Digital Commons Network^™

Transcriptomic Profiles Of Peripheral White Blood Cells In Type Ii Diabetes And Racial Differences In Expression Profiles, Jinghe Mao, Junmei Ai, Xinchun Zhou, Ming Shenwu, Manuel Ong Jr., Marketta Blue, Jasmine T. Washington, Xiaonan Wang, Youping Deng

Faculty Publications

Background: Along with obesity, physical inactivity, and family history of metabolic disorders, African American ethnicity is a risk factor for type 2 diabetes (T2D) in the United States. However, little is known about the differences in gene expression and transcriptomic profiles of blood in T2D between African Americans (AA) and Caucasians (CAU), and microarray analysis of peripheral white blood cells (WBCs) from these two ethnic groups will facilitate our understanding of the underlying molecular mechanism in T2D and identify genetic biomarkers responsible for the disparities.

Results: A whole human genome oligomicroarray of peripheral WBCs was performed on 144 …

Modeling Protein Expression And Protein Signaling Pathways, Donatello Telesca, Peter Muller, Steven Kornblau, Marc Suchard, Yuan Ji

COBRA Preprint Series

High-throughput functional proteomic technologies provide a way to quantify the expression of proteins of interest. Statistical inference centers on identifying the activation state of proteins and their patterns of molecular interaction formalized as dependence structure. Inference on dependence structure is particularly important when proteins are selected because they are part of a common molecular pathway. In that case inference on dependence structure reveals properties of the underlying pathway. We propose a probability model that represents molecular interactions at the level of hidden binary latent variables that can be interpreted as indicators for active versus inactive states of the proteins. The …

A Study Of Correlations Between The Definition And Application Of The Gene Ontology, Yuji Mo

Computer and Electronics Engineering: Dissertations, Theses, and Student Research

When using the Gene Ontology (GO), nucleotide and amino acid sequences are annotated by terms in a structured and controlled vocabulary organized into relational graphs. The usage of the vocabulary (GO terms) in the annotation of these sequences may diverge from the relations defined in the ontology. We measure the consistency of the use of GO terms by comparing GO's defined structure to the terms' application. To do this, we first use synthetic data with different characteristics to understand how these characteristics influence the correlation values determined by various similarity measures. Using these results as a baseline, we found that …

Overview Of Contrast Data Mining As A Field And Preview Of An Upcoming Book, Guozhu Dong, James Bailey

Kno.e.sis Publications

This report provides an overview of the field of contrast data mining and its applications, and offers a preview of an upcoming book on the topic. The importance of contrasting is discussed and a brief survey is given covering the following topics: general definitions and terminology for contrast patterns, representative contrast pattern mining algorithms, applications of contrast mining for fundamental data mining tasks such as classification and clustering, applications of contrast mining in bioinformatics, medicine, blog analysis, image analysis and subgroup mining, results on contrast based dataset similarity measure, and on analyzing item interaction in contrast patterns, and open research …

Computing Inconsistency Measure Based On Paraconsistent Semantics, Pascal Hitzler, Yue Ma, Guilin Qi

Computer Science and Engineering Faculty Publications

Measuring inconsistency in knowledge bases has been recognized as an important problem in several research areas. Many methods have been proposed to solve this problem and a main class of them is based on some kind of paraconsistent semantics. However, existing methods suffer from two limitations: (i) they are mostly restricted to propositional knowledge bases; (ii) very few of them discuss computational aspects of computing inconsistency measures. In this article, we try to solve these two limitations by exploring algorithms for computing an inconsistency measure of first-order knowledge bases. After introducing a four-valued semantics for first-order logic, we define an …

A Comparison Of Intensive Care Unit Mortality Prediction Models Through The Use Of Data Mining Techniques, Sujin Kim, Woojae Kim, Rae Woong Park

Institute for Biomedical Informatics Faculty Publications

OBJECTIVES: The intensive care environment generates a wealth of critical care data suited to developing a well-calibrated prediction tool. This study was done to develop an intensive care unit (ICU) mortality prediction model built on University of Kentucky Hospital (UKH)'s data and to assess whether the performance of various data mining techniques, such as the artificial neural network (ANN), support vector machine (SVM) and decision trees (DT), outperform the conventional logistic regression (LR) statistical model.

