Papers by andrzej polanski
The method of gait identification based on the nearest neighbor classification technique with mot... more The method of gait identification based on the nearest neighbor classification technique with motion similarity assessment by the dynamic time warping is proposed. The model based kinematic motion data, represented by the joints rotations coded by Euler angles and unit quaternions is used. The different pose distance functions in Euler angles and quaternion spaces are considered. To evaluate individual features of the subsequent joints movements during gait cycle, joint selection is carried out. To examine proposed approach database containing 353 gaits of 25 humans collected in motion capture laboratory is used. The obtained results are promising. The classifications, which takes into consideration all joints has accuracy over 91%. Only analysis of movements of hip joints allows to correctly identify gaits with almost 80% precision.
Structured Bi-clusters Algorithm for Classification of DNA Microarray Data
Advances in Intelligent Systems and Computing, 2016
We propose a new algorithm for classification of transcriptomic data based on the two stage proce... more We propose a new algorithm for classification of transcriptomic data based on the two stage procedure of feature selection. The construction of the new feature set is based on the hypothesis that in many transcriptomic datasets there is an additional hidden structure dictated by some biological factors, which were not taken into account in the design of the experiment. The hidden structure in the data is detected by using the specialized version of the bi-clustering methodology, called the method of structured bi-clusters. The idea of the new method is that the constructed bi-clusters must coincide along one dimension with the existing data classes. The second dimension of the bi-clusters is a free design parameter of our algorithm. Combining the proposed the feature selection procedure with the nearest neighbour classification rule leads to improvements of classification accuracy for real transcriptomic data.
Analysis of Semestral Progress in Higher Technical Education with HMM Models
Computational Science – ICCS 2021, 2021
Przegląd Elektrotechniczny, 2012
The analysis of effectiveness of deep brain stimulation and pharmacological treatment in Parkinso... more The analysis of effectiveness of deep brain stimulation and pharmacological treatment in Parkinson disease is presented. It is based on an examination of discriminative properties of distinctive motion features. The feature extraction and selection of kinematical motion data is carried out. The attribute ranking with entropy based attribute evaluation and greedy hill climbing search with assessment of an average inner class dissimilarity are applied. The obtained results show that deep brain stimulation has greater impact on investigated motion activities.
Wybrane zagadnienia stabilności układów liniowych o zmiennych w czasie parametrach
Bioinformatics, 2018
Motivation: In contemporary biological experiments, bias, which interferes with the measurements,... more Motivation: In contemporary biological experiments, bias, which interferes with the measurements, requires attentive processing. Important sources of bias in high-throughput biological experiments are batch effects and diverse methods towards removal of batch effects have been established. These include various normalization techniques, yet many require knowledge on the number of batches and assignment of samples to batches. Only few can deal with the problem of identification of batch effect of unknown structure. For this reason, an original batch identification algorithm through dynamical programming is introduced for omics data that may be sorted on a timescale. Results: BatchI algorithm is based on partitioning a series of high-throughput experiment samples into sub-series corresponding to estimated batches. The dynamic programming method is used for splitting data with maximal dispersion between batches, while maintaining minimal within batch dispersion. The procedure has been tested on a number of available datasets with and without prior information about batch partitioning. Datasets with a priori identified batches have been split accordingly, measured with weighted average Dice Index. Batch effect correction is justified by higher intra-group correlation. In the blank datasets, identified batch divisions lead to improvement of parameters and quality of biological information, shown by literature study and Information Content. The outcome of the algorithm serves as a starting point for correction methods. It has been demonstrated that omitting the essential step of batch effect control may lead to waste of valuable potential discoveries.
Sensors, 2017
The paper describes a scalable, wearable multi-sensor system for motion capture based on inertial... more The paper describes a scalable, wearable multi-sensor system for motion capture based on inertial measurement units (IMUs). Such a unit is composed of accelerometer, gyroscope and magnetometer. The final quality of an obtained motion arises from all the individual parts of the described system. The proposed system is a sequence of the following stages: sensor data acquisition, sensor orientation estimation, system calibration, pose estimation and data visualisation. The construction of the system's architecture with the dataflow programming paradigm makes it easy to add, remove and replace the data processing steps. The modular architecture of the system allows an effortless introduction of a new sensor orientation estimation algorithms. The original contribution of the paper is the design study of the individual components used in the motion capture system. The two key steps of the system design are explored in this paper: the evaluation of sensors and algorithms for the orientation estimation. The three chosen algorithms have been implemented and investigated as part of the experiment. Due to the fact that the selection of the sensor has a significant impact on the final result, the sensor evaluation process is also explained and tested. The experimental results confirmed that the choice of sensor and orientation estimation algorithm affect the quality of the final results.
