Department of Computer Science Activity 1998-2004

This report summarizes much of the research and teaching activity of the Department of Computer Science at Dartmouth College between late 1998 and late 2004. The material for this report was collected as part of the final report for NSF Institutional Infrastructure award EIA-9802068, which funded equipment and technical staff during that six-year period. This equipment and staff supported essentially all of the department\u27s research activity during that period

Evolution of motif variants and positional bias of the cyclic-AMP response element

BACKGROUND: Transcription factors regulate gene expression by interacting with their specific DNA binding sites. Some transcription factors, particularly those involved in transcription initiation, always bind close to transcription start sites (TSS). Others have no such preference and are functional on sites even tens of thousands of base pairs (bp) away from the TSS. The Cyclic-AMP response element (CRE) binding protein (CREB) binds preferentially to a palindromic sequence (TGACGTCA), known as the canonical CRE, and also to other CRE variants. CREB can activate transcription at CREs thousands of bp away from the TSS, but in mammals CREs are found far more frequently within 1 to 150 bp upstream of the TSS than in any other region. This property is termed positional bias. The strength of CREB binding to DNA is dependent on the sequence of the CRE motif. The central CpG dinucleotide in the canonical CRE (TGACGTCA) is critical for strong binding of CREB dimers. Methylation of the cytosine in the CpG can inhibit binding of CREB. Deamination of the methylated cytosines causes a C to T transition, resulting in a functional, but lower affinity CRE variant, TGATGTCA. RESULTS: We performed genome-wide surveys of CREs in a number of species (from worm to human) and showed that only vertebrates exhibited a CRE positional bias. We performed pair-wise comparisons of human CREs with orthologous sequences in mouse, rat and dog genomes and found that canonical and TGATGTCA variant CREs are highly conserved in mammals. However, when orthologous sequences differ, canonical CREs in human are most frequently TGATGTCA in the other species and vice-versa. We have identified 207 human CREs showing such differences. CONCLUSION: Our data suggest that the positional bias of CREs likely evolved after the separation of urochordata and vertebrata. Although many canonical CREs are conserved among mammals, there are a number of orthologous genes that have canonical CREs in one species but the TGATGTCA variant in another. These differences are likely due to deamination of the methylated cytosines in the CpG and may contribute to differential transcriptional regulation among orthologous genes

Hierarchical ensemble methods for protein function prediction

Protein function prediction is a complex multiclass multilabel classification problem, characterized by multiple issues such as the incompleteness of the available annotations, the integration of multiple sources of high dimensional biomolecular data, the unbalance of several functional classes, and the difficulty of univocally determining negative examples. Moreover, the hierarchical relationships between functional classes that characterize both the Gene Ontology and FunCat taxonomies motivate the development of hierarchy-aware prediction methods that showed significantly better performances than hierarchical-unaware \u201cflat\u201d prediction methods. In this paper, we provide a comprehensive review of hierarchical methods for protein function prediction based on ensembles of learning machines. According to this general approach, a separate learning machine is trained to learn a specific functional term and then the resulting predictions are assembled in a \u201cconsensus\u201d ensemble decision, taking into account the hierarchical relationships between classes. The main hierarchical ensemble methods proposed in the literature are discussed in the context of existing computational methods for protein function prediction, highlighting their characteristics, advantages, and limitations. Open problems of this exciting research area of computational biology are finally considered, outlining novel perspectives for future research

Faculty Publications and Creative Works 2004

Faculty Publications & Creative Works is an annual compendium of scholarly and creative activities of University of New Mexico faculty during the noted calendar year. Published by the Office of the Vice President for Research and Economic Development, it serves to illustrate the robust and active intellectual pursuits conducted by the faculty in support of teaching and research at UNM

Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]

An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u

Real-Time Context-Aware Computing with Applications in Civil Infrastructure Systems.

