Olcay Kursun

Collection and Analysis of a Parkinson Speech Dataset With Multiple Types of Sound Recordings

There has been an increased interest in speech pattern analysis applications of Parkinsonism for building predictive telediagnosis and telemonitoring models. For this purpose, we have collected a wide variety of voice samples, including sustained vowels, words, and sentences compiled from a set of speaking exercises for people with Parkinsons disease. There are two main issues in learning from such a dataset that consists of multiple speech recordings per subject: 1) How predictive these various types, e.g., sustained vowels versus words, of voice samples are in Parkinsons disease (PD) diagnosis? 2) How well the central tendency and dispersion metrics serve as representatives of all sample recordings of a subject? In this paper, investigating our Parkinson dataset using well-known machine learning tools, as reported in the literature, sustained vowels are found to carry more PD-discriminative information. We have also found that rather than using each voice recording of each subject as an independent data sample, representing the samples of a subject with central tendency and dispersion metrics improves generalization of the predictive model.

Telediagnosis of Parkinson’s Disease Using Measurements of Dysphonia

Parkinson’s disease (PD) is a neurological illness which impairs motor skills, speech, and other functions such as mood, behavior, thinking, and sensation. It causes vocal impairment for approximately 90% of the patients. As the symptoms of PD occur gradually and mostly targeting the elderly people for whom physical visits to the clinic are inconvenient and costly, telemonitoring of the disease using measurements of dysphonia (vocal features) has a vital role in its early diagnosis. Such dysphonia features extracted from the voice come in variety and most of them are interrelated. The purpose of this study is twofold: (1) to select a minimal subset of features with maximal joint relevance to the PD-score, a binary score indicating whether or not the sample belongs to a person with PD; and (2) to build a predictive model with minimal bias (i.e. to maximize the generalization of the predictions so as to perform well with unseen test examples). For these tasks, we apply the mutual information measure with the permutation test for assessing the relevance and the statistical significance of the relations between the features and the PD-score, rank the features according to the maximum-relevance-minimum-redundancy (mRMR) criterion, use a Support Vector Machine (SVM) for building a classification model and test it with a more suitable cross-validation scheme that we called leave-one-individual-out that fits with the dataset in hand better than the conventional bootstrapping or leave-one-out validation methods.

Single-molecule correlated chemical probing of RNA

Significance RNA molecules function as the central conduit of information transfer in biology. To do this, they encode information both in their sequences and in their higher-order structures. Understanding the higher-order structure of RNA remains challenging. In this work we devise a simple, experimentally concise, and accurate approach for examining higher-order RNA structure by converting widely used massively parallel sequencing into an easily implemented single-molecule experiment for detecting through-space interactions and multiple conformations. We then use this experiment to analyze higher-order RNA structure, detect biologically important hidden states, and refine accurate three-dimensional structure models. Complex higher-order RNA structures play critical roles in all facets of gene expression; however, the through-space interaction networks that define tertiary structures and govern sampling of multiple conformations are poorly understood. Here we describe single-molecule RNA structure analysis in which multiple sites of chemical modification are identified in single RNA strands by massively parallel sequencing and then analyzed for correlated and clustered interactions. The strategy thus identifies RNA interaction groups by mutational profiling (RING-MaP) and makes possible two expansive applications. First, we identify through-space interactions, create 3D models for RNAs spanning 80–265 nucleotides, and characterize broad classes of intramolecular interactions that stabilize RNA. Second, we distinguish distinct conformations in solution ensembles and reveal previously undetected hidden states and large-scale structural reconfigurations that occur in unfolded RNAs relative to native states. RING-MaP single-molecule nucleic acid structure interrogation enables concise and facile analysis of the global architectures and multiple conformations that govern function in RNA.

A feature selection method based on kernel canonical correlation analysis and the minimum Redundancy-Maximum Relevance filter method

Highlights? We propose a feature selection method based on a recently popular mRMR criterion, which we called KCCAmRMR. ? Our method is based on finding the unique information that a candidate variable possesses about the target variable. ? We propose using correlated functions explored by KCCA instead of using the features themselves as inputs to mRMR. In this paper, we propose a feature selection method based on a recently popular minimum Redundancy-Maximum Relevance (mRMR) criterion, which we called Kernel Canonical Correlation Analysis basedmRMR (KCCAmRMR) based on the idea of finding the unique information, i.e. information that is distinct from the set of already selected variables, that a candidate variable possesses about the target variable. In simplest terms, for this purpose, we propose using correlated functions explored by KCCA instead of using the features themselves as inputs to mRMR. We demonstrate the usefulness of our method on both toy and benchmark datasets.

Canonical correlation analysis using within-class coupling

Fishers linear discriminant analysis (LDA) is one of the most popular supervised linear dimensionality reduction methods. Unfortunately, LDA is not suitable for problems where the class labels are not available and only the spatial or temporal association of data samples is implicitly indicative of class membership. In this study, a new strategy for reducing LDA to Hotellings canonical correlation analysis (CCA) is proposed. CCA seeks prominently correlated projections between two views of data and it has been long known to be equivalent to LDA when the data features are used in one view and the class labels are used in the other view. The basic idea of the new equivalence between LDA and CCA, which we call within-class coupling CCA (WCCCA), is to apply CCA to pairs of data samples that are most likely to belong to the same class. We prove the equivalence between LDA and such an application of CCA. With such an implicit representation of the class labels, WCCCA is applicable both to regular LDA problems and to problems in which only spatial and/or temporal continuity provides clues to the class labels.

