114 research outputs found
Optimization algorithms for inference and classification of genetic profiles from undersampled measurements
In this thesis, we tackle three different problems, all related to optimization techniques for inference and classification of genetic profiles. First, we extend the deterministic Non-negative Matrix Factorization (NMF) framework to the probabilistic case (PNMF). We apply the PNMF algorithm to cluster and classify DNA microarrays data. The proposed PNMF is shown to outperform the deterministic NMF and the sparse NMF algorithms in clustering stability and classification accuracy. Second, we propose SMURC: Small-sample MUltivariate Regression with Covariance estimation. Specifically, we consider a high dimension low sample-size multivariate regression problem that accounts for correlation of the response variables. We show that, in this case, the maximum likelihood approach is senseless because the likelihood diverges. We propose a normalization of the likelihood function that guarantees convergence. Simulation results show that SMURC outperforms the regularized likelihood estimator with known covariance matrix and the state-of-the-art sparse Conditional Graphical Gaussian Model (sCGGM). In the third Chapter, we derive a new greedy algorithm that provides an exact sparse solution of the combinatorial l sub zero-optimization problem in an exponentially less computation time. Unlike other greedy approaches, which are only approximations of the exact sparse solution, the proposed greedy approach, called Kernel reconstruction, leads to the exact optimal solution
Graph-based Regularization in Machine Learning: Discovering Driver Modules in Biological Networks
Curiosity of human nature drives us to explore the origins of what makes each of us different. From ancient legends and mythology, Mendel\u27s law, Punnett square to modern genetic research, we carry on this old but eternal question. Thanks to technological revolution, today\u27s scientists try to answer this question using easily measurable gene expression and other profiling data. However, the exploration can easily get lost in the data of growing volume, dimension, noise and complexity. This dissertation is aimed at developing new machine learning methods that take data from different classes as input, augment them with knowledge of feature relationships, and train classification models that serve two goals: 1) class prediction for previously unseen samples; 2) knowledge discovery of the underlying causes of class differences. Application of our methods in genetic studies can help scientist take advantage of existing biological networks, generate diagnosis with higher accuracy, and discover the driver networks behind the differences. We proposed three new graph-based regularization algorithms. Graph Connectivity Constrained AdaBoost algorithm combines a connectivity module, a deletion function, and a model retraining procedure with the AdaBoost classifier. Graph-regularized Linear Programming Support Vector Machine integrates penalty term based on submodular graph cut function into linear classifier\u27s objective function. Proximal Graph LogisticBoost adds lasso and graph-based penalties into logistic risk function of an ensemble classifier. Results of tests of our models on simulated biological datasets show that the proposed methods are able to produce accurate, sparse classifiers, and can help discover true genetic differences between phenotypes
Pre-processing of tandem mass spectra using machine learning methods
Protein identification has been more helpful than before in the diagnosis and treatment of many diseases, such as cancer, heart disease and HIV. Tandem mass spectrometry is a powerful tool for protein identification. In a typical experiment, proteins are broken into small amino acid oligomers called peptides. By determining the amino acid sequence of several peptides of a protein, its whole amino acid sequence can be inferred. Therefore, peptide identification is the first step and a central issue for protein identification. Tandem mass spectrometers can produce a large number of tandem mass spectra which are used for peptide identification. Two issues should be addressed to improve the performance of current peptide identification algorithms. Firstly, nearly all spectra are noise-contaminated. As a result, the accuracy of peptide identification algorithms may suffer from the noise in spectra. Secondly, the majority of spectra are not identifiable because they are of too poor quality. Therefore, much time is wasted attempting to identify these unidentifiable spectra.
The goal of this research is to design spectrum pre-processing algorithms to both speedup and improve the reliability of peptide identification from tandem mass spectra. Firstly, as a tandem mass spectrum is a one dimensional signal consisting of dozens to hundreds of peaks, and majority of peaks are noisy peaks, a spectrum denoising algorithm is proposed to remove most noisy peaks of spectra. Experimental results show that our denoising algorithm can remove about 69% of peaks which are potential noisy peaks among a spectrum. At the same time, the number of spectra that can be identified by Mascot algorithm increases by 31% and 14% for two tandem mass spectrum datasets. Next, a two-stage recursive feature elimination based on support vector machines (SVM-RFE) and a sparse logistic regression method are proposed to select the most relevant features to describe the quality of tandem mass spectra. Our methods can effectively select the most relevant features in terms of performance of classifiers trained with the different number of features. Thirdly, both supervised and unsupervised machine learning methods are used for the quality assessment of tandem mass spectra. A supervised classifier, (a support vector machine) can be trained to remove more than 90% of poor quality spectra without removing more than 10% of high quality spectra. Clustering methods such as model-based clustering are also used for quality assessment to cancel the need for a labeled training dataset and show promising results
Feature selection and modelling methods for microarray data from acute coronary syndrome
Acute coronary syndrome (ACS) represents a leading cause of mortality and morbidity worldwide. Providing better diagnostic solutions and developing therapeutic strategies customized to the individual patient represent societal and economical urgencies. Progressive improvement in diagnosis and treatment procedures require a thorough understanding of the underlying genetic mechanisms of the disease. Recent advances in microarray technologies together with the decreasing costs of the specialized equipment enabled affordable harvesting of time-course gene expression data. The high-dimensional data generated demands for computational tools able to extract the underlying biological knowledge.