This dissertation contributes a structured understanding of the fundamental processes involved in developing context-aware computing applications for the civil infrastructure industry. The civil infrastructure industry is characterized by mobile human and machine agents actively engaged in real-time decision-making tasks in a dynamic and unstructured workspace environment. This distinguishes context-aware computing from other computing technologies in three aspects: 1) it has the ability to perceive, interpret, and adapt to the agent’s evolving workspace; 2) It streamlines project data and presents the agent with information pertinent to its context, thus eliminating the agent’s tasks to accomplish the same; 3) By leveraging contextual information, it supplements decision-making tasks in real-time. This research has successfully investigated technical approaches to address fundamental aspects of introducing context-aware applications to civil engineering, including: the ubiquitous localization of mobile agents in dynamic, unstructured environments; abstraction of the spatial-context and identifying the objects of interest to the agent; and the suitability of using standard models to manage and organize data for context-aware computing applications. A computational framework for designing context-aware applications to support real-time decision-making has also been implemented. The framework allows researchers and other end users to leverage currently available context-sensing technology to design and implement innovative solutions to domain specific problems. The researched methods have been validated through several experiments conducted at the University of Michigan, the National Institute of Standards and Technology, and the Michigan Department of Transportation. These experiments have resulted in the implementation of several applications – to support real-life decision-making tasks – that not only serve to illustrate the usefulness of the framework, but also have significant social and economic implications. Among these applications are the controlled drilling system that warns drilling personnel when the drill bit tip is about to strike rebar or utility lines, thus helping preserve the structural integrity of concrete decks and preventing utility strike accidents; an automated fault detection system that diagnoses faulty components of an underperforming HVAC distribution network; and an innovative bridge inspection solution that supports condition assessment decision-making, thus introducing objectivity to visual condition assessment by providing concurrence with the Structural Health Monitoring data.PhDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99816/1/akulaman_1.pd

The structure and function of biological networks

Biology has been revolutionized in recent years by an explosion in the availability of data. Transforming this new wealth of data into meaningful biological insights and clinical breakthroughs requires a complete overhaul both in the questions being asked and the methodologies used to answer them. A major challenge in organizing and understanding the data is the ability to define the structure in biological systems, especially high level structures. Networks are a powerful and versatile tool useful in bridging the data and the complex biological systems. To address the importance of the higher-level modular and hierarchical structure in biological networks, we have investigated in this thesis the topological structure of protein-protein interaction networks through a comprehensive network analysis using statistical and computational techniques and publicly available protein-protein interaction data sets. Furthermore, we have designed and implemented a novel and efficient computational approach to identify modules from a seed protein. The experiment results demonstrate the efficiency and effectiveness of this approach in finding a module whose members exhibit high functional coherency. In addition, toward quantitative studies of protein translation regulatory networks (PTRN), we have developed a novel approach to reconstruct the PTRN through integration of protein-protein interaction data and Gene Ontology annotations. We have applied computational techniques based on hierarchical random graph model on these reconstructed PTRN to explore their modular and hierarchical and to detect missing and false positive links from these networks. The identification of the high order structures in these networks unveils insights into their functional organization.Ph.D., Information Science and Technology -- Drexel University, 201

Feature Selection and Classification Methods for Decision Making: A Comparative Analysis

The use of data mining methods in corporate decision making has been increasing in the past decades. Its popularity can be attributed to better utilizing data mining algorithms, increased performance in computers, and results which can be measured and applied for decision making. The effective use of data mining methods to analyze various types of data has shown great advantages in various application domains. While some data sets need little preparation to be mined, whereas others, in particular high-dimensional data sets, need to be preprocessed in order to be mined due to the complexity and inefficiency in mining high dimensional data processing. Feature selection or attribute selection is one of the techniques used for dimensionality reduction. Previous research has shown that data mining results can be improved in terms of accuracy and efficacy by selecting the attributes with most significance. This study analyzes vehicle service and sales data from multiple car dealerships. The purpose of this study is to find a model that better classifies existing customers as new car buyers based on their vehicle service histories. Six different feature selection methods such as; Information Gain, Correlation Based Feature Selection, Relief-F, Wrapper, and Hybrid methods, were used to reduce the number of attributes in the data sets are compared. The data sets with the attributes selected were run through three popular classification algorithms, Decision Trees, k-Nearest Neighbor, and Support Vector Machines, and the results compared and analyzed. This study concludes with a comparative analysis of feature selection methods and their effects on different classification algorithms within the domain. As a base of comparison, the same procedures were run on a standard data set from the financial institution domain