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2
Content available remote MicroRNA expression prediction: Regression from regulatory elements
100%
EN
MicroRNAs are known as important actors in post-transcriptional regulation and relevant biological processes. Their expression levels do not only provide information about their own activities but also implicitly explain the behaviors of their targets, thus, in turn, the circuitry of underlying gene regulatory network. In this study, we consider the problem of estimating the expression of a newly discovered microRNA with known promoter sequence in a certain condition where the expression values of some known microRNAs are available. To this end, we offer a regression model to be learnt from the expression levels of other microRNAs obtained through a microarray experiment. To our knowledge, this is the first study that evaluates the predictability of microRNA expression from the regulatory elements found in its promoter sequence. The results obtained through the experiments on real microarray data justify the applicability of the framework in practice.
EN
The paper presents data mining methods applied to gene selection for recognition of a particular type of prostate cancer on the basis of gene expression arrays. Several chosen methods of gene selection, including the Fisher method, correlation of gene with a class, application of the support vector machine and statistical hypotheses, are compared on the basis of clustering measures. The results of applying these individual selection methods are combined together to identify the most often selected genes forming the required pattern, best associated with the cancerous cases. This resulting pattern of selected gene lists is treated as the input data to the classifier, performing the task of the final recognition of the patterns. The numerical results of the recognition of prostate cancer from normal (reference) cases using the selected genes and the support vector machine confirm the good performance of the proposed gene selection approach.
EN
The objective of this study was to demonstrate diff erences in the gene expression of human cervical cancer cells (HeLa) and vinblastine-resistant KB-V1 subline treated with doxorubicin alone and combination of Selol 5% and doxorubicin. Ongoing studies seek to clarify the mechanism of action of Selol in diff erent types of cancer cells, including those which show multidrug resistance. Cells treatment with the tested compounds in the group of genes tested in HeLa cells causes other changes than in KB-V1 cells. In the resistant cells, exposure to Selol 5% and doxorubicin, released the cytotoxic eff ects by changing the expression of ABCC2 and BCL2L1 genes. The observed dependence also allows better understanding the molecular mechanisms of resistance in the KB-V1 cell line.
PL
Celem pracy było wykazanie różnic w ekspresji genów komórek ludzkiego nowotworu szyjki macicy (HeLa) i opornej na winblastynę podlinii KB-V1, poddanych działaniu samej doksorubicyny oraz po łącznym podaniu Selolu 5% i doksorubicy. Prowadzone badania zmierzają do wyjaśnienia mechanizmu działania Selolu w różnych typach komórek nowotworowych, w tym opornych wielolekowo. Poddanie komórek działaniu testowanych związków powoduje inne zmiany w grupie badanych genów w komórkach HeLa niż w komórkach KB-V1. Łączne podanie Selolu 5% i doksorubicyny wyzwala efekt cytotoksyczny w komórkach opornych KB-V1, co przypuszczalnie jest związane ze zmianą ekspresji genów ABCC2 i BCL2L1. Zaobserwowana zależność pozwala także lepiej zrozumieć molekularne podłoże oporności komórek linii KB-V1.
5
84%
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2021
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tom Vol. 41, no. 3
916--932
EN
Recognizing the cancer genes from the microarray dataset is considered as the most essential research topic in bioinformatics and computational biology domain. Microarray dataset represents the state of each cell at the molecular level which is identified as the important diagnostic tool in medical field. Analyzing the microarray data may provide a huge support for cancer gene classification. Therefore recently a number of artificial intelligence and machine learning techniques are developed which utilize the microarray data for distinguishing the cancer and non-cancer cells. But still now these techniques does not achieved a satisfactory performance. Therefore, an efficient technique that provides a crisp output for cancer classification is required. To overcome such defect, an enhanced ANFIS (EANFIS) method is used in this proposed architecture for classifying the cancer genes. The convergence time of ANFIS gets increased during learning process, therefore to avoid such issue the Manta ray foraging optimization (MaFO) algorithm is hybrid along with ANFIS which improves the overall classification performance. The data given as an input to the classification process is pre-processed at the initial phase using the Ensemble Kalman Filter (EnKF) technique. After pre-processing, the genes having similar properties are clustered using an adaptive density-based spatial clustering with noise (ADBSCAN) clustering technique. Finally, the performance of proposed enhanced ANFIS is evaluated using the precision, accuracy, f-measure, recall, sensitivity, and specificity metrics. Further, the clustering based performance evaluation is also carried out using the cluster index metrics. Finally, the comparison with the state-of-the-art techniques is also performed to show the effectiveness of proposed approach.
6
Content available remote Evaluation of how low frequency magnetic field 50 Hz affect living cells
84%
EN
The mechanism of ELF-MF impact on the metabolic processes occurring in cells of the living organisms is discussed. Existing research suggests that biological membranes may be composite antenna for stimulation by an electromagnetic field. To further elucidate this mechanism the use of fluorescent probes is suggested.
PL
W referacie przedstawiono postulowany mechanizm oddziaływania ELF-MF na procesy metaboliczne zachodzące w komórkach organizmów żywych. Błony biologiczne mogą być anteną zbiorczą dla bodźca, jakim jest pole elektromagnetyczne. Postuluje się wykorzystanie sond fluorescencyjnych do dalszych badań.
7
Content available Aktyna i miozyny w jądrze komórkowym
67%
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nr 1
75-93
PL
Aktyna i miozyna to białka kojarzone przede wszystkim z ich kluczową rolą w generacji skurczu mięśni. Natomiast poza izoformami charakterystycznymi dla mięśni są również izoformy aktyny i miozyny, które występują we wszystkich typach komórek i tkanek (patrz artykuł Suszek i współaut. w tym zeszycie KOSMOSU). Badania prowadzone w ostatnich dwóch dekadach wykazały niezbicie, że zarówno aktyna (i szereg białek wiążących aktynę) oraz liczne miozyny (przedstawiciele rodzin I, II, V, VI, XVI i XVIII) lokalizują się w jądrze komórkowym gdzie są zaangażowane w procesy transkrypcji i naprawy DNA, transport w nukleoplazmie oraz import i eksport jądrowy, a także w utrzymywanie architektury jądra. Niniejszy artykuł opisuje dotychczasowy stan wiedzy o roli układu akto-miozynowego w jądrze komórkowym.
EN
Actin and myosins are the proteins mainly known from their key roles in muscle contraction. However, besides typical muscle isoforms there are actins and myosins that are present in all cell and tissue types. Studies performed within the last two decades have irrefutably shown that both the cytoplasmic actin isoforms (along with numerous actin-binding proteins) as well as many myosins (representing class I, II, V, VI, XVI and XVIII) are present within the nucleus. They play important roles in nuclear processes as they are involved in transcription and DNA repair, intranuclear transport as well as nuclear import and export, and in maintenance of nuclear architecture. This article describes the current knowledge on the acto-myosin system in this biggest cellular compartment.
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