This thesis is concerned with developing new methods for analysing time-course gene expression data, focused on identifying differentially expressed genes, deconvolving heterogeneous gene expression measurements and inferring
dynamic gene regulatory interactions. The main contributions include: a novel multi-stage feature selection method, a new deconvolution approach for estimating cell-type specific signatures and quantifying the contribution of each cell type to the variance of the gene expression patters, a novel approach to identify the
cellular sources of differential gene expression, a new approach to model gene expression dynamics using sums of exponentials and a novel method to estimate stable linear dynamical systems from noisy and unequally spaced time series data.
The performance of the proposed methods was demonstrated on a time-course dataset consisting of microarray gene expression levels collected from the blood samples of patients with ACS and associated blood count measurements. The results of the feature selection study are of significant biological relevance. For the
first time is was reported high diagnostic performance of the ACS subtypes up to three months after hospital admission. The deconvolution study exposed features
of within and between groups variation in expression measurements and identified potential cell type markers and cellular sources of differential gene expression.
It was shown that the dynamics of post-admission gene expression data can be accurately modelled using sums of exponentials, suggesting that gene expression
levels undergo a transient response to the ACS events before returning to equilibrium. The linear dynamical models capturing the gene regulatory interactions
exhibit high predictive performance and can serve as platforms for system-level analysis, numerical simulations and intervention studies
A Survey on Metric Learning for Feature Vectors and Structured Data
The need for appropriate ways to measure the distance or similarity between
data is ubiquitous in machine learning, pattern recognition and data mining,
but handcrafting such good metrics for specific problems is generally
difficult. This has led to the emergence of metric learning, which aims at
automatically learning a metric from data and has attracted a lot of interest
in machine learning and related fields for the past ten years. This survey
paper proposes a systematic review of the metric learning literature,
highlighting the pros and cons of each approach. We pay particular attention to
Mahalanobis distance metric learning, a well-studied and successful framework,
but additionally present a wide range of methods that have recently emerged as
powerful alternatives, including nonlinear metric learning, similarity learning
and local metric learning. Recent trends and extensions, such as
semi-supervised metric learning, metric learning for histogram data and the
derivation of generalization guarantees, are also covered. Finally, this survey
addresses metric learning for structured data, in particular edit distance
learning, and attempts to give an overview of the remaining challenges in
metric learning for the years to come.Comment: Technical report, 59 pages. Changes in v2: fixed typos and improved
presentation. Changes in v3: fixed typos. Changes in v4: fixed typos and new
method
Robust Algorithms for Detecting Hidden Structure in Biological Data
Biological data, such as molecular abundance measurements and protein
sequences, harbor complex hidden structure that reflects its underlying
biological mechanisms. For example, high-throughput abundance measurements
provide a snapshot the global state of a living cell, while homologous
protein sequences encode the residue-level logic of the proteins\u27 function
and provide a snapshot of the evolutionary trajectory of the protein family.
In this work I describe algorithmic approaches and analysis software I
developed for uncovering hidden structure in both kinds of data.
Clustering is an unsurpervised machine learning technique commonly used
to map the structure of data collected in high-throughput experiments,
such as quantification of gene expression by DNA microarrays or
short-read sequencing. Clustering algorithms always yield a partitioning
of the data, but relying on a single partitioning solution can lead to
spurious conclusions. In particular, noise in the data can cause objects
to fall into the same cluster by chance rather than due to meaningful
association. In the first part of this thesis I demonstrate approaches to
clustering data robustly in the presence of noise and apply robust clustering
to analyze the transcriptional response to injury in a neuron cell.
In the second part of this thesis I describe identifying hidden specificity
determining residues (SDPs) from alignments of protein sequences descended
through gene duplication from a common ancestor (paralogs) and apply the
approach to identify numerous putative SDPs in bacterial transcription
factors in the LacI family. Finally, I describe and demonstrate a new
algorithm for reconstructing the history of duplications by which paralogs
descended from their common ancestor. This algorithm addresses the
complexity of such reconstruction due to indeterminate or erroneous
homology assignments made by sequence alignment algorithms and to the
vast prevalence of divergence through speciation over divergence through
gene duplication in protein evolution
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