Automatic PDF Files Based Information Retrieval System with Section Selection and Key Terms Aggregation Rules
Advances in Intelligent Systems and Computing, 2015
Standard approaches to knowledge extraction from biomedical literature focus on information retri... more Standard approaches to knowledge extraction from biomedical literature focus on information retrieval from abstracts publicly available in medical databases like PubMed. To limit the number of the results initially, a suitable query against such databases can be constructed. However, for many research topics the pre-selection of small enough set of the documents can be very difficult or even impossible. Another problem stems from large variability of the retrieved lists of publications when changing keywords in search engines. In this paper we address both of these problems by proposing an algorithm and an implementation capable of working on the full text articles. We present an information retrieval system with selection of separate sections of full texts of papers and a rule-based search engine. We demonstrate that in some research our solution can provide much better results than finding documents only by keywords and abstracts.
The paper presents method of motion analysis supporting diagnosis of gait abnormalities on the ba... more The paper presents method of motion analysis supporting diagnosis of gait abnormalities on the basis of reduced kinematical data of a gait. The proposed method consist of the following steps: kinematical data reduction by Principal Component Analysis, determination of the Fourier component for the 3D PCA trajectories and supervised learning. To examine proposed approach, we have collected database of gaits containing data of coxarthrosis patients. We have got 100% of classification accuracy for the considered disease. Streszczenie. W artykule zaprezentowano metodę analizy danych ruchu dla celów wspierania diagnostyki nieprawidłowości chodu. W kolejnych krokach przeprowadzana jest redukcja wymiarowości kinematycznych danych chodu z wykorzystaniem metody analizy składowych głównych, wyznaczane są składowe Fouriera dla otrzymanych trójwymiarowych trajektorii PCA oraz przeprowadzane jest uczenie nadzorowane. W celu weryfikacji zaproponowanej metody zgromadzono bazę danych przejść ze schorzeniami stawu biodrowego, dla której to udało się uzyskać 100% skuteczność klasyfikacji.. (Diagnostyka patologii ruchu na podstawie zredukowanych danych kinematycznych).
Procedia Computer Science, 2015
Exploration of tissue sections by imaging mass spectrometry reveals abundance of different biomol... more Exploration of tissue sections by imaging mass spectrometry reveals abundance of different biomolecular ions in different sample spots, allowing finding region specific features. In this paper we present computational and statistical methods for investigation of protein biomarkers i.e. biological features related to presence of different pathological states. Proposed complete processing pipeline includes data pre-processing, detection and quantification of peaks by using Gaussian mixture modeling and identification of specific features for different tissue regions by performing permutation tests. Application of created methodology provides detection of proteins/peptides with concentration levels specific for tumor area, normal epithelium, muscle or saliva gland regions with high confidence.
Estimating regions of absolute stability with the use of piecewise linear Lyapunov functions
European Journal of Control, 2004
In the paper we study application of piecewise linear Lyapunov functions, defined by triangulatio... more In the paper we study application of piecewise linear Lyapunov functions, defined by triangulation of the state space, to problems of verifying local stability and estimating stability region of non-linear, time-varying systems. We compare results obtained by two methods: (i) piecewise linear Lyapunov function defined on rectangular or polar grid, and (ii) quadratic Lyapunov function.
Establishment of a Radiogenomics Consortium
International Journal of Radiation Oncology*Biology*Physics, 2010
1. Int J Radiat Oncol Biol Phys. 2010 Apr;76(5):1295-6. ... West C, Rosenstein BS, Alsner J, Azri... more 1. Int J Radiat Oncol Biol Phys. 2010 Apr;76(5):1295-6. ... West C, Rosenstein BS, Alsner J, Azria D, Barnett G, Begg A, Bentzen S, Burnet N, Chang-Claude J, Chuang E, Coles C, De Ruyck K, De Ruysscher D, Dunning A, Elliott R, Fachal L, Hall J, Haustermans K, Herskind C, ...
