Propagation-Based Biclustering Algorithm for Extracting Inclusion-Maximal Motifs

Biclustering, which is simultaneous clustering of columns and rows in data matrix, became an issue when classical clustering algorithms proved not to be good enough to detect similar expressions of genes under subset of conditions. Biclustering algorithms may be also applied to different datasets, such as medical, economical, social networks etc. In this article we explain the concept beneath hybrid biclustering algorithms and present details of propagation-based biclustering, a novel approach for extracting inclusion-maximal gene expression motifs conserved in gene microarray data. We prove that this approach may successfully compete with other well-recognized biclustering algorithms

PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison

The selection, development, or comparison of machine learning methods in data mining can be a difficult task based on the target problem and goals of a particular study. Numerous publicly available real-world and simulated benchmark datasets have emerged from different sources, but their organization and adoption as standards have been inconsistent. As such, selecting and curating specific benchmarks remains an unnecessary burden on machine learning practitioners and data scientists. The present study introduces an accessible, curated, and developing public benchmark resource to facilitate identification of the strengths and weaknesses of different machine learning methodologies. We compare meta-features among the current set of benchmark datasets in this resource to characterize the diversity of available data. Finally, we apply a number of established machine learning methods to the entire benchmark suite and analyze how datasets and algorithms cluster in terms of performance. This work is an important first step towards understanding the limitations of popular benchmarking suites and developing a resource that connects existing benchmarking standards to more diverse and efficient standards in the future.Comment: 14 pages, 5 figures, submitted for review to JML

Automated Affect and Emotion Recognition from Cardiovascular Signals - A Systematic Overview Of The Field

Currently, artificial intelligence is increasingly used to recognize and differentiate emotions. Through the action of the nervous system, the heart and vascular system can respond differently depending on the type of arousal. With the growing popularity of wearable devices able to measure such signals, people may monitor their states and manage their wellness. Our goal was to explore and summarize the field of automated emotion and affect recognition from cardiovascular signals. According to our protocol, we searched electronic sources (MEDLINE, EMBASE, Web of Science, Scopus, dblp, Cochrane Library, IEEE Explore, arXiv and medRxiv) up to 31 August 2020. In the case of all identified studies, two independent reviewers were involved at each stage: screening, full-text assessment, data extraction, and quality evaluation. All conflicts were resolved during the discussion. The credibility of included studies was evaluated using a proprietary tool based on QUADAS, PROBAST. After screening 4649 references, we identified 195 eligible studies. From artificial intelligence most used methods in emotion or affect recognition were Support Vector Machines (42.86%), Neural Network (21.43%), and k-Nearest Neighbors (11.67%). Among the most explored datasets were DEAP (10.26%), MAHNOB-HCI (10.26%), AMIGOS (6.67%) and DREAMER (2.56%). The most frequent cardiovascular signals involved electrocardiogram (63.16%), photoplethysmogram (15.79%), blood volume pressure (13.16%) and heart rate (6.58%). Sadness, fear, and anger were the most examined emotions. However, there is no standard set of investigated internal feelings. On average, authors explore 4.50 states (range from 4 to 24 feelings). Research using artificial intelligence in recognizing emotions or affect using cardiovascular signals shows an upward trend. There are significant variations in the quality of the datasets, the choice of states to detect, and the classifiers used for analysis. Research project supported by program Excellence initiative - research university for the University of Science and Technology. The authors declare that they have no conflict of interest

looking back and looking forward

Mcdermott, J., Kronberger, G., Orzechowski, P., Vanneschi, L., Manzoni, L., Kalkreuth, R., & Castelli, M. (2022). Genetic programming benchmarks: looking back and looking forward. ACM SIGEVOlution, 15(3), 1-19. https://doi.org/10.1145/3578482.3578483The top image shows a set of scales, which are intended to bring to mind the ideas of balance and fair experimentation which are the focus of our article on genetic programming benchmarks in this issue. Image by Elena Mozhvilo and made available under the Unsplash license on https://unsplash.com/photos/j06gLuKK0GM.authorsversionpublishe

