Jay Pedersen

Conceptual Foundations of Crowdsourcing: A Review of IS Research

Crowd sourcing is a collaboration model enabled by people-centric web technologies to solve individual, organizational, and societal problems using a dynamically formed crowd of people who respond to an open call for participation. We report on a literature survey of crowd sourcing research, focusing on top journals and conferences in the Information Systems (IS) field. To our knowledge, ours is the first effort of this type in the IS discipline. Contributions include providing a synopsis of crowd sourcing research to date, a common definition for crowd sourcing, and a conceptual model for guiding future studies of crowd sourcing. We show how existing IS literature applies to the elements of that conceptual model: Problem, People (Problem Owner, Individual, and Crowd), Governance, Process, Technology, and Outcome. We close with suggestions for future research.

Bio-logic builder: a non-technical tool for building dynamical, qualitative models.

Computational modeling of biological processes is a promising tool in biomedical research. While a large part of its potential lies in the ability to integrate it with laboratory research, modeling currently generally requires a high degree of training in mathematics and/or computer science. To help address this issue, we have developed a web-based tool, Bio-Logic Builder, that enables laboratory scientists to define mathematical representations (based on a discrete formalism) of biological regulatory mechanisms in a modular and non-technical fashion. As part of the user interface, generalized “bio-logic” modules have been defined to provide users with the building blocks for many biological processes. To build/modify computational models, experimentalists provide purely qualitative information about a particular regulatory mechanisms as is generally found in the laboratory. The Bio-Logic Builder subsequently converts the provided information into a mathematical representation described with Boolean expressions/rules. We used this tool to build a number of dynamical models, including a 130-protein large-scale model of signal transduction with over 800 interactions, influenza A replication cycle with 127 species and 200+ interactions, and mammalian and budding yeast cell cycles. We also show that any and all qualitative regulatory mechanisms can be built using this tool.

A content and structural assessment of oxidative motifs across a diverse set of life forms

Exposure to weightlessness (microgravity) or other protein stresses are detrimental to animal and human protein tissue health. Protein damage has been associated with stress and is linked to aging and the onset of diseases such as Alzheimer׳s, Parkinson׳s, sepsis, and others. Protein stresses may cause alterations to physical protein structure, altering its functional identity. Alterations from stresses such as microgravity may be responsible for forms of muscle atrophy (as noted in returning astronauts), however, protein stresses come from other sources as well. Oxidative carbonylation is a protein stress which is a driving force behind protein decay and is attracted to protein segments enriched in R, K, P, T, E and S residues. Since mitochondria apply oxidative processes to produce ATP, their proteins may be placed in the same danger as those that are exposed to stresses. However, they do not appear to be impacted in the same way. Across 14 diverse organisms, we evaluate the coverage of motifs which are high in the amino acids thought to be affected by protein stresses such as oxidation. For this study, we study RKPT and PEST motifs which are both responsible for attracting forms of oxidation across mitochondrial and non-mitochondrial proteins. We show that mitochondrial proteins have fewer of these oxidative sites compared to non-mitochondrial proteins. Additionally, we analyze the oxidative regions to determine that their motifs preferentially tend to make up the connection points between the four kinds of structures of folded proteins (helices, turns, sheets, and coils).

Modeling the Effects of Microgravity on Oxidation in Mitochondria: A Protein Damage Assessment across a Diverse Set of Life Forms

Exposure to microgravity conditions is detrimental to animal and human protein tissue and is linked to ailments associated with aging, disease and other disorders originating at the protein level. With exposure, dangerously low blood pressure results from diminished blood production forces the heart to beat at abnormal rates and causes damage. The heart, like the other muscles of the body, risk developing muscular atrophy from the reduced dependence on muscle-use. Oxidative carbonylation, the addition of a CO to an amino acid chain, is a natural process used by the cell to degrade and remove proteins. This reaction may also cause many of the diseases associated with protein dysfunction (Alzheimers, muscular atrophy, Parkinsons, sepsis, etc.). Although aging has been associated with similar ailments from protein degradation, the stress from weightlessness is thought to increase the rates of oxidative processes impacting general health by upsetting protein function and its structure. Carbonylation is an oxidative reaction for which, motifs high in R, K, P, T, E and S residues can be used to explore its composition in protein data. Since mitochondria also apply oxidative processes to make energy, we hypothesize that this reaction is highly contained so as to minimize local oxidative damage. In this paper, we evaluate the coverage of motifs which are likely attractors of oxidative activity across mitochondrial and non-mitochondrial protein data of fourteen diverse organisms. Here we show that mitochondrial proteins have generally reduced amounts of the same oxidative carbonylation content which we found in abundance in the organisms nuclear proteins. Furthermore, we show that this general finding is similar between two major profiling systems: oxidative carbonylation (RKPT enriched sequences) and protein degradation (PEST sequences). We suggest an mitochondrial intolerance for motifs that may attract forms of oxidation.