Application of canonical correlation analysis for identifying viral integration preferences

MOTIVATION Gene therapy aims at using viral vectors for attaching helpful genetic code to target genes. Therefore, it is of great importance to develop methods that can discover significant patterns around viral integration sites. Canonical correlation analysis is an unsupervised statistical tool that is used to describe the relations between two related views of the same semantic object, which fits well for identifying such salient patterns. RESULTS Proposed method is demonstrated on a sequence dataset obtained from a study on HIV-1 preferred integration regions. The subsequences on the left and right sides of the integration points are given to the method as the two views, and statistically significant relations are found between sequence-driven features derived from these two views, which suggest that the viral preference must be the factor responsible for this correlation. We found that there are significant correlations at x=5 indicating a palindromic behavior surrounding the viral integration site, which complies with the previously reported results. AVAILABILITY Developed software tool is available at http://ce.istanbul.edu.tr/bioinformatics/hiv1/.

A method for combining mutual information and canonical correlation analysis

Highlights? We propose a hybrid measure of relevance based on MI and KCCA. ? Our measure PMI weighs more the samples with predictive powers. ? PMI effectively eliminates the samples with no predictive contribution. ? We show that PMI has improved feature detection capability. Feature selection is a critical step in many artificial intelligence and pattern recognition problems. Shannons Mutual Information (MI) is a classical and widely used measure of dependence measure that serves as a good feature selection algorithm. However, as it is a measure of mutual information in average, under-sampled classes (rare events) can be overlooked by this measure, which can cause critical false negatives (missing a relevant feature very predictive of some rare but important classes). Shannons mutual information requires a well sampled database, which is not typical of many fields of modern science (such as biomedical), in which there are limited number of samples to learn from, or at least, not all the classes of the target function (such as certain phenotypes in biomedical) are well-sampled. On the other hand, Kernel Canonical Correlation Analysis (KCCA) is a nonlinear correlation measure effectively used to detect independence but its use for feature selection or ranking is limited due to the fact that its formulation is not intended to measure the amount of information (entropy) of the dependence. In this paper, we propose a hybrid measure of relevance, Predictive Mutual Information (PMI) based on MI, which also accounts for predictability of signals from each other in its calculation as in KCCA. We show that PMI has more improved feature detection capability than MI, especially in catching suspicious coincidences that are rare but potentially important not only for experimental studies but also for building computational models. We demonstrate the usefulness of PMI, and superiority over MI, on both toy and real datasets.

A genome-wide analysis of lentivector integration sites using targeted sequence capture and next generation sequencing technology

One application of next-generation sequencing (NGS) is the targeted resequencing of interested genes which has not been used in viral integration site analysis of gene therapy applications. Here, we combined targeted sequence capture array and next generation sequencing to address the whole genome profiling of viral integration sites. Human 293T and K562 cells were transduced with a HIV-1 derived vector. A custom made DNA probe sets targeted pLVTHM vector used to capture lentiviral vector/human genome junctions. The captured DNA was sequenced using GS FLX platform. Seven thousand four hundred and eighty four human genome sequences flanking the long terminal repeats (LTR) of pLVTHM fragment sequences matched with an identity of at least 98% and minimum 50 bp criteria in both cells. In total, 203 unique integration sites were identified. The integrations in both cell lines were totally distant from the CpG islands and from the transcription start sites and preferentially located in introns. A comparison between the two cell lines showed that the lentiviral-transduced DNA does not have the same preferred regions in the two different cell lines.

Online naive bayes classification for network intrusion detection

Intrusion detection system (IDS) is an important component to ensure network security. In this paper we build an online Naïve Bayes classifier to discriminate normal and bad (intrusion) connections on KDD 99 dataset for network intrusion detection. The classifier starts with a small number of training examples of normal and bad classes; then, as it classifies the rest of the samples one at a time, it continuously updates the mean and the standard deviations of the features (IDS variables). We present experimental results of parameter updating methods and their parameters for the online Naïve Bayes classifier. The obtained results show that our proposed method performs comparably to the simple incremental update.

Ensemble canonical correlation analysis

Canonical Correlation Analysis (CCA) aims at identifying linear dependencies between two different but related multivariate views of the same underlying semantics. Ignoring its various extensions to more than two views, CCA uses these two views as complex labels to guide the search of maximally correlated projection vectors (covariates). Therefore, CCA can overfit the training data, meaning that different correlated projections can be found when the two-view training dataset is resampled. Although, to avoid such overfitting, ensemble approaches that utilize resampling techniques have been effectively used for improving generalization of many machine learning methods, an ensemble approach has not yet been formulated for CCA. In this paper, we propose an ensemble method for obtaining a final set of covariates by combining multiple sets of covariates extracted from subsamples. In comparison to those obtained by the application of the classical CCA on the whole set of training data, combining covariates with weaker correlations extracted from a number of subsamples of the training data produces stronger correlations that generalize to unseen test examples. Experimental results on emotion recognition, digit recognition, content-based retrieval, and multiple view object recognition have shown that ensemble CCA has better generalization for both the test set correlations of the covariates and the test set accuracy of classification performed on these covariates.