Computer Sciences Commons^™

Citationally Enhanced Semantic Literature Based Discovery, John David Fleig

CCE Theses and Dissertations

We are living within the age of information. The ever increasing flow of data and publications poses a monumental bottleneck to scientific progress as despite the amazing abilities of the human mind, it is woefully inadequate in processing such a vast quantity of multidimensional information. The small bits of flotsam and jetsam that we leverage belies the amount of useful information beneath the surface. It is imperative that automated tools exist to better search, retrieve, and summarize this content. Combinations of document indexing and search engines can quickly find you a document whose content best matches your query - if …

Efficient Reduced Bias Genetic Algorithm For Generic Community Detection Objectives, Aditya Karnam Gururaj Rao

Theses

The problem of community structure identification has been an extensively investigated area for biology, physics, social sciences, and computer science in recent years for studying the properties of networks representing complex relationships. Most traditional methods, such as K-means and hierarchical clustering, are based on the assumption that communities have spherical configurations. Lately, Genetic Algorithms (GA) are being utilized for efficient community detection without imposing sphericity. GAs are machine learning methods which mimic natural selection and scale with the complexity of the network. However, traditional GA approaches employ a representation method that dramatically increases the solution space to be searched by …

Clinical Information Extraction From Unstructured Free-Texts, Mingzhe Tao

Legacy Theses & Dissertations (2009 - 2024)

Information extraction (IE) is a fundamental component of natural language processing (NLP) that provides a deeper understanding of the texts. In the clinical domain, documents prepared by medical experts (e.g., discharge summaries, drug labels, medical history records) contain a significant amount of clinically-relevant information that is crucial to the overall well-being of patients. Unfortunately, in many cases, clinically-relevant information is presented in an unstructured format, predominantly consisting of free-texts, making it inaccessible to computerized methods. Automatic extraction of this information can improve accessibility. However, the presence of synonymous expressions, medical acronyms, misspellings, negated phrases, and ambiguous terminologies make automatic extraction …

Novel Computational Methods For Transcript Reconstruction And Quantification Using Rna-Seq Data, Yan Huang

Theses and Dissertations--Computer Science

The advent of RNA-seq technologies provides an unprecedented opportunity to precisely profile the mRNA transcriptome of a specific cell population. It helps reveal the characteristics of the cell under the particular condition such as a disease. It is now possible to discover mRNA transcripts not cataloged in existing database, in addition to assessing the identities and quantities of the known transcripts in a given sample or cell. However, the sequence reads obtained from an RNA-seq experiment is only a short fragment of the original transcript. How to recapitulate the mRNA transcriptome from short RNA-seq reads remains a challenging problem. We …

A Novel Computational Framework For Transcriptome Analysis With Rna-Seq Data, Yin Hu

Theses and Dissertations--Computer Science

The advance of high-throughput sequencing technologies and their application on mRNA transcriptome sequencing (RNA-seq) have enabled comprehensive and unbiased profiling of the landscape of transcription in a cell. In order to address the current limitation of analyzing accuracy and scalability in transcriptome analysis, a novel computational framework has been developed on large-scale RNA-seq datasets with no dependence on transcript annotations. Directly from raw reads, a probabilistic approach is first applied to infer the best transcript fragment alignments from paired-end reads. Empowered by the identification of alternative splicing modules, this framework then performs precise and efficient differential analysis at automatically detected …

Data Mining Of Tetraloop-Tetraloop Receptors In Rna Xml Files, Sinan Ramazanoglu

Theses

RNA (Ribonucleic acid) Motifs are tertiary structures that play an important role in the folding mechanism of the RNA molecule. The overall function of a RNA Motif depends on its specific bp (base pairs) sequence that constitutes the secondary structure. Data mining is a novel method in both discovering potential tertiary structures within DNA (Deoxyribonucleic acid), RNA, and protein molecules and storing the information in databases. The RNA Motif of interest is the tetraloop-tetraloop receptor, which is composed of a highly conserved 11 nt (nucleotide) sequence and a tetraloop with the generic form of GNRA (where N = any base …

Word Sense Disambiguation In Biomedical Ontologies With Term Co-Occurrence Analysis And Document Clustering, Bill Andreopoulos, Dimitra Alexopoulou, Michael Schroeder

Faculty Publications, Computer Science

With more and more genomes being sequenced, a lot of effort is devoted to their annotation with terms from controlled vocabularies such as the GeneOntology. Manual annotation based on relevant literature is tedious, but automation of this process is difficult. One particularly challenging problem is word sense disambiguation. Terms such as |development| can refer to developmental biology or to the more general sense. Here, we present two approaches to address this problem by using term co-occurrences and document clustering. To evaluate our method we defined a corpus of 331 documents on development and developmental biology. Term co-occurrence analysis achieves an …

Word Sense Disambiguation In Biomedical Ontologies With Term Co-Occurrence Analysis And Document Clustering, Bill Andreopoulos, Dimitra Alexopoulou, Michael Schroeder

William B. Andreopoulos

Mobile Semantic Computing, Karthik Gomadam, Anupam Joshi, Amit P. Sheth

Kno.e.sis Publications

We propose to organize a special session on research in the intersection of mobile computing, the Semantic Web and Web services.

This session will examine how the research in these areas can serve as a foundation for new architectural and communication paradigms that can enhance service creation, distribution, discovery, integration and utilization in distributed and ubiquitous environments. Some of the initial areas that our early research have highlighted are :

1. Semantic annotation of data in bandwidth constrained environments such as mobile networks to promote efficient bandwidth utilization
2. Possibilities of using microformats such as RDFa and opportunities that can be explored …

Bi-Level Clustering Of Mixed Categorical And Numerical Biomedical Data, Bill Andreopoulos, Aijun An, Xiaogang Wang

Faculty Publications, Computer Science

Biomedical data sets often have mixed categorical and numerical types, where the former represent semantic information on the objects and the latter represent experimental results. We present the BILCOM algorithm for |Bi-Level Clustering of Mixed categorical and numerical data types|. BILCOM performs a pseudo-Bayesian process, where the prior is categorical clustering. BILCOM partitions biomedical data sets of mixed types, such as hepatitis, thyroid disease and yeast gene expression data with Gene Ontology annotations, more accurately than if using one type alone.

Bi-Level Clustering Of Mixed Categorical And Numerical Biomedical Data, Bill Andreopoulos, Aijun An, Xiaogang Wang

William B. Andreopoulos