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- All Subjects: Bioinformatics
- All Subjects: Data Mining
- Genre: Academic theses
- Creators: Dinu, Valentin
- Creators: Ye, Jieping
- Member of: ASU Electronic Theses and Dissertations
- Resource Type: Text
Description
Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of l-1 norm based regularization, the structured sparse learning algorithms can produce highly accurate models while imposing various predefined structures on the data, such as feature groups or graphs. In this thesis, I first propose to solve a sparse learning model with a general group structure, where the predefined groups may overlap with each other. Then, I present three real world applications which can benefit from the group structured sparse learning technique. In the first application, I study the Alzheimer's Disease diagnosis problem using multi-modality neuroimaging data. In this dataset, not every subject has all data sources available, exhibiting an unique and challenging block-wise missing pattern. In the second application, I study the automatic annotation and retrieval of fruit-fly gene expression pattern images. Combined with the spatial information, sparse learning techniques can be used to construct effective representation of the expression images. In the third application, I present a new computational approach to annotate developmental stage for Drosophila embryos in the gene expression images. In addition, it provides a stage score that enables one to more finely annotate each embryo so that they are divided into early and late periods of development within standard stage demarcations. Stage scores help us to illuminate global gene activities and changes much better, and more refined stage annotations improve our ability to better interpret results when expression pattern matches are discovered between genes.
ContributorsYuan, Lei (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Xue, Guoliang (Committee member) / Kumar, Sudhir (Committee member) / Arizona State University (Publisher)
Created2013
Description
In blindness research, the corpus callosum (CC) is the most frequently studied sub-cortical structure, due to its important involvement in visual processing. While most callosal analyses from brain structural magnetic resonance images (MRI) are limited to the 2D mid-sagittal slice, we propose a novel framework to capture a complete set of 3D morphological differences in the corpus callosum between two groups of subjects. The CCs are segmented from whole brain T1-weighted MRI and modeled as 3D tetrahedral meshes. The callosal surface is divided into superior and inferior patches on which we compute a volumetric harmonic field by solving the Laplace's equation with Dirichlet boundary conditions. We adopt a refined tetrahedral mesh to compute the Laplacian operator, so our computation can achieve sub-voxel accuracy. Thickness is estimated by tracing the streamlines in the harmonic field. We combine areal changes found using surface tensor-based morphometry and thickness information into a vector at each vertex to be used as a metric for the statistical analysis. Group differences are assessed on this combined measure through Hotelling's T2 test. The method is applied to statistically compare three groups consisting of: congenitally blind (CB), late blind (LB; onset > 8 years old) and sighted (SC) subjects. Our results reveal significant differences in several regions of the CC between both blind groups and the sighted groups; and to a lesser extent between the LB and CB groups. These results demonstrate the crucial role of visual deprivation during the developmental period in reshaping the structural architecture of the CC.
ContributorsXu, Liang (Author) / Wang, Yalin (Thesis advisor) / Maciejewski, Ross (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
Surgery as a profession requires significant training to improve both clinical decision making and psychomotor proficiency. In the medical knowledge domain, tools have been developed, validated, and accepted for evaluation of surgeons' competencies. However, assessment of the psychomotor skills still relies on the Halstedian model of apprenticeship, wherein surgeons are observed during residency for judgment of their skills. Although the value of this method of skills assessment cannot be ignored, novel methodologies of objective skills assessment need to be designed, developed, and evaluated that augment the traditional approach. Several sensor-based systems have been developed to measure a user's skill quantitatively, but use of sensors could interfere with skill execution and thus limit the potential for evaluating real-life surgery. However, having a method to judge skills automatically in real-life conditions should be the ultimate goal, since only with such features that a system would be widely adopted. This research proposes a novel video-based approach for observing surgeons' hand and surgical tool movements in minimally invasive surgical training exercises as well as during laparoscopic surgery. Because our system does not require surgeons to wear special sensors, it has the distinct advantage over alternatives of offering skills assessment in both learning and real-life environments. The system automatically detects major skill-measuring features from surgical task videos using a computing system composed of a series of computer vision algorithms and provides on-screen real-time performance feedback for more efficient skill learning. Finally, the machine-learning approach is used to develop an observer-independent composite scoring model through objective and quantitative measurement of surgical skills. To increase effectiveness and usability of the developed system, it is integrated with a cloud-based tool, which automatically assesses surgical videos upload to the cloud.
