1,617 research outputs found
Void-and-Cluster Sampling of Large Scattered Data and Trajectories
We propose a data reduction technique for scattered data based on statistical
sampling. Our void-and-cluster sampling technique finds a representative subset
that is optimally distributed in the spatial domain with respect to the blue
noise property. In addition, it can adapt to a given density function, which we
use to sample regions of high complexity in the multivariate value domain more
densely. Moreover, our sampling technique implicitly defines an ordering on the
samples that enables progressive data loading and a continuous level-of-detail
representation. We extend our technique to sample time-dependent trajectories,
for example pathlines in a time interval, using an efficient and iterative
approach. Furthermore, we introduce a local and continuous error measure to
quantify how well a set of samples represents the original dataset. We apply
this error measure during sampling to guide the number of samples that are
taken. Finally, we use this error measure and other quantities to evaluate the
quality, performance, and scalability of our algorithm.Comment: To appear in IEEE Transactions on Visualization and Computer Graphics
as a special issue from the proceedings of VIS 201
Data mining techniques for complex application domains
The emergence of advanced communication techniques has increased availability of large collection of data in electronic form in a number of application domains including healthcare, e- business, and e-learning. Everyday a large amount of records are stored electronically. However, finding useful information from such a large data collection is a challenging issue. Data mining technology aims automatically extracting hidden knowledge from large data repositories exploiting sophisticated algorithms. The hidden knowledge in the electronic data may be potentially utilized to facilitate the procedures, productivity, and reliability of several application domains.
The PhD activity has been focused on novel and effective data mining approaches to tackle the complex data coming from two main application domains: Healthcare data analysis and Textual data analysis.
The research activity, in the context of healthcare data, addressed the application of different data mining techniques to discover valuable knowledge from real exam-log data of patients. In particular, efforts have been devoted to the extraction of medical pathways, which can be exploited to analyze the actual treatments followed by patients. The derived knowledge not only provides useful information to deal with the treatment procedures but may also play an important role in future predictions of potential patient risks associated with medical treatments.
The research effort in textual data analysis is twofold. On the one hand, a novel approach to discovery of succinct summaries of large document collections has been proposed. On the other hand, the suitability of an established descriptive data mining to support domain experts in making decisions has been investigated. Both research activities are focused on adopting widely exploratory data mining techniques to textual data analysis, which require overcoming intrinsic limitations for traditional algorithms for handling textual documents efficiently and effectively
Probabilistic analysis of the human transcriptome with side information
Understanding functional organization of genetic information is a major
challenge in modern biology. Following the initial publication of the human
genome sequence in 2001, advances in high-throughput measurement technologies
and efficient sharing of research material through community databases have
opened up new views to the study of living organisms and the structure of life.
In this thesis, novel computational strategies have been developed to
investigate a key functional layer of genetic information, the human
transcriptome, which regulates the function of living cells through protein
synthesis. The key contributions of the thesis are general exploratory tools
for high-throughput data analysis that have provided new insights to
cell-biological networks, cancer mechanisms and other aspects of genome
function.
A central challenge in functional genomics is that high-dimensional genomic
observations are associated with high levels of complex and largely unknown
sources of variation. By combining statistical evidence across multiple
measurement sources and the wealth of background information in genomic data
repositories it has been possible to solve some the uncertainties associated
with individual observations and to identify functional mechanisms that could
not be detected based on individual measurement sources. Statistical learning
and probabilistic models provide a natural framework for such modeling tasks.
Open source implementations of the key methodological contributions have been
released to facilitate further adoption of the developed methods by the
research community.Comment: Doctoral thesis. 103 pages, 11 figure
Multivariate Pointwise Information-Driven Data Sampling and Visualization
With increasing computing capabilities of modern supercomputers, the size of
the data generated from the scientific simulations is growing rapidly. As a
result, application scientists need effective data summarization techniques
that can reduce large-scale multivariate spatiotemporal data sets while
preserving the important data properties so that the reduced data can answer
domain-specific queries involving multiple variables with sufficient accuracy.
