithm with a particular kernel for pairs, which we call the metric learning pairwise kernel (MLPK). We demonstrate, using several real biological networks, that this direct approach often improves upon the state-of-the-art SVM for indirect inference with the tensor product pairwise kernel

Is Drug Coverage a Free Lunch? Cross-Price Elasticities and the Design of Prescription Drug Benefits

Recently, many U.S. employers have adopted less generous prescription drug benefits. In addition, the U.S. began to offer prescription drug insurance to approximately 42 million Medicare beneficiaries in 2006. We use data on individual health insurance claims and benefit data from 1997-2003 to study the effects of changing consumers' co-payments for prescription drugs on the quantity demanded and expenditure on prescription drugs, inpatient care and outpatient care. We allow for effects both in the year of the co-payment change and in the year following the change. Our results show that increases in prescription drug prices reduce both the use of and spending on prescription drugs. However, consumers substitute the use of outpatient care and inpatient care for prescription drug use, and about 35% of the expenditure reductions on prescription drugs are offset by the increases in other spending.

Is Drug Coverage a Free Lunch? Cross-Price Elasticities and the Design of Prescription Drug Benefits

Recently, many US employers have adopted less generous prescription drug benefits. In addition, the U.S. began to offer prescription drug insurance to approximately 42 million Medicare beneficiaries in 2006. We use data on individual health insurance claims and benefit data from 1997-2003 to study the effects of changing consumers’ co-payments for prescription drugs on the quantity demanded and expenditure on prescription drugs, inpatient care and outpatient care. We allow for effects both in the year of the co-payment change and in the year following the change. Our results show that increases in prescription drug prices reduce both the use of and spending on prescription drugs. However, consumers substitute the use of outpatient care and inpatient care for prescription drug use, and the expenditure reductions on prescription drugs are largely offset by the increases in outpatient spending.drugs, elasticity, substitution, cost-sharing, insurance

Role of CCM1 loss-of-function-induced endothelial-to-mesenchymal transition in the development of cavernous malformations

pre-printCerebral cavernous malformations (CCM) occur in two variants: sporadic and familial. Mutations in three genes-CCM1, CCM2, and CCM3-play a role in both subtypes, with mouse models showing the development of multiple cavernous malformations in animals with loss of function in any of these three genes. Identification of these genes has already allowed for improved screening of family members at risk for familial cavernous malformations, but deeper knowledge about the underlying physiology of these lesions could open up new avenues for treatment

“Runx”ing towards Sensory Differentiation

Somatosensory stimuli are encoded by molecularly and anatomically diverse classes of dorsal root ganglia (DRG) neurons. In this issue of Neuron, three papers demonstrate that the Runx transcription factors, Runx1 and Runx3, respectively regulate the molecular identities and spinal terminations of TrkA+ nociceptive neurons and TrkC+ proprioceptive neurons. These findings emphasize the importance of intrinsic genetic programs in generating the diversity of DRG neurons and specifying the circuits into which they incorporate

Opening Books and the National Corpus of Graduate Research

Virginia Tech University Libraries, in collaboration with Virginia Tech Department of Computer Science and Old Dominion University Department of Computer Science, request $505,214 in grant funding for a 3-year project, the goal of which is to bring computational access to book-length documents, demonstrating that with Electronic Theses and Dissertations (ETDs). The project is motivated by the following library and community needs. (1) Despite huge volumes of book-length documents in digital libraries, there is a lack of models offering effective and efficient computational access to these long documents. (2) Nationwide open access services for ETDs generally function at the metadata level. Much important knowledge and scientific data lie hidden in ETDs, and we need better tools to mine the content and facilitate the identification, discovery, and reuse of these important components. (3) A wide range of audiences can potentially benefit from this research, including but not limited to Librarians, Students, Authors, Educators, Researchers, and other interested readers. We will answer the following key research questions: (1) How can we effectively identify and extract key parts (chapters, sections, tables, figures, citations), in both born digital and page image formats? (2) How can we develop effective automatic classication as well as chapter summarization techniques? (3) How can our ETD digital library most effectively serve stakeholders? In response to these questions, we plan to first compile an ETD corpus consisting of at least 50,000 documents from multiple institutional repositories. We will make the corpus inclusive and diverse, covering a range of degrees (master’s and doctoral), years, graduate programs (STEM and non-STEM), and authors (from HBCUs and non-HBCUs). Testing first with this sample, we will investigate three major research areas (RAs), outlined below. RA 1: Document analysis and extraction, in which we experiment with machine/deep learning models for effective ETD segmentation and subsequent information extraction. Anticipated results of this research include new software tools that can be used and adapted by libraries for automatic extraction of structural metadata and document components (chapters, sections, figures, tables, citations, bibliographies) from ETDs - applied to both page image and born digital documents. RA 2: Adding value, in which we investigate techniques and build machine/deep learning models to automatically summarize and classify ETD chapters. Anticipated results of this research include software implementations of a chapter-level text summarizer that generates paragraph-length summaries of ETD chapters, and a multi-label classifier that assigns subject categories to ETD chapters. Our aim is to develop software that can be adapted or replicated by libraries to add value to their existing ETD services. RA 3: User services, in which we study users to identify and understand their information needs and information seeking behaviors, so that we may establish corresponding requirements for user interface and service components most useful for interacting with ETD content. Basing our design decisions on empirical evidence obtained from user analysis, we will construct a prototype system to demonstrate how these components can improve the user experience with ETD collections, and ultimately increase the capacity of libraries to provide access to ETDs and other long-form document content. Our project brings to bear cutting-edge computer science and machine/deep learning technologies to advance discovery, use, and potential for reuse of the knowledge hidden in the text of books and book-length documents. In addition, by focusing on libraries\u27 ETD collections (where legal restrictions from book publishers generally are not applicable), our research will open this rich corpus of graduate research and scholarship, leverage ETDs to advance further research and education, and allow libraries to achieve greater impact

Maximizing Equitable Reach and Accessibility of ETDs

This poster addresses accessibility issues of electronic theses and dissertations (ETDs) in digital libraries (DLs). ETDs are available primarily as PDF files, which present barriers to equitable access, especially for users with visual impairments, cognitive or learning disabilities, or for anyone needing more efficient and effective ways of finding relevant information within these long documents. We propose using AI techniques, including natural language processing (NLP), computer vision, and text analysis, to convert PDFs into machine-readable HTML documents with semantic tags and structure, extracting figures and tables, and generating summaries and keywords. Our goal is to increase the accessibility of ETDs and to make this important scholarship available to a wider audience