ContributorsIslam, Gazi (Author) / Li, Baoxin (Thesis advisor) / Liang, Jianming (Thesis advisor) / Dinu, Valentin (Committee member) / Greenes, Robert (Committee member) / Smith, Marshall (Committee member) / Kahol, Kanav (Committee member) / Patel, Vimla L. (Committee member) / Arizona State University (Publisher)
Created2013
Description
Genomic structural variation (SV) is defined as gross alterations in the genome broadly classified as insertions/duplications, deletions inversions and translocations. DNA sequencing ushered structural variant discovery beyond laboratory detection techniques to high resolution informatics approaches. Bioinformatics tools for computational discovery of SVs however are still missing variants in the complex cancer genome. This study aimed to define genomic context leading to tool failure and design novel algorithm addressing this context. Methods: The study tested the widely held but unproven hypothesis that tools fail to detect variants which lie in repeat regions. Publicly available 1000-Genomes dataset with experimentally validated variants was tested with SVDetect-tool for presence of true positives (TP) SVs versus false negative (FN) SVs, expecting that FNs would be overrepresented in repeat regions. Further, the novel algorithm designed to informatically capture the biological etiology of translocations (non-allelic homologous recombination and 3&ndashD; placement of chromosomes in cells –context) was tested using simulated dataset. Translocations were created in known translocation hotspots and the novel&ndashalgorithm; tool compared with SVDetect and BreakDancer. Results: 53% of false negative (FN) deletions were within repeat structure compared to 81% true positive (TP) deletions. Similarly, 33% FN insertions versus 42% TP, 26% FN duplication versus 57% TP and 54% FN novel sequences versus 62% TP were within repeats. Repeat structure was not driving the tool's inability to detect variants and could not be used as context. The novel algorithm with a redefined context, when tested against SVDetect and BreakDancer was able to detect 10/10 simulated translocations with 30X coverage dataset and 100% allele frequency, while SVDetect captured 4/10 and BreakDancer detected 6/10. For 15X coverage dataset with 100% allele frequency, novel algorithm was able to detect all ten translocations albeit with fewer reads supporting the same. BreakDancer detected 4/10 and SVDetect detected 2/10 Conclusion: This study showed that presence of repetitive elements in general within a structural variant did not influence the tool's ability to capture it. This context-based algorithm proved better than current tools even with half the genome coverage than accepted protocol and provides an important first step for novel translocation discovery in cancer genome.
ContributorsShetty, Sheetal (Author) / Dinu, Valentin (Thesis advisor) / Bussey, Kimberly (Committee member) / Scotch, Matthew (Committee member) / Wallstrom, Garrick (Committee member) / Arizona State University (Publisher)
Created2014
Description
In many fields one needs to build predictive models for a set of related machine learning tasks, such as information retrieval, computer vision and biomedical informatics. Traditionally these tasks are treated independently and the inference is done separately for each task, which ignores important connections among the tasks. Multi-task learning aims at simultaneously building models for all tasks in order to improve the generalization performance, leveraging inherent relatedness of these tasks. In this thesis, I firstly propose a clustered multi-task learning (CMTL) formulation, which simultaneously learns task models and performs task clustering. I provide theoretical analysis to establish the equivalence between the CMTL formulation and the alternating structure optimization, which learns a shared low-dimensional hypothesis space for different tasks. Then I present two real-world biomedical informatics applications which can benefit from multi-task learning. In the first application, I study the disease progression problem and present multi-task learning formulations for disease progression. In the formulations, the prediction at each point is a regression task and multiple tasks at different time points are learned simultaneously, leveraging the temporal smoothness among the tasks. The proposed formulations have been tested extensively on predicting the progression of the Alzheimer's disease, and experimental results demonstrate the effectiveness of the proposed models. In the second application, I present a novel data-driven framework for densifying the electronic medical records (EMR) to overcome the sparsity problem in predictive modeling using EMR. The densification of each patient is a learning task, and the proposed algorithm simultaneously densify all patients. As such, the densification of one patient leverages useful information from other patients.
ContributorsZhou, Jiayu (Author) / Ye, Jieping (Thesis advisor) / Mittelmann, Hans (Committee member) / Li, Baoxin (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2014
Description
The processes of a human somatic cell are very complex with various genetic mechanisms governing its fate. Such cells undergo various genetic mutations, which translate to the genetic aberrations that we see in cancer. There are more than 100 types of cancer, each having many more subtypes with aberrations being unique to each. In the past two decades, the widespread application of high-throughput genomic technologies, such as micro-arrays and next-generation sequencing, has led to the revelation of many such aberrations. Known types and subtypes can be readily identified using gene-expression profiling and more importantly, high-throughput genomic datasets have helped identify novel sub-types with distinct signatures. Recent studies showing usage of gene-expression profiling in clinical decision making in breast cancer patients underscore the utility of high-throughput datasets. Beyond prognosis, understanding the underlying cellular processes is essential for effective cancer treatment. Various high-throughput techniques are now available to look at a particular aspect of a genetic mechanism in cancer tissue. To look at these mechanisms individually is akin to looking at a broken watch; taking apart each of its parts, looking at them individually and finally making a list of all the faulty ones. Integrative approaches are needed to transform one-dimensional cancer signatures into multi-dimensional interaction and regulatory networks, consequently bettering our understanding of cellular processes in cancer. Here, I attempt to (i) address ways to effectively identify high quality variants when multiple assays on the same sample samples are available through two novel tools, snpSniffer and NGSPE; (ii) glean new biological insight into multiple myeloma through two novel integrative analysis approaches making use of disparate high-throughput datasets. While these methods focus on multiple myeloma datasets, the informatics approaches are applicable to all cancer datasets and will thus help advance cancer genomics.