METHODS: The models were built on ICU data collected regarding 38,474 admissions to the UKH between January 1998 and September 2007. The first 24 hours …

Planning Combinatorial Disulfide Cross-Links For Protein Fold Determination, Fei Xiong, Alan M Friedman, Chris Bailey-Kellogg

Dartmouth Scholarship

Fold recognition techniques take advantage of the limited number of overall structural organizations, and have become increasingly effective at identifying the fold of a given target sequence. However, in the absence of sufficient sequence identity, it remains difficult for fold recognition methods to always select the correct model. While a native-like model is often among a pool of highly ranked models, it is not necessarily the highest-ranked one, and the model rankings depend sensitively on the scoring function used. Structure elucidation methods can then be employed to decide among the models based on relatively rapid biochemical/biophysical experiments.

Assessing The Impact Of Non-Differential Genotyping Errors On Rare Variant Tests Of Association, Scott Powers, Shyam Gopalakrishnan, Nathan L. Tintle

Faculty Work Comprehensive List

Background/Aims: We aim to quantify the effect of non-differential genotyping errors on the power of rare variant tests and identify those situations when genotyping errors are most harmful. Methods: We simulated genotype and phenotype data for a range of sample sizes, minor allele frequencies, disease relative risks and numbers of rare variants. Genotype errors were then simulated using five different error models covering a wide range of error rates. Results: Even at very low error rates, misclassifying a common homozygote as a heterozygote translates into a substantial loss of power, a result that is exacerbated even further as the minor …

The Knowledge-Driven Exploration Of Integrated Biomedical Knowledge Sources Facilitates The Generation Of New Hypotheses, Vinh Nguyen, Olivier Bodenreider, Todd Minning, Amit P. Sheth

Kno.e.sis Publications

Knowledge gained from the scientific literature can complement newly obtained experimental data in helping researchers understand the pathological processes underlying diseases. However, unless the scientific literature and experimental data are semantically integrated, it is generally difficult for scientists to exploit the two sources effectively. We argue that, in addition to the semantic integration of heterogeneous knowledge sources, the usability of the integrated resource by scientists is dependent upon the availability of knowledge visualization and exploration tools. Moreover, the integration techniques must be scalable and the exploration interfaces must be easy to use by bench scientists. The end goal of such …

Demonstration: Real-Time Semantic Analysis Of Sensor Streams, Harshal Patni, Cory Andrew Henson, Michael Cooney, Amit P. Sheth, Krishnaprasad Thirunarayan

Kno.e.sis Publications

The emergence of dynamic information sources – including sensor networks – has led to large streams of real-time data on the Web. Research studies suggest, these dynamic networks have created more data in the last three years than in the entire history of civilization, and this trend will only increase in the coming years [1]. With this coming data explosion, real-time analytics software must either adapt or die [2]. This paper focuses on the task of integrating and analyzing multiple heterogeneous streams of sensor data with the goal of creating meaningful abstractions, or features. These features are then temporally aggregated …

Demonstration: Secure - Semantics Empowered Rescue Environment, Pratikkumar Desai, Cory Andrew Henson, Pramod Anantharam, Amit P. Sheth

Kno.e.sis Publications

This paper demonstrates a Semantic Web enabled system for collecting and processing sensor data within a rescue environment. The real-time system collects heterogeneous raw sensor data from rescue robots through a wireless sensor network. The raw sensor data is converted to RDF using the Semantic Sensor Network (SSN) ontology and further processed to generate abstractions used for event detection in emergency scenarios.

