Jan Kalina

DNA conformations and their sequence preferences

The geometry of the phosphodiester backbone was analyzed for 7739 dinucleotides from 447 selected crystal structures of naked and complexed DNA. Ten torsion angles of a near-dinucleotide unit have been studied by combining Fourier averaging and clustering. Besides the known variants of the A-, B- and Z-DNA forms, we have also identified combined A + B backbone-deformed conformers, e.g. with α/γ switches, and a few conformers with a syn orientation of bases occurring e.g. in G-quadruplex structures. A plethora of A- and B-like conformers show a close relationship between the A- and B-form double helices. A comparison of the populations of the conformers occurring in naked and complexed DNA has revealed a significant broadening of the DNA conformational space in the complexes, but the conformers still remain within the limits defined by the A- and B- forms. Possible sequence preferences, important for sequence-dependent recognition, have been assessed for the main A and B conformers by means of statistical goodness-of-fit tests. The structural properties of the backbone in quadruplexes, junctions and histone-core particles are discussed in further detail.

Implicitly Weighted Methods in Robust Image Analysis

This paper is devoted to highly robust statistical methods with applications to image analysis. The methods of the paper exploit the idea of implicit weighting, which is inspired by the highly robust least weighted squares regression estimator. We use a correlation coefficient based on implicit weighting of individual pixels as a highly robust similarity measure between two images. The reweighted least weighted squares estimator is considered as an alternative regression estimator with a clear interpretation. We apply implicit weighting to dimension reduction by means of robust principal component analysis. Highly robust methods are exploited in tasks of face localization and face detection in a database of 2D images. In this context we investigate a method for outlier detection and a filter for image denoising based on implicit weighting.

A Robust Supervised Variable Selection for Noisy High-Dimensional Data

The Minimum Redundancy Maximum Relevance (MRMR) approach to supervised variable selection represents a successful methodology for dimensionality reduction, which is suitable for high-dimensional data observed in two or more different groups. Various available versions of the MRMR approach have been designed to search for variables with the largest relevance for a classification task while controlling for redundancy of the selected set of variables. However, usual relevance and redundancy criteria have the disadvantages of being too sensitive to the presence of outlying measurements and/or being inefficient. We propose a novel approach called Minimum Regularized Redundancy Maximum Robust Relevance (MRRMRR), suitable for noisy high-dimensional data observed in two groups. It combines principles of regularization and robust statistics. Particularly, redundancy is measured by a new regularized version of the coefficient of multiple correlation and relevance is measured by a highly robust correlation coefficient based on the least weighted squares regression with data-adaptive weights. We compare various dimensionality reduction methods on three real data sets. To investigate the influence of noise or outliers on the data, we perform the computations also for data artificially contaminated by severe noise of various forms. The experimental results confirm the robustness of the method with respect to outliers.

Highly robust analysis of keystroke dynamics measurements

Standard classification procedures of both data mining and multivariate statistics are sensitive to the presence of outlying values. In this paper, we propose new algorithms for computing regularized versions of linear discriminant analysis for data with small sample sizes in each group. Further, we propose a highly robust version of a regularized linear discriminant analysis. The new method denoted as MWCD-L2-LDA is based on the idea of implicit weights assigned to individual observations, inspired by the minimum weighted covariance determinant estimator. Classification performance of the new method is illustrated on a detailed analysis of our pilot study of authentication methods on computers, using individual typing characteristics by means of keystroke dynamics.

Robust Image Analysis of BeadChip Microarrays

Microarray images in molecular genetics are heavily contaminated by noise and outlying measurements. This paper is devoted to analysis of Illumina BeadChip microarray images, primarily to their low-level preprocessing. We point out that standard image analysis procedures, which are implemented in the beadarray package of BioConductor software, are highly sensitive to contamination by severe noise and outliers. Therefore, the habitually used methodology does not discover many of the outliers. We illustrate this on real data and show that the standard background correction method may actually amplify the noise in the image. A robust image analysis tailor-made for this type of microarray images is highly desirable. We explain principles and show preliminary results of our robust alternative to the standard approach, which aims to be robust to noise and outliers in each its step.

Highly Robust Classification: A Regularized Approach for Omics Data

Various regularized approaches to linear discriminant analysis suffer from sensitivity to the presence of outlying measurements in the data. This work has the aim to propose new versions of regularized linear discriminant analysis suitable for high-dimensional data contaminated by outliers. We use principles of robust statistics to propose classification methods suitable for data with the number of variables exceeding the number of observations. Particularly, we propose two robust regularized versions of linear discriminant analysis, which have a high breakdown point. For this purpose, we propose a regularized version of the minimum weighted covariance determinant estimator, which is one of highly robust estimators of multivariate location and scatter. It assigns implicit weights to individual observations and represents a unique attempt to combine regularization and high robustness. Algorithms for the efficient computation of the new classification methods are proposed and the performance of these methods is illustrated on real data sets.

Perspectives of Information-based Methods in Medicine: An Outlook for Mental Health Care

Information-based medicine represents a concept characterizing the future ideal of medical practice overcoming the limitations of the popular concept of evidence-based medicine. The potential of information-based medicine is catalyzed by recent development of new technologies and basic research allowing to acquire a new medical knowledge relevant for an individual patient. The paper is focused on the specialty field of psychiatry. We discuss the challenges for the development of information-based psychiatry from the point of view of medical informatics together with its specific barriers and constraints. We discuss the development of telemedicine tools for psychiatric care, so far making mainly a disappointing experience. Medical informatics will also play the role in making results of basic research available to the psychiatrist at the point of care. Research results e.g. in molecular genetics or cognitive neuroscience will require to collect and analyze massive data on an individual patient. If these data are properly combined from various sources and analyzed, they represent an enormous potential for bringing a new psychiatric knowledge closer to an individual patient. This may contribute to improving the availability of psychiatric care and bringing its desirable destigmatization and humanization.

Decision Support for Mental Health: Towards the Information-based Psychiatry

Decision support systems represent an important tool offering assistance with the decision making process in a variety of applications. This paper starts with recalling the basic principles and structure of decision support systems in medicine from a general perspective. Their effect in terms of both potential and limitations for finding the diagnosis, prognosis and therapy are overviewed from the points of view of health care effectiveness and patient safety. The authors are particularly interested in the specialty field of psychiatry. They discuss its specific challenges and analyze the slower penetration of telemedicine tools to psychiatry compared to other clinical fields. Finally, they claim that the development of decision support systems play a key role in the development of the concept of information-based medicine in general as well as to the particular context of information-based psychiatry.