Класифікація ЕКГ сигналів методами машинного навчання

The importance of electrocardiogram (ECG) analysis is difficult to overestimate. Rhythm of life, stress and other factors affect the frequency of diseases and their early appearance. At the same time, the technologization (digitalization) of life and hardware-software complexes, such as mobile electronic cardiographs and wearable devices in general, which are rapidly developing, open new opportunities for rapid analysis of human state by certain indicators, as well as allow to diagnose on the new higher level in almost real time. There are many methods for analyzing cardiograms. In this paper, the authors propose a new approach based on an ensemble of individual classifiers, which effectively solves the problem of ECG analysis. The study is based on the PhysioNet Computing in Cardiology Challenge 2017 and the MIT-BIH Arrhythmia Database. The algorithm consists of the following stages: data filtering using moving average and Butterworth filters, R-peak localization via threshold and grouping method, ECG resampling for the better comparability, “Noisy” vs “NotNoisy” classification as the most hard-to-identify class, final classification as “Normal”, “Atrial Fibrillation”, “Other” using an ensemble of 1D CNN classifiers and a final classifier of selection using logistic regression, random forest or support vector machine (SVM). The proposed method shows high accuracy by the metric F1, so it gives the background for further research, optimization and implementation. This way this algorithm could help to save human’s life by in-time detection of problems with cardiovascular system (CVS) at early stage. Pages of the article in the issue: 70 - 77 Language of the article: UkrainianВажливість аналізу електрокардіограм (ЕКГ) важко переоцінити. Ритм життя, стреси та інші фактори впливають на частоту захворювань та їх ранні прояви. Разом з тим, технологізація (цифровізація) життя та апаратно-програмних комплекси, такі як мобільні електронні кардіографи та носимі пристрої загалом, що бурхливо розвиваються останнім часом, відкривають нові можливості для швидкого аналізу стану людини за певними показниками, а також дозволяють проводити діагностику на новому рівні практично у реальному часі. Існує багато методів для аналізу кардіограм. В даній роботі авторами запропоновано новий підхід, що ефективно розв’язує задачу аналізу ЕКГ. Дослідження базується на наборі даних PhysioNetComputing in Cardiology Challenge 2017 та MIT-BIH Arrhythmia Database. Алгоритм складається зтаких етапів: фільтрація даних, локалізація R піків, передискретизація ЕКГ, визначення класу ЕКГ задопомогою ансамблю з 1D CNN та підсумкового класифікатора. Запропонований метод показує високу точність за метрикою F1, тому являє собою цінність дляподальших досліджень, оптимізації та впровадження

Tiling array data analysis: a multiscale approach using wavelets

<p>Abstract</p> <p>Background</p> <p>Tiling array data is hard to interpret due to noise. The wavelet transformation is a widely used technique in signal processing for elucidating the true signal from noisy data. Consequently, we attempted to denoise representative tiling array datasets for ChIP-chip experiments using wavelets. In doing this, we used specific wavelet basis functions, <it>Coiflets</it>, since their triangular shape closely resembles the expected profiles of true ChIP-chip peaks.</p> <p>Results</p> <p>In our wavelet-transformed data, we observed that noise tends to be confined to small scales while the useful signal-of-interest spans multiple large scales. We were also able to show that wavelet coefficients due to non-specific cross-hybridization follow a log-normal distribution, and we used this fact in developing a thresholding procedure. In particular, wavelets allow one to set an unambiguous, absolute threshold, which has been hard to define in ChIP-chip experiments. One can set this threshold by requiring a similar confidence level at different length-scales of the transformed signal. We applied our algorithm to a number of representative ChIP-chip data sets, including those of Pol II and histone modifications, which have a diverse distribution of length-scales of biochemical activity, including some broad peaks.</p> <p>Conclusions</p> <p>Finally, we benchmarked our method in comparison to other approaches for scoring ChIP-chip data using spike-ins on the ENCODE Nimblegen tiling array. This comparison demonstrated excellent performance, with wavelets getting the best overall score.</p

The RIP140 Gene Is a Transcriptional Target of E2F1

RIP140 is a transcriptional coregulator involved in energy homeostasis and ovulation which is controlled at the transcriptional level by several nuclear receptors. We demonstrate here that RIP140 is a novel target gene of the E2F1 transcription factor. Bioinformatics analysis, gel shift assay, and chromatin immunoprecipitation demonstrate that the RIP140 promoter contains bona fide E2F response elements. In transiently transfected MCF-7 breast cancer cells, the RIP140 promoter is transactivated by overexpression of E2F1/DP1. Interestingly, RIP140 mRNA is finely regulated during cell cycle progression (5-fold increase at the G1/S and G2/M transitions). The positive regulation by E2F1 requires sequences located in the proximal region of the promoter (−73/+167), involves Sp1 transcription factors, and undergoes a negative feedback control by RIP140. Finally, we show that E2F1 participates in the induction of RIP140 expression during adipocyte differentiation. Altogether, this work identifies the RIP140 gene as a new transcriptional target of E2F1 which may explain some of the effect of E2F1 in both cancer and metabolic diseases