Sempush: Privacy-Aware And Scalable Broadcasting For Semantic Microblogging, Pavan Kapanipathi, Julia Anaya, Alexandre Passant

Kno.e.sis Publications

Users of traditional microblogging platforms such as Twitter face drawbacks in terms of (1) Privacy of status updates as a followee - reaching undesired people (2) Information overload as a follower - receiving uninteresting microposts from followees. In this paper we demonstrate distributed and user-controlled dissemination of microposts using SMOB (semantic microblogging framework) and Semantic Hub (privacy-aware implementation of PuSH3 protocol) . The approach leverages users' Social Graph to dynamically create group of followers who are eligible to receive micropost. The restrictions to create the groups are provided by the followee based on the hastags in the micropost. Both SMOB …

A Domain Specific Language For Enterprise Grade Cloud-Mobile Hybrid Applications, Ajith H. Ranabahu, E. Michael Maximilien, Amit P. Sheth, Krishnaprasad Thirunarayan

Kno.e.sis Publications

Cloud computing has changed the technology landscape by offering flexible and economical computing resources to the masses. However, vendor lock-in makes the migration of applications and data across clouds an expensive proposition. The lock-in is especially serious when considering the new technology trend of combining cloud with mobile devices.

In this paper, we present a domain specific language (DSL) that is purposely created for generating hybrid applications spanning across mobile devices as well as computing clouds. We propose a model-driven development process that makes use of a DSL to provide sufficient programming abstractions over both cloud and mobile features. We …

Semantic Annotation And Search For Resources In The Next Generation Web With Sa-Rest, Ajith H. Ranabahu, Amit P. Sheth, Maryam Panahiazar, Sanjaya Wijeratne

Kno.e.sis Publications

SA-REST, the W3C member submission, can be used for supporting a wide variety of Plain Old Semantic HTML (POSH) annotation capabilities on any type of Web resource. Kino framework and tools provide support of capabilities to realize SA-RESTs promised value. These tools include (a) a browser-plugin to support annotation of a Web resource (including services) with respect to an ontology, domain model or vocabulary, (b) an annotation aware indexing engine and (c) faceted search and selection of the Web resources. At one end of the spectrum, we present Kino^E (aka Kino for Enterprise) which uses NCBO formal ontologies and …

Personalized Filtering Of The Twitter Stream, Pavan Kapanipathi, Fabrizio Orlandi, Amit P. Sheth, Alexandre Passant

Kno.e.sis Publications

With the rapid growth in users on social networks, there is a corresponding increase in user-generated content, in turn resulting in information overload. On Twitter, for example, users tend to receive uninterested information due to their non-overlapping interests from the people whom they follow. In this paper we present a Semantic Web approach to filter public tweets matching interests from personalized user profiles. Our approach includes automatic generation of multi-domain and personalized user profiles, filtering Twitter stream based on the generated profiles and delivering them in real-time. Given that users interests and personalization needs change with time, we also discuss …

Refnetbuilder: A Platform For Construction Of Integrated Reference Gene Regulatory Networks From Expressed Sequence Tags, Ying Li, Ping Gong, Edward J. Perkins, Chaoyang Zhang, Nan Wang

Faculty Publications

Background: Gene Regulatory Networks (GRNs) provide integrated views of gene interactions that control biological processes. Many public databases contain biological interactions extracted from experimentally validated literature reports, but most furnish only information for a few genetic model organisms. In order to provide a bioinformatic tool for researchers who work with non-model organisms, we developed RefNetBuilder, a new platform that allows construction of putative reference pathways or GRNs from expressed sequence tags (ESTs).

Results: RefNetBuilder was designed to have the flexibility to extract and archive pathway or GRN information from public databases such as the Kyoto Encyclopedia of Genes …

Semantic Computing In Real-World: Vertical And Horizontal Application Within Enterprise And On The Web, Amit P. Sheth

Kno.e.sis Publications

No abstract provided.

