575 research outputs found
Sparse integrative clustering of multiple omics data sets
High resolution microarrays and second-generation sequencing platforms are
powerful tools to investigate genome-wide alterations in DNA copy number,
methylation and gene expression associated with a disease. An integrated
genomic profiling approach measures multiple omics data types simultaneously in
the same set of biological samples. Such approach renders an integrated data
resolution that would not be available with any single data type. In this
study, we use penalized latent variable regression methods for joint modeling
of multiple omics data types to identify common latent variables that can be
used to cluster patient samples into biologically and clinically relevant
disease subtypes. We consider lasso [J. Roy. Statist. Soc. Ser. B 58 (1996)
267-288], elastic net [J. R. Stat. Soc. Ser. B Stat. Methodol. 67 (2005)
301-320] and fused lasso [J. R. Stat. Soc. Ser. B Stat. Methodol. 67 (2005)
91-108] methods to induce sparsity in the coefficient vectors, revealing
important genomic features that have significant contributions to the latent
variables. An iterative ridge regression is used to compute the sparse
coefficient vectors. In model selection, a uniform design [Monographs on
Statistics and Applied Probability (1994) Chapman & Hall] is used to seek
"experimental" points that scattered uniformly across the search domain for
efficient sampling of tuning parameter combinations. We compared our method to
sparse singular value decomposition (SVD) and penalized Gaussian mixture model
(GMM) using both real and simulated data sets. The proposed method is applied
to integrate genomic, epigenomic and transcriptomic data for subtype analysis
in breast and lung cancer data sets.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS578 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Identifying progressive imaging genetic patterns via multi-task sparse canonical correlation analysis: a longitudinal study of the ADNI cohort
Motivation
Identifying the genetic basis of the brain structure, function and disorder by using the imaging quantitative traits (QTs) as endophenotypes is an important task in brain science. Brain QTs often change over time while the disorder progresses and thus understanding how the genetic factors play roles on the progressive brain QT changes is of great importance and meaning. Most existing imaging genetics methods only analyze the baseline neuroimaging data, and thus those longitudinal imaging data across multiple time points containing important disease progression information are omitted.
Results
We propose a novel temporal imaging genetic model which performs the multi-task sparse canonical correlation analysis (T-MTSCCA). Our model uses longitudinal neuroimaging data to uncover that how single nucleotide polymorphisms (SNPs) play roles on affecting brain QTs over the time. Incorporating the relationship of the longitudinal imaging data and that within SNPs, T-MTSCCA could identify a trajectory of progressive imaging genetic patterns over the time. We propose an efficient algorithm to solve the problem and show its convergence. We evaluate T-MTSCCA on 408 subjects from the Alzheimer’s Disease Neuroimaging Initiative database with longitudinal magnetic resonance imaging data and genetic data available. The experimental results show that T-MTSCCA performs either better than or equally to the state-of-the-art methods. In particular, T-MTSCCA could identify higher canonical correlation coefficients and capture clearer canonical weight patterns. This suggests that T-MTSCCA identifies time-consistent and time-dependent SNPs and imaging QTs, which further help understand the genetic basis of the brain QT changes over the time during the disease progression.
Availability and implementation
The software and simulation data are publicly available at https://github.com/dulei323/TMTSCCA.
Supplementary information
Supplementary data are available at Bioinformatics online
A Scalable and Extensible Framework for Superposition-Structured Models
In many learning tasks, structural models usually lead to better
interpretability and higher generalization performance. In recent years,
however, the simple structural models such as lasso are frequently proved to be
insufficient. Accordingly, there has been a lot of work on
"superposition-structured" models where multiple structural constraints are
imposed. To efficiently solve these "superposition-structured" statistical
models, we develop a framework based on a proximal Newton-type method.
Employing the smoothed conic dual approach with the LBFGS updating formula, we
propose a scalable and extensible proximal quasi-Newton (SEP-QN) framework.
Empirical analysis on various datasets shows that our framework is potentially
powerful, and achieves super-linear convergence rate for optimizing some
popular "superposition-structured" statistical models such as the fused sparse
group lasso
Bayesian Modeling of Multiple Structural Connectivity Networks During the Progression of Alzheimer's Disease
Alzheimer's disease is the most common neurodegenerative disease. The aim of
this study is to infer structural changes in brain connectivity resulting from
disease progression using cortical thickness measurements from a cohort of
participants who were either healthy control, or with mild cognitive
impairment, or Alzheimer's disease patients. For this purpose, we develop a
novel approach for inference of multiple networks with related edge values
across groups. Specifically, we infer a Gaussian graphical model for each group
within a joint framework, where we rely on Bayesian hierarchical priors to link
the precision matrix entries across groups. Our proposal differs from existing
approaches in that it flexibly learns which groups have the most similar edge
values, and accounts for the strength of connection (rather than only edge
presence or absence) when sharing information across groups. Our results
identify key alterations in structural connectivity which may reflect
disruptions to the healthy brain, such as decreased connectivity within the
occipital lobe with increasing disease severity. We also illustrate the
proposed method through simulations, where we demonstrate its performance in
structure learning and precision matrix estimation with respect to alternative
approaches.Comment: Accepted to Biometrics January 202
Machine Learning for Multiclass Classification and Prediction of Alzheimer\u27s Disease
Alzheimer\u27s disease (AD) is an irreversible neurodegenerative disorder and a common form of dementia. This research aims to develop machine learning algorithms that diagnose and predict the progression of AD from multimodal heterogonous biomarkers with a focus placed on the early diagnosis. To meet this goal, several machine learning-based methods with their unique characteristics for feature extraction and automated classification, prediction, and visualization have been developed to discern subtle progression trends and predict the trajectory of disease progression.
The methodology envisioned aims to enhance both the multiclass classification accuracy and prediction outcomes by effectively modeling the interplay between the multimodal biomarkers, handle the missing data challenge, and adequately extract all the relevant features that will be fed into the machine learning framework, all in order to understand the subtle changes that happen in the different stages of the disease. This research will also investigate the notion of multitasking to discover how the two processes of multiclass classification and prediction relate to one another in terms of the features they share and whether they could learn from one another for optimizing multiclass classification and prediction accuracy.
This research work also delves into predicting cognitive scores of specific tests over time, using multimodal longitudinal data. The intent is to augment our prospects for analyzing the interplay between the different multimodal features used in the input space to the predicted cognitive scores. Moreover, the power of modality fusion, kernelization, and tensorization have also been investigated to efficiently extract important features hidden in the lower-dimensional feature space without being distracted by those deemed as irrelevant.
With the adage that a picture is worth a thousand words, this dissertation introduces a unique color-coded visualization system with a fully integrated machine learning model for the enhanced diagnosis and prognosis of Alzheimer\u27s disease. The incentive here is to show that through visualization, the challenges imposed by both the variability and interrelatedness of the multimodal features could be overcome. Ultimately, this form of visualization via machine learning informs on the challenges faced with multiclass classification and adds insight into the decision-making process for a diagnosis and prognosis
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