Reconstruction of the Core and Extended Regulons of Global Transcription Factors

The processes underlying the evolution of regulatory networks are unclear. To address this question, we used a comparative genomics approach that takes advantage of the large number of sequenced bacterial genomes to predict conserved and variable members of transcriptional regulatory networks across phylogenetically related organisms. Specifically, we developed a computational method to predict the conserved regulons of transcription factors across α-proteobacteria. We focused on the CRP/FNR super-family of transcription factors because it contains several well-characterized members, such as FNR, FixK, and DNR. While FNR, FixK, and DNR are each proposed to regulate different aspects of anaerobic metabolism, they are predicted to recognize very similar DNA target sequences, and they occur in various combinations among individual α-proteobacterial species. In this study, the composition of the respective FNR, FixK, or DNR conserved regulons across 87 α-proteobacterial species was predicted by comparing the phylogenetic profiles of the regulators with the profiles of putative target genes. The utility of our predictions was evaluated by experimentally characterizing the FnrL regulon (a FNR-type regulator) in the α-proteobacterium Rhodobacter sphaeroides. Our results show that this approach correctly predicted many regulon members, provided new insights into the biological functions of the respective regulons for these regulators, and suggested models for the evolution of the corresponding transcriptional networks. Our findings also predict that, at least for the FNR-type regulators, there is a core set of target genes conserved across many species. In addition, the members of the so-called extended regulons for the FNR-type regulators vary even among closely related species, possibly reflecting species-specific adaptation to environmental and other factors. The comparative genomics approach we developed is readily applicable to other regulatory networks

Genome-Wide Progesterone Receptor Binding: Cell Type-Specific and Shared Mechanisms in T47D Breast Cancer Cells and Primary Leiomyoma Cells

Progesterone, via its nuclear receptor (PR), exerts an overall tumorigenic effect on both uterine fibroid (leiomyoma) and breast cancer tissues, whereas the antiprogestin RU486 inhibits growth of these tissues through an unknown mechanism. Here, we determined the interaction between common or cell-specific genome-wide binding sites of PR and mRNA expression in RU486-treated uterine leiomyoma and breast cancer cells.ChIP-sequencing revealed 31,457 and 7,034 PR-binding sites in breast cancer and uterine leiomyoma cells, respectively; 1,035 sites overlapped in both cell types. Based on the chromatin-PR interaction in both cell types, we statistically refined the consensus progesterone response element to G•ACA• • •TGT•C. We identified two striking differences between uterine leiomyoma and breast cancer cells. First, the cis-regulatory elements for HSF, TEF-1, and C/EBPα and β were statistically enriched at genomic RU486/PR-targets in uterine leiomyoma, whereas E2F, FOXO1, FOXA1, and FOXF sites were preferentially enriched in breast cancer cells. Second, 51.5% of RU486-regulated genes in breast cancer cells but only 6.6% of RU486-regulated genes in uterine leiomyoma cells contained a PR-binding site within 5 kb from their transcription start sites (TSSs), whereas 75.4% of RU486-regulated genes contained a PR-binding site farther than 50 kb from their TSSs in uterine leiomyoma cells. RU486 regulated only seven mRNAs in both cell types. Among these, adipophilin (PLIN2), a pro-differentiation gene, was induced via RU486 and PR via the same regulatory region in both cell types.Our studies have identified molecular components in a RU486/PR-controlled gene network involved in the regulation of cell growth, cell migration, and extracellular matrix function. Tissue-specific and common patterns of genome-wide PR binding and gene regulation may determine the therapeutic effects of antiprogestins in uterine fibroids and breast cancer

Identification of Novel Targets of CSL-Dependent Notch Signaling in Hematopoiesis