Structural Analysis Of The Hot Spots In The Binding Between H1n1 Ha And The 2di Antibody: Do Mutations Of H1n1 From 1918 To 2009 Affect Much On This Binding?, Qian Liu, Steven C. H. Hoi, Chinh T. T. Su, Zhenhua Li, Chee-Keong Kwoh, Limsoon Wong, Jinyan Li

Research Collection School Of Computing and Information Systems

Worldwide and substantial mortality caused by the 2009 H1N1 influenza A has stimulated a new surge of research on H1N1 viruses. An epitope conservation has been learned in the HA1 protein that allows antibodies to cross-neutralize both 1918 and 2009 H1N1. However, few works have thoroughly studied the binding hot spots in those two antigen–antibody interfaces which are responsible for the antibody cross-neutralization. We apply predictive methods to identify binding hot spots at the epitope sites of the HA1 proteins and at the paratope sites of the 2D1 antibody. We find that the six mutations at the HA1's epitope from …

Kino: A Generic Document Management System For Biologists Using Sa-Rest And Faceted Search, Ajith Harshana Ranabahu, Priti Parikh, Maryam Panahiazar, Amit P. Sheth

Kno.e.sis Publications

Document management has become an important consideration for the scientific community over the last decade. Human knowledge is central to many scientific domains, thus it is not possible to completely automate the document management process. Managing scientific documents require a semi-automatic approach to overcome issues of large volume, yet support the human participation in the process. In this paper we present Kino, a set of tools that streamline the document management process in life science domains. Kino is integrated with National Center for Biomedical Ontology (NCBO), providing scientists access to quality domain models. Annotated documents are indexed using a faceted …

Gc-Content Normalization For Rna-Seq Data, Davide Risso, Katja Schwartz, Gavin Sherlock, Sandrine Dudoit

U.C. Berkeley Division of Biostatistics Working Paper Series

Background: Transcriptome sequencing (RNA-Seq) has become the assay of choice for high-throughput studies of gene expression. However, as is the case with microarrays, major technology-related artifacts and biases affect the resulting expression measures. Normalization is therefore essential to ensure accurate inference of expression levels and subsequent analyses thereof.

Results: We focus on biases related to GC-content and demonstrate the existence of strong sample-specific GC-content effects on RNA-Seq read counts, which can substantially bias differential expression analysis. We propose three simple within-lane gene-level GC-content normalization approaches and assess their performance on two different RNA-Seq datasets, involving different species and experimental designs. …

Multiple Testing Of Local Maxima For Detection Of Peaks In Chip-Seq Data, Armin Schwartzman, Andrew Jaffe, Yulia Gavrilov, Clifford A. Meyer

Harvard University Biostatistics Working Paper Series

No abstract provided.

Gata-Family Transcription Factors In Magnaporthe Oryzae, Cristian F. Quispe

Department of Agronomy and Horticulture: Dissertations, Theses, and Student Research

The filamentous fungus, Magnaporthe oryzae, responsible for blast rice disease, destroys around 10-30% of the rice crop annually. Infection begins when the specialized infection structure, the appressorium, generates enormous internal turgor pressure through the accumulation of glycerol. This turgor acts on a penetration peg emerging at the base of the cell, causing it to breach the leaf surface allowing its infection.

The enzyme trehalose-6- phosphate synthase (Tps1) is a central regulator of the transition from appressorium development to infectious hyphal growth. In the first chapter we show that initiation of rice blast disease requires a regulatory mechanism involving an …

Citizen Sensing: Opportunities And Challenges In Mining Social Signals And Perceptions, Amit P. Sheth

Kno.e.sis Publications

Millions of persons have become 'citizens' of an Internet- or Web-enabled social community. Web 2.0 fostered the open environment and applications for tagging, blogging, wikis, and social networking sites that have made information consumption, production, and sharing so incredibly easy. An interconnected network of people who actively observe, report, collect, analyze, and disseminate information via text, audio, or video messages, increasingly through pervasively connected mobile devices, has led to what we term citizen sensing. In this talk, we review recent progress in supporting collective intelligence through intelligent processing of citizen sensing. Key issues we cover in this talk are: - …

A Unified Approach To Non-Negative Matrix Factorization And Probabilistic Latent Semantic Indexing, Karthik Devarajan, Guoli Wang, Nader Ebrahimi