While analyzing complex scientific events, domain experts often analyze and
visualize two or more variables together to obtain a better understanding of
the characteristics of the data features. Therefore, data summarization
techniques are required to analyze multi-variable relationships in detail and
then perform data reduction such that the important features involving multiple
variables are preserved in the reduced data. To achieve this, in this work, we
propose a data sub-sampling algorithm for performing statistical data
summarization that leverages pointwise information theoretic measures to
quantify the statistical association of data points considering multiple
variables and generates a sub-sampled data that preserves the statistical
association among multi-variables. Using such reduced sampled data, we show
that multivariate feature query and analysis can be done effectively. The
efficacy of the proposed multivariate association driven sampling algorithm is
presented by applying it on several scientific data sets.Comment: 25 page
A novel diffusion tensor imaging-based computer-aided diagnostic system for early diagnosis of autism.
Autism spectrum disorders (ASDs) denote a significant growing public health concern. Currently, one in 68 children has been diagnosed with ASDs in the United States, and most children are diagnosed after the age of four, despite the fact that ASDs can be identified as early as age two. The ultimate goal of this thesis is to develop a computer-aided diagnosis (CAD) system for the accurate and early diagnosis of ASDs using diffusion tensor imaging (DTI). This CAD system consists of three main steps. First, the brain tissues are segmented based on three image descriptors: a visual appearance model that has the ability to model a large dimensional feature space, a shape model that is adapted during the segmentation process using first- and second-order visual appearance features, and a spatially invariant second-order homogeneity descriptor. Secondly, discriminatory features are extracted from the segmented brains. Cortex shape variability is assessed using shape construction methods, and white matter integrity is further examined through connectivity analysis. Finally, the diagnostic capabilities of these extracted features are investigated. The accuracy of the presented CAD system has been tested on 25 infants with a high risk of developing ASDs. The preliminary diagnostic results are promising in identifying autistic from control patients
Identification of Yeast Transcriptional Regulation Networks Using Multivariate Random Forests
The recent availability of whole-genome scale data sets that investigate complementary and diverse aspects of transcriptional regulation has spawned an increased need for new and effective computational approaches to analyze and integrate these large scale assays. Here, we propose a novel algorithm, based on random forest methodology, to relate gene expression (as derived from expression microarrays) to sequence features residing in gene promoters (as derived from DNA motif data) and transcription factor binding to gene promoters (as derived from tiling microarrays). We extend the random forest approach to model a multivariate response as represented, for example, by time-course gene expression measures. An analysis of the multivariate random forest output reveals complex regulatory networks, which consist of cohesive, condition-dependent regulatory cliques. Each regulatory clique features homogeneous gene expression profiles and common motifs or synergistic motif groups. We apply our method to several yeast physiological processes: cell cycle, sporulation, and various stress conditions. Our technique displays excellent performance with regard to identifying known regulatory motifs, including high order interactions. In addition, we present evidence of the existence of an alternative MCB-binding pathway, which we confirm using data from two independent cell cycle studies and two other physioloigical processes. Finally, we have uncovered elaborate transcription regulation refinement mechanisms involving PAC and mRRPE motifs that govern essential rRNA processing. These include intriguing instances of differing motif dosages and differing combinatorial motif control that promote regulatory specificity in rRNA metabolism under differing physiological processes
NLP Driven Models for Automatically Generating Survey Articles for Scientific Topics.
This thesis presents new methods that use natural language processing (NLP) driven models for summarizing research in scientific fields. Given a topic query in the form of a text string, we present methods for finding research articles relevant to the topic as well as summarization algorithms that use lexical and discourse information present in the text of these articles to generate coherent and readable extractive summaries of past research on the topic. In addition to summarizing prior research, good survey articles should also forecast future trends. With this motivation, we present work on forecasting future impact of scientific publications using NLP driven features.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113407/1/rahuljha_1.pd
- …