Somatic activating mutations in the Notch1 receptor result in the overexpression of activated Notch1, which can be tumorigenic. The goal of this study is to understand the molecular mechanisms underlying the phenotypic changes caused by the overexpression of ligand independent Notch 1 by using a tetracycline inducible promoter in an in vitro embryonic stem (ES) cells/OP9 stromal cells coculture system, recapitulating normal hematopoiesis. First, an in silico analysis of the promoters of Notch regulated genes (previously determined by microarray analysis) revealed that the motifs recognized by regulatory proteins known to mediate hematopoiesis were overrepresented. Notch 1 does not bind DNA but instead binds the CSL transcription factor to regulate gene expression. The in silico analysis also showed that there were putative CSL binding sites observed in the promoters of 28 out of 148 genes. A custom ChIP-chip array was used to assess the occupancy of CSL in the promoter regions of the Notch1 regulated genes in vivo and showed that 61 genes were bound by activated Notch responsive CSL. Then, comprehensive mapping of the CSL binding sites genome-wide using ChIP-seq analysis revealed that over 10,000 genes were bound within 10 kb of the TSS (transcription start site). The majority of the targets discovered by ChIP-seq belong to pathways that have been shown by others to crosstalk with Notch signaling. Finally, 83 miRNAs were significantly differentially expressed by greater than 1.5-fold during the course of in vitro hematopoiesis. Thirty one miRNA were up-regulated and fifty two were down-regulated. Overexpression of Notch1 altered this pattern of expression of microRNA: six miRNAs were up-regulated and four were down regulated as a result of activated Notch1 overexpression during the course of hematopoiesis. Time course analysis of hematopoietic development revealed that cells with Notch 1 overexpression mimic miRNA expression of cells in a less mature stage, which is consistent with our previous biological characterization

Systematic evaluation of variability in ChIP-chip experiments using predefined DNA targets

The most widely used method for detecting genome-wide protein–DNA interactions is chromatin immunoprecipitation on tiling microarrays, commonly known as ChIP-chip. Here, we conducted the first objective analysis of tiling array platforms, amplification procedures, and signal detection algorithms in a simulated ChIP-chip experiment. Mixtures of human genomic DNA and “spike-ins” comprised of nearly 100 human sequences at various concentrations were hybridized to four tiling array platforms by eight independent groups. Blind to the number of spike-ins, their locations, and the range of concentrations, each group made predictions of the spike-in locations. We found that microarray platform choice is not the primary determinant of overall performance. In fact, variation in performance between labs, protocols, and algorithms within the same array platform was greater than the variation in performance between array platforms. However, each array platform had unique performance characteristics that varied with tiling resolution and the number of replicates, which have implications for cost versus detection power. Long oligonucleotide arrays were slightly more sensitive at detecting very low enrichment. On all platforms, simple sequence repeats and genome redundancy tended to result in false positives. LM-PCR and WGA, the most popular sample amplification techniques, reproduced relative enrichment levels with high fidelity. Performance among signal detection algorithms was heavily dependent on array platform. The spike-in DNA samples and the data presented here provide a stable benchmark against which future ChIP platforms, protocol improvements, and analysis methods can be evaluated

Локалізація зміни сцен у відео

Millions of videos are uploaded each day to Youtube and similar platforms. One of the many issues that these services face is the extraction of useful metadata. There are a lot of tasks that arise with the processing of videos. For example, putting an ad is better in the middle of a video, and as an advertiser, one would probably prefer to show the ad in between scene cuts, where it would be less intrusive. Another example is when one would like to watch only through the most interesting or important pieces of video recording. In many cases, it is better to have an automatic scene cut detection approach instead of manually labeling thousands of videos. The scene change detection can help to analyze video-stream automatically: which characters appear in which scenes, how they interact and for how long, their relations and importance, and also to track many other issues. The potential solution can rely on different facts: objects appearance, contrast or intensity changed, other colorization, background chang, and also sound changes. In this work, we propose the method for effective scene change detection, which is based on thresholding, and also fade-in/fade-out scene analysis. It uses computer vision and image analysis approaches to identify the scene cuts. Experiments demonstrate the effectiveness of the proposed scene change detection approach. Pages of the article in the issue: 57 - 62 Language of the article: UkrainianМільйони відео щодня завантажуються на Youtube та подібні платформи. Однією з багатьох проблем, з якими стикаються ці служби, є вилучення корисних метаданих. Наприклад, розміщувати рекламу краще в середині відео, і рекламодавець вважає за краще показувати рекламу в перервах між сценами, де це буде менш нав'язливо. Інший приклад – подивитись лише найцікавіші чи найважливіші фрагменти відеозапису. Зрозуміло, що краще застосовувати автоматичний підхід до розпізнавання сцени замість того, щоб розмічати тисячі відеозаписів вручну. Виявлення змін сцени може допомогти автоматично проаналізувати відеопотік: відстежувати, які персонажі з’являються в яких сценах, як довго взаємодіють, їх стосунки та важливість. Потенційне рішення може враховувати різні фактори: появу об’єкта, зміну контрасту чи інтенсивності, зміну фону, зміни звуку. У цій роботі запропоновано метод для ефективного виявлення зміни сцени, який базується на аналізі сцени з пороговими значенням, а також плавними змінами сцен. Він використовує підходи комп’ютерного зору та аналізу зображень для виявлення зміни сцени. Експериментальні результати демонструють ефективність запропонованого підходу до виявлення змін сцени. Pages of the article in the issue: 57 - 62 Language of the article: Ukrainia