COBRA Preprint Series

Non-negative matrix factorization (NMF) by the multiplicative updates algorithm is a powerful machine learning method for decomposing a high-dimensional nonnegative matrix V into two matrices, W and H, each with nonnegative entries, V ~ WH. NMF has been shown to have a unique parts-based, sparse representation of the data. The nonnegativity constraints in NMF allow only additive combinations of the data which enables it to learn parts that have distinct physical representations in reality. In the last few years, NMF has been successfully applied in a variety of areas such as natural language processing, information retrieval, image processing, speech recognition …

Evolving Hard Problems: Generating Human Genetics Datasets With A Complex Etiology, Daniel S Himmelstein, Casey S Greene, Jason H Moore

Dartmouth Scholarship

BackgroundA goal of human genetics is to discover genetic factors that influence individuals' susceptibility to common diseases. Most common diseases are thought to result from the joint failure of two or more interacting components instead of single component failures. This greatly complicates both the task of selecting informative genetic variants and the task of modeling interactions between them. We and others have previously developed algorithms to detect and model the relationships between these genetic factors and disease. Previously these methods have been evaluated with datasets simulated according to pre-defined genetic models.

Beyond Structural Genomics: Computational Approaches For The Identification Of Ligand Binding Sites In Protein Structures, Dario Ghersi, Roberto Sanchez

Interdisciplinary Informatics Faculty Publications

t Structural genomics projects have revealed structures for a large number of proteins of unknown function. Understanding the interactions between these proteins and their ligands would provide an initial step in their functional characterization. Binding site identification methods are a fast and cost-effective way to facilitate the characterization of functionally important protein regions. In this review we describe our recently developed methods for binding site identification in the context of existing methods. The advantage of energy-based approaches is emphasized, since they provide flexibility in the identifi- cation and characterization of different types of binding sites

Local Closed World Semantics: Keep It Simple, Stupid!, Adila Krishnadhi, Kunal Sengupta, Pascal Hitzler

Computer Science and Engineering Faculty Publications

A combination of open and closed-world reasoning (usually called local closed world reasoning) is a desirable capability of knowledge representation formalisms for Semantic Web applications. However, none of the proposals made to date for extending description logics with local closed world capabilities has had any significant impact on applications. We believe that one of the key reasons for this is that current proposals fail to provide approaches which are intuitively accessible for application developers at the same time are applicable, as extensions, to expressive description logics as SROIQ, which underlies the Web Ontology Language OWL.

In this paper, we propose …

Web Wisdom: An Essay On How Web 2.0 And Semantic Web Can Foster A Global Knowledge Society, Christopher Thomas, Amit P. Sheth

Kno.e.sis Publications

Admittedly this is a presumptuous title that should never be used when reporting on individual research advances. Wisdom is just not a scientific concept. In this case, though, we are reporting on recent developments on the web that lead us to believe that the web is on the way to providing a platform for not only information acquisition and business transactions but also for large scale knowledge development and decision support. It is likely that by now every web user has participated in some sort of social function or knowledge accumulating function on the web, many times without even being …

A Bayesian Model Averaging Approach For Observational Gene Expression Studies, Xi Kathy Zhou, Fei Liu, Andrew J. Dannenberg

COBRA Preprint Series

Identifying differentially expressed (DE) genes associated with a sample characteristic is the primary objective of many microarray studies. As more and more studies are carried out with observational rather than well controlled experimental samples, it becomes important to evaluate and properly control the impact of sample heterogeneity on DE gene finding. Typical methods for identifying DE genes require ranking all the genes according to a pre-selected statistic based on a single model for two or more group comparisons, with or without adjustment for other covariates. Such single model approaches unavoidably result in model misspecification, which can lead to increased error …

Smob: The Best Of Both Worlds, Alexandre Passant, Julia Anaya, Owen Sacco, Pavan Kapanipathi

Kno.e.sis Publications

This paper presents the architecture of SMOB and the way it combines Semantic Web standards (RDF(S) / SPARQL) and new protocols such as PubSubHubbub to enable a Federated and Privacy-Aware Social Web.