ContributorsYellapantula, Venkata (Author) / Dinu, Valentin (Thesis advisor) / Scotch, Matthew (Committee member) / Wallstrom, Garrick (Committee member) / Keats, Jonathan (Committee member) / Arizona State University (Publisher)
Created2014
Description
Social networking services have emerged as an important platform for large-scale information sharing and communication. With the growing popularity of social media, spamming has become rampant in the platforms. Complex network interactions and evolving content present great challenges for social spammer detection. Different from some existing well-studied platforms, distinct characteristics of newly emerged social media data present new challenges for social spammer detection. First, texts in social media are short and potentially linked with each other via user connections. Second, it is observed that abundant contextual information may play an important role in distinguishing social spammers and normal users. Third, not only the content information but also the social connections in social media evolve very fast. Fourth, it is easy to amass vast quantities of unlabeled data in social media, but would be costly to obtain labels, which are essential for many supervised algorithms. To tackle those challenges raise in social media data, I focused on developing effective and efficient machine learning algorithms for social spammer detection.
I provide a novel and systematic study of social spammer detection in the dissertation. By analyzing the properties of social network and content information, I propose a unified framework for social spammer detection by collectively using the two types of information in social media. Motivated by psychological findings in physical world, I investigate whether sentiment analysis can help spammer detection in online social media. In particular, I conduct an exploratory study to analyze the sentiment differences between spammers and normal users; and present a novel method to incorporate sentiment information into social spammer detection framework. Given the rapidly evolving nature, I propose a novel framework to efficiently reflect the effect of newly emerging social spammers. To tackle the problem of lack of labeling data in social media, I study how to incorporate network information into text content modeling, and design strategies to select the most representative and informative instances from social media for labeling. Motivated by publicly available label information from other media platforms, I propose to make use of knowledge learned from cross-media to help spammer detection on social media.
I provide a novel and systematic study of social spammer detection in the dissertation. By analyzing the properties of social network and content information, I propose a unified framework for social spammer detection by collectively using the two types of information in social media. Motivated by psychological findings in physical world, I investigate whether sentiment analysis can help spammer detection in online social media. In particular, I conduct an exploratory study to analyze the sentiment differences between spammers and normal users; and present a novel method to incorporate sentiment information into social spammer detection framework. Given the rapidly evolving nature, I propose a novel framework to efficiently reflect the effect of newly emerging social spammers. To tackle the problem of lack of labeling data in social media, I study how to incorporate network information into text content modeling, and design strategies to select the most representative and informative instances from social media for labeling. Motivated by publicly available label information from other media platforms, I propose to make use of knowledge learned from cross-media to help spammer detection on social media.
ContributorsHu, Xia, Ph.D (Author) / Liu, Huan (Thesis advisor) / Kambhampati, Subbarao (Committee member) / Ye, Jieping (Committee member) / Faloutsos, Christos (Committee member) / Arizona State University (Publisher)
Created2015
Description
Users often join an online social networking (OSN) site, like Facebook, to remain social, by either staying connected with friends or expanding social networks. On an OSN site, users generally share variety of personal information which is often expected to be visible to their friends, but sometimes vulnerable to unwarranted access from others. The recent study suggests that many personal attributes, including religious and political affiliations, sexual orientation, relationship status, age, and gender, are predictable using users' personal data from an OSN site. The majority of users want to remain socially active, and protect their personal data at the same time. This tension leads to a user's vulnerability, allowing privacy attacks which can cause physical and emotional distress to a user, sometimes with dire consequences. For example, stalkers can make use of personal information available on an OSN site to their personal gain. This dissertation aims to systematically study a user vulnerability against such privacy attacks.
A user vulnerability can be managed in three steps: (1) identifying, (2) measuring and (3) reducing a user vulnerability. Researchers have long been identifying vulnerabilities arising from user's personal data, including user names, demographic attributes, lists of friends, wall posts and associated interactions, multimedia data such as photos, audios and videos, and tagging of friends. Hence, this research first proposes a way to measure and reduce a user vulnerability to protect such personal data. This dissertation also proposes an algorithm to minimize a user's vulnerability while maximizing their social utility values.