An alternative database approach for management of SNOMED CT and improved patient data queries

OBJECTIVE SNOMED CT is the international lingua franca of terminologies for human health. Based in Description Logics (DL), the terminology enables data queries that incorporate inferences between data elements, as well as, those relationships that are explicitly stated. However, the ontologic and polyhierarchical nature of the SNOMED CT concept model make it difficult to implement in its entirety within electronic health record systems that largely employ object oriented or relational database architectures. The result is a reduction of data richness, limitations of query capability and increased systems overhead. The hypothesis of this research was that a graph database (graph DB) architecture using SNOMED CT as the basis for the data model and subsequently modeling patient data upon the semantic core of SNOMED CT could exploit the full value of the terminology to enrich and support advanced data querying capability of patient data sets. METHODS The hypothesis was tested by instantiating a graph DB with the fully classified SNOMED CT concept model. The graph DB instance was tested for integrity by calculating the transitive closure table for the SNOMED CT hierarchy and comparing the results with transitive closure tables created using current, validated methods. The graph DB was then populated with 461,171 anonymized patient record fragments and over 2.1 million associated SNOMED CT clinical findings. Queries, including concept negation and disjunction, were then run against the graph database and an enterprise Oracle relational database (RDBMS) of the same patient data sets. The graph DB was then populated with laboratory data encoded using LOINC, as well as, medication data encoded with RxNorm and complex queries performed using LOINC, RxNorm and SNOMED CT to identify uniquely described patient populations. RESULTS A graph database instance was successfully created for two international releases of SNOMED CT and two US SNOMED CT editions. Transitive closure tables and descriptive statistics generated using the graph database were identical to those using validated methods. Patient queries produced identical patient count results to the Oracle RDBMS with comparable times. Database queries involving defining attributes of SNOMED CT concepts were possible with the graph DB. The same queries could not be directly performed with the Oracle RDBMS representation of the patient data and required the creation and use of external terminology services. Further, queries of undefined depth were successful in identifying unknown relationships between patient cohorts. CONCLUSION The results of this study supported the hypothesis that a patient database built upon and around the semantic model of SNOMED CT was possible. The model supported queries that leveraged all aspects of the SNOMED CT logical model to produce clinically relevant query results. Logical disjunction and negation queries were possible using the data model, as well as, queries that extended beyond the structural IS_A hierarchy of SNOMED CT to include queries that employed defining attribute-values of SNOMED CT concepts as search parameters. As medical terminologies, such as SNOMED CT, continue to expand, they will become more complex and model consistency will be more difficult to assure. Simultaneously, consumers of data will increasingly demand improvements to query functionality to accommodate additional granularity of clinical concepts without sacrificing speed. This new line of research provides an alternative approach to instantiating and querying patient data represented using advanced computable clinical terminologies.

Examining Disease Risk Factors by Mining Publicly Available Information

Human disease and associated risk factors are of great interest in the medical field. The skyrocketing cost of health care makes the understanding of disease risk factors of even greater importance. When risk factors are well understood for a disease it is possible to educate the public to reduce their risk by avoiding risk factors that they can control. There are publicly available data stores which document risk factors facing the general public in the United States. In particular, the Behavioral Risk Factor Surveillance System (BRFSS) has been maintained by the Center for Disease Control (CDC). This study examines the disease of diabetes using a set of risk factors which the BRFSS maintains. A logistic regression model is created which models diabetes as a function of risk factors. Nine years of existing data from BRFSS was used in building this model (for the years 2002 to 2010). The generated model shows promise in modeling diabetes as a function of risk factors and correctly identified obesity as an important risk factor for diabetes. Studies of this nature can be very cost effective and may generate insights into the risks of disease.

PathwayLinks: Network analysis of metabolic pathways across bacterial organisms in a community

The Human Microbiome Project (HMP) recognizes that several microbiome environments reside within the human body (such as the mouth and the GI tract). HMP is involved in documenting and sequencing bacteria existing in these environments. These environments can be considered to host a community of bacteria, where one bacterium may provide support for the metabolic needs of other bacteria. Publicly available metabolic pathway information exists for many of these organisms, most notably the well-established KEGG database. To predict the metabolic relationships that exist between organisms living in a community, we analyzed publicly available metabolic data and developed a set of procedures whose input is a set of organisms in a community and whose output is a set of predictions of possible metabolic linkages between them. We refer to these procedures collectively as the PathwayLinks procedures, as they find possible links between metabolic pathways. A pilot study was conducted using the well-known Red-Complex of bacteria in the mouth, which is linked to gingivitis. Results indicate that the Citrulline and Protoporphyrin metabolites may link two of the key organisms in the complex. This research shows promise in providing a theoretical basis for predicting metabolic relationships between bacteria living in a community.