PLOS ONE, 2015
Mixture-modeling of mass spectra is an approach with many potential applications including peak d... more Mixture-modeling of mass spectra is an approach with many potential applications including peak detection and quantification, smoothing, denoising, feature extraction and spectral signal compression. However, existing algorithms do not allow for automatic analyses of whole spectra. Therefore, despite highlighting potential advantages of mixture modeling of mass spectra of peptide/protein mixtures and some preliminary results presented in several papers, the mixture modeling approach was so far not developed to the stage enabling systematic comparisons with existing software packages for proteomic mass spectra analyses. In this paper we present an efficient algorithm for Gaussian mixture modeling of proteomic mass spectra of different types (e.g., MALDI-ToF profiling, MALDI-IMS). The main idea is automatic partitioning of protein mass spectral signal into fragments. The obtained fragments are separately decomposed into Gaussian mixture models. The parameters of the mixture models of fragments are then aggregated to form the mixture model of the whole spectrum. We compare the elaborated algorithm to existing algorithms for peak detection and we demonstrate improvements of peak detection efficiency obtained by using Gaussian mixture modeling. We also show applications of the elaborated algorithm to real proteomic datasets of low and high resolution.
RESEARCH ARTICLE Signal Partitioning Algorithm for Highly Efficient Gaussian Mixture Modeling in Mass
Mixture- modeling of mass spectra is an approach with many potential applications includ-ing peak... more Mixture- modeling of mass spectra is an approach with many potential applications includ-ing peak detection and quantification, smoothing, de-noising, feature extraction and spec-tral signal compression. However, existing algorithms do not allow for automated analyses of whole spectra. Therefore, despite highlighting potential advantages of mixture modeling of mass spectra of peptide/protein mixtures and some preliminary results presented in sev-eral papers, the mixture modeling approach was so far not developed to the stage enabling systematic comparisons with existing software packages for proteomic mass spectra analy-ses. In this paper we present an efficient algorithm for Gaussian mixture modeling of proteo-mic mass spectra of different types (e.g., MALDI-ToF profiling, MALDI-IMS). The main idea is automated partitioning of protein mass spectral signal into fragments. The obtained frag-ments are separately decomposed into Gaussian mixture models. The parameters of the mixture mo...
On time-invariant realizations of discrete random processes
Automatic Control, IEEE Transactions …, 1987
A class of discrete second-order random processes, described by linear time-invariant state-space... more A class of discrete second-order random processes, described by linear time-invariant state-space models, is investigated. Equivalent realizations with reduced number of noise inputs are presented. In contrast to the innovations approach these realizations ...
Quantum computing for clustering big datasets
2018 Applications of Electromagnetics in Modern Techniques and Medicine (PTZE)
GaMRed – adaptive filtering of high-throughput biological data
IEEE/ACM Transactions on Computational Biology and Bioinformatics
PLOS ONE
We present new results concerning probability distributions of times in the coalescence tree and ... more We present new results concerning probability distributions of times in the coalescence tree and expected allele frequencies for coalescent with large sample size. The obtained results are based on computational methodologies, which involve combining coalescence time scale changes with techniques of integral transformations and using analytical formulae for infinite products. We show applications of the proposed methodologies for computing probability distributions of times in the coalescence tree and their limits, for evaluation of accuracy of approximate expressions for times in the coalescence tree and expected allele frequencies, and for analysis of large human mitochondrial DNA dataset.
International Journal of Computational Methods
Setting initial values of parameters of mixture distributions estimated by using the EM recursive... more Setting initial values of parameters of mixture distributions estimated by using the EM recursive algorithm is very important to the overall quality of estimation. None of the existing methods are suitable for heteroscedastic mixtures with a large number of components. We present relevant novel methodology of estimating the initial values of parameters of univariate, heteroscedastic Gaussian mixtures, on the basis of dynamic programming partitioning of the range of observations into bins. We evaluate variants of the dynamic programming method corresponding to different scoring functions for partitioning. We demonstrate the superior efficiency of the proposed method compared to existing techniques for both simulated and real datasets.
Interdisciplinary Sciences: Computational Life Sciences
Springerlink.com provides insight into all of the studied subtypes, followed by the emergence of ... more Springerlink.com provides insight into all of the studied subtypes, followed by the emergence of four leukaemia subtype biomarkers. In addition, the class enhanced DEG signature obtained on the basis of novel pipeline processing leads to significantly better classification power of multi-class data classifiers. The developed methodology consisting of batch effect adjustment, adaptive noise and feature filtration coupled with adequate statistical testing and biomarker definition proves to be an effective approach towards knowledge discovery in high-throughput molecular biology experiments.
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Papers by andrzej polanski