To address these vulnerability concerns, social networking sites like Facebook usually let their users to adjust their profile settings so as to make some of their data invisible. However, users sometimes interact with others using unprotected posts (e.g., posts from a ``Facebook page\footnote{The term ''Facebook page`` refers to the page which are commonly dedicated for businesses, brands and organizations to share their stories and connect with people.}''). Such interactions help users to become more social and are publicly accessible to everyone. Thus, visibilities of these interactions are beyond the control of their profile settings. I explore such unprotected interactions so that users' are well aware of these new vulnerabilities and adopt measures to mitigate them further. In particular, {\em are users' personal attributes predictable using only the unprotected interactions}? To answer this question, I address a novel problem of predictability of users' personal attributes with unprotected interactions. The extreme sparsity patterns in users' unprotected interactions pose a serious challenge. Therefore, I approach to mitigating the data sparsity challenge by designing a novel attribute prediction framework using only the unprotected interactions. Experimental results on Facebook dataset demonstrates that the proposed framework can predict users' personal attributes.
A user vulnerability can be managed in three steps: (1) identifying, (2) measuring and (3) reducing a user vulnerability. Researchers have long been identifying vulnerabilities arising from user's personal data, including user names, demographic attributes, lists of friends, wall posts and associated interactions, multimedia data such as photos, audios and videos, and tagging of friends. Hence, this research first proposes a way to measure and reduce a user vulnerability to protect such personal data. This dissertation also proposes an algorithm to minimize a user's vulnerability while maximizing their social utility values.
To address these vulnerability concerns, social networking sites like Facebook usually let their users to adjust their profile settings so as to make some of their data invisible. However, users sometimes interact with others using unprotected posts (e.g., posts from a ``Facebook page\footnote{The term ''Facebook page`` refers to the page which are commonly dedicated for businesses, brands and organizations to share their stories and connect with people.}''). Such interactions help users to become more social and are publicly accessible to everyone. Thus, visibilities of these interactions are beyond the control of their profile settings. I explore such unprotected interactions so that users' are well aware of these new vulnerabilities and adopt measures to mitigate them further. In particular, {\em are users' personal attributes predictable using only the unprotected interactions}? To answer this question, I address a novel problem of predictability of users' personal attributes with unprotected interactions. The extreme sparsity patterns in users' unprotected interactions pose a serious challenge. Therefore, I approach to mitigating the data sparsity challenge by designing a novel attribute prediction framework using only the unprotected interactions. Experimental results on Facebook dataset demonstrates that the proposed framework can predict users' personal attributes.
ContributorsGundecha, Pritam S (Author) / Liu, Huan (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Ye, Jieping (Committee member) / Barbier, Geoffrey (Committee member) / Arizona State University (Publisher)
Created2015
Description
Multi-label learning, which deals with data associated with multiple labels simultaneously, is ubiquitous in real-world applications. To overcome the curse of dimensionality in multi-label learning, in this thesis I study multi-label dimensionality reduction, which extracts a small number of features by removing the irrelevant, redundant, and noisy information while considering the correlation among different labels in multi-label learning. Specifically, I propose Hypergraph Spectral Learning (HSL) to perform dimensionality reduction for multi-label data by exploiting correlations among different labels using a hypergraph. The regularization effect on the classical dimensionality reduction algorithm known as Canonical Correlation Analysis (CCA) is elucidated in this thesis. The relationship between CCA and Orthonormalized Partial Least Squares (OPLS) is also investigated. To perform dimensionality reduction efficiently for large-scale problems, two efficient implementations are proposed for a class of dimensionality reduction algorithms, including canonical correlation analysis, orthonormalized partial least squares, linear discriminant analysis, and hypergraph spectral learning. The first approach is a direct least squares approach which allows the use of different regularization penalties, but is applicable under a certain assumption; the second one is a two-stage approach which can be applied in the regularization setting without any assumption. Furthermore, an online implementation for the same class of dimensionality reduction algorithms is proposed when the data comes sequentially. A Matlab toolbox for multi-label dimensionality reduction has been developed and released. The proposed algorithms have been applied successfully in the Drosophila gene expression pattern image annotation. The experimental results on some benchmark data sets in multi-label learning also demonstrate the effectiveness and efficiency of the proposed algorithms.
ContributorsSun, Liang (Author) / Ye, Jieping (Thesis advisor) / Li, Baoxin (Committee member) / Liu, Huan (Committee member) / Mittelmann, Hans D. (Committee member) / Arizona State University (Publisher)
Created2011
Description
Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. To validate these approaches in a disease-specific context, we built a schizophreniaspecific network based on the inferred associations and performed a comprehensive prioritization of human genes with respect to the disease. These results are expected to be validated empirically, but computational validation using known targets are very positive.
ContributorsLee, Jang (Author) / Gonzalez, Graciela (Thesis advisor) / Ye, Jieping (Committee member) / Davulcu, Hasan (Committee member) / Gallitano-Mendel, Amelia (Committee member) / Arizona State University (Publisher)
Created2011