Vascular phenotypes in early hypertension

The study characterises vascular phenotypes of hypertensive patients utilising machine learning approaches. Newly diagnosed and treatment-naïve primary hypertensive patients without co-morbidities (aged 18–55, n = 73), and matched normotensive controls (n = 79) were recruited (NCT04015635). Blood pressure (BP) and BP variability were determined using 24 h ambulatory monitoring. Vascular phenotyping included SphygmoCor® measurement of pulse wave velocity (PWV), pulse wave analysis-derived augmentation index (PWA-AIx), and central BP; EndoPAT™-2000® provided reactive hyperaemia index (LnRHI) and augmentation index adjusted to heart rate of 75bpm. Ultrasound was used to analyse flow mediated dilatation and carotid intima-media thickness (CIMT). In addition to standard statistical methods to compare normotensive and hypertensive groups, machine learning techniques including biclustering explored hypertensive phenotypic subgroups. We report that arterial stiffness (PWV, PWA-AIx, EndoPAT-2000-derived AI@75) and central pressures were greater in incident hypertension than normotension. Endothelial function, percent nocturnal dip, and CIMT did not differ between groups. The vascular phenotype of white-coat hypertension imitated sustained hypertension with elevated arterial stiffness and central pressure; masked hypertension demonstrating values similar to normotension. Machine learning revealed three distinct hypertension clusters, representing ‘arterially stiffened’, ‘vaso-protected’, and ‘non-dipper’ patients. Key clustering features were nocturnal- and central-BP, percent dipping, and arterial stiffness measures. We conclude that untreated patients with primary hypertension demonstrate early arterial stiffening rather than endothelial dysfunction or CIMT alterations. Phenotypic heterogeneity in nocturnal and central BP, percent dipping, and arterial stiffness observed early in the course of disease may have implications for risk stratification

Parallel Approach for Visual Clustering of Protein Databases

Visualization of a large-scale protein databases may help biologists in discovering similarity between sequences of different organisms. In this article we present a complex approach for visually representing relations between proteins in large scale databases. Our approach includes sequence alignment, mutual distance measurement, clustering and classification of protein sequences. We propose a visual representation method for considered as well-established Pfam 4.0 proteins database. Our objective is to visually reflect the similarity of protein sequences in three dimensional space using non-standard approach

Towards design of web service based vehicle navigation system

Tyt. z nagł.Bibliogr. s. 428-430.Inteligentne systemy transportu zyskują na popularności w ostatnich latach, zwłaszcza w większych miastach. W artykule opisano rezultat przeglądu literatury poświęconej zagadnieniom nawigacji samochodowej oraz pomiarom ruchu drogowego. Zebrane informacje, z uwzględnieniem badań nad psychologicznym aspektem procesu podejmowania decyzji przez kierowców, stanowią bazę wyjściową do zaprojektowania systemu zarządzania ruchu w mieście. Załączony został opis koncepcji oraz prototypowa implementacja prostego serwisu WWW, prezentującego informacje o ruchu ulicznym w Krakowie.Inteligentne systemy transportu zyskują na popularności w ostatnich latach, zwłaszcza w większych miastach. W artykule opisano rezultat przeglądu literatury poświęconej zagadnieniom nawigacji samochodowej oraz pomiarom ruchu drogowego. Zebrane informacje, z uwzględnieniem badań nad psychologicznym aspektem procesu podejmowania decyzji przez kierowców, stanowią bazę wyjściową do zaprojektowania systemu zarządzania ruchu w mieście. Załączony został opis koncepcji oraz prototypowa implementacja prostego serwisu WWW, prezentującego informacje o ruchu ulicznym w Krakowie.Intelligent Transportation Systems (ITS) have been growing in popularity lately, especially in larger cities. In this article we present the result of a survey on vehicle navigation systems and on traffic measurement. Gathered information, including studies on psychological aspects of drivers decision making process, are a basis for designing a web service solution. Concept and prototypical implementation of a web service displaying traffic information for the city of Cracow is also included.Dostępny również w formie drukowanej.SŁOWA KLUCZOWE: nawigacja samochodowa, monitorowanie ruchu drogowego, serwer WWW, inteligentny system transportu. KEYWORDS: vehicle navigation, traffic monitoring, web service intelligent transportation system

Strategie poprawy efektywności uczenia sieci neuronowej

Tyt. z nagłówka.Bibliogr. s. 374.Dostępny również w formie drukowanej.STRESZCZENIE: W artykule przedstawiono wyniki eksperymentów procesu uczenia sieci neuronowej ze wsteczną propagacją błędu, wykorzystywanej w procesie rozpoznawania ręcznie pisanych cyfr. Opisano kilka niestandardowych technik, takich jak: korekcja nachylenia cyfr oraz dwa warianty uczenia sekwencyjnego sieci neuronowej, bazujących na pewności rozpoznania cyfr przez sieć oraz statystyce pomyłek klasyfikacyjnych sieci. SŁOWA KLUCZOWE: wsteczna propagacja błędu, ręcznie pisane cyfry, rozpoznawanie obrazów, uczenie sekwencyjne, strategie uczenia. ABSTRACT: This article presents the results of experiments carried out before and during the learning process of artificial neural network (backpropagation), used for handwritten digits recognition. Some unconventional techniques are described, such as an algorithm of slant correction and two variants of sequential learning, basing on the recognition reliability of the specific digit and statistical confusion matrix. KEYWORDS: backpropagation, handwritten digits, image recognition, sequential learning, learning schemes