Wyniki wyszukiwania - Biblioteka Nauki

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Methods of microarray data preprocessing for classification of tumors

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Rubel T. , Pawłowski Z. , Zaremba K.

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tom Vol. 25, no. 4

37-57

EN

Gene arrays measure expression levels for thousands of genes simultaneously, providing a powerful tool for both basic research and clinical medicine. The aim of this paper was to present an optimal approach to preprocessing data from cancer microarray studies. The performance of different statistical methods used for the tumor classification was also compared. These methods include: the Bayes classifier, Fisher's classifier, minimum Euclidean and Mahalanobis distance classifiers and K-nearest neighbours classifier. The preprocessing algorithms and classification methods were applied to three datasets used for diagnosis of lymphoma, leukemia and lung cancer.

2

A multi-layered incremental feature selection algorithm for adjuvant chemotherapy effectiveness/futileness assessment in non-small cell lung cancer

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Naftchali R. E. , Abadeh M. S.

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2017

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tom Vol. 37, no. 3

477--488

EN

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer; and is one of the leading causes of death in the world. Surgery combined with chemotherapy is the recommended treatment for NSCLC. Since chemotherapy is an expensive treatment for either medical staff or patients suffering from pain, this study attempts to construct an intelligent predictive model to predict the adjuvant chemotherapy (ACT) effectiveness/ futileness in the patients, in order to help futile cases for unnecessary applications. There is a 2-step method: preprocessing and predicting. First a purposefully preprocessing tech-nique: chi-square test, SVM-RFE and correlation matrix, were employed in NSCLC gene expression dataset as a novel multi-layered feature selection method to defeat the curse of dimension and detect the chemotherapy target genes from tens of thousands features, based on which the patients can be classified into two groups, with NB classifier at second step. 10-Fold cross-validation was found with accuracy of 68.93% for 2 genes, TGFA (205015_s_at) and SEMA6C (208100_x_at), which is preferable compared to earlier studies, even though more than 2 input features are employed for the prediction. According to the results found in this study, one can concludes that the multi-layered feature selection approach has increased the classification accuracy in terms of finding the fitted patient for receiving ACT by reducing the number of features and has significant power to be used in medical datasets with small train samples and large number of features.

3

Ocena ekspresji genu ABCC1 kodującego białko MRP1 u osób chorych na depresję

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Świechowski R. , Balcerczak E. , Żebrowska M. , Jeleń A.

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nr 2

107-122

EN

Introduction: According to the World Health Organization, depression is the most common mental disorder. Over 350 million people suffer from this disease, making it the fourth most important health problem in the world. Multidrug resistance protein 1 (MRP1), encoded by ABCC1 gene is co–creating the blood– brain barrier and blood–cerebrospinal fluid barrier. Changes in the expression of ABCC1 can influence the bioavailability of antidepressants, and thereby, determine the efficacy of the treatment. The aim of the study was to evaluate ABCC1 gene expression in patients with depression. Material and methods: We analyzed 32 samples of RNA isolated from the leukocytes of peripheral blood, derived from Babinski Hospital patients suffering from recurrent depressive episodes. The investigated gene expression was assessed using the technique of Real–time PCR. Results: On the basis of qualitative analysis, ABCC1 gene expression has been shown in 30 samples, and the GAPDH gene expression in 32 samples. All 32 samples were quantitatively analyzed. The level of ABCC1 gene expression relative to GAPDH gene was variable among all 32 cases. An average, positive and significant correlation (r = 0.3988) between age of the patients and the relative expression level of ABCC1 has been shown. Conclusions: The relative level of gene expression increases with the age of the patients with depression. The obtained results require confirmation in a larger group of patients.

PL

Wstęp: Według Światowej Organizacji Zdrowia depresja jest najczęściej występującym zaburzeniem psychicznym. Ponad 350 milionów ludzi na całym świecie cierpi na tę chorobę, co czyni ją czwartym z najważniejszych obecnie problemów zdrowotnych. Białko oporności wielolekowej 1 (MRP1), kodowane przez gen ABCC1, jest elementem współtworzącym barierę krew–mózg oraz krew–płyn mózgowo–rdzeniowy. Zmiana ekspresji genu ABCC1 może wpłynąć na biodostępność leków antydepresyjnych, a tym samym na skuteczność farmakoterapii. Celem pracy była ocena ekspresji genu ABCC1 u pacjentów chorych na depresję. Materiał i metody: Badaniu poddano 32 próby RNA wyizolowane z leukocytów krwi obwodowej pacjentów chorych na depresję, leczonych w szpitalu im. Babińskiego w Łodzi. Ekspresja badanego genu została oceniona przy użyciu techniki Real–time PCR. Wyniki: Na podstawie analizy jakościowej wykazano ekspresję genu ABCC1 w 30 próbach, a genu referencyjnego GAPDH w 32 próbach. Wszystkie 32 próby poddano analizie ilościowej. Poziom ekspresji genu ABCC1 względem genu GAPDH we wszystkich 32 przypadkach był wysoce zróżnicowany. Wykazano przeciętną, dodatnią, istotną statystycznie korelację (r = 0,398 pomiędzy wiekiem pacjentów, a względnym poziom ekspresji genu ABCC1. Wnioski: Względny poziom ekspresji genu ABCC1 jest tym wyższy, im wyższy jest wiek pacjentów w grupie badanej. Uzyskane w pracy wyniki badań wymagają potwierdzenia na większej grupie pacjentów.