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With the advent of precision medicine, biomarkers have recently come into focus as a promising tool for early cancer detection and treatment individualization. In particular, much interest has been shown in the oral microbiome as a promising potential cancer biomarker, especially for head and neck cancers. The American Cancer Society estimates that there will be nearly 50,000 new cases and roughly...
In this paper, we discuss the importance of feature subset selection methods in machine learning techniques. An analysis of microarray expression was used to check whether global biological differences underlie common pathological features for different types of cancer datasets and to identify genes that might anticipate the clinical behavior of this disease. One way of finding relevant gene selection...
With the development of deep sequencing technology, isomiRs (isoform of miRNA) are consistently observed in a variety of cell types, tissues, and different cell development stages. miRNA isoforms as the products of miRNA genes, are variants which are different from mature miRNAs in length and position. Recently, many studies emphasized on isomiR and found its subtypes are differentially expression...
Genome classification has become an increasingly important genomic research method for cancer identification and treatment. One challenge associated with genome classification is feature selection; which genes can be used for phenotyping. This challenge is made more complicated considering affected gene mutate at different rates and schedules. In addition, the number of genes and consequently the...
Machine learning classifiers help physicians to make near-perfect diagnoses, minimizing costs and time. Since medical data usually contains a high degree of uncertainty and ambiguity, proper ordering and classification require a proper comparative performance analysis of machine learning classifiers. Machine learning classifiers are applied on the Ovarian Cancer Dataset. Ovarian cancer is the fifth...
Computer-assisted analysis of endoscopic imagescan be helpful to the automatic diagnosis and classificationof neoplastic lesions. Barrett's esophagus (BE) is a commontype of reflux that is not straightforward to be detected byendoscopic surveillance, thus being way susceptible to erroneousdiagnosis, which can cause cancer when not treated properly. In this work, we introduce the Optimum-Path Forest...
This paper aims to classify a peripheral pulmonary lesion whether it is malignant or benign by proposing the new method to select a window of interest (WOI) using window slicing and the new feature called the "weight-sum of upper and lower gray level co-occurrence matrix (GLCM)" of an endobronchial ultrasound (EBUS) image. The proposed feature can be used to determine the heterogeneity of...
Circular RNAs (circRNAs) are a novel class of RNAs with mostly unknown function, and their biogenesis is still unclear. CircRNAs often show tissue and developmental stage-specific expression, and have been reported to be associated with human diseases such as cancer. During the pre-RNA processing, the 5' and 3' termini of one or more exons can be joined together to form circRNAs, and this process...
Identifying effective cancer biomarkers is crucial in precision medicine. Based on the high-throughput available omics data such as microarray, this paper aims to identify potential biomarker genes for hepatocellular carcinoma by bioinformatics and machine learning. We describe the gene coexpressions with network model and detect out the genes that are closely related to liver cancer infected by hepatitis...
Classification of different tumor type are of great significance in problems cancer prediction. Choosing the most relevant qualities from huge microarray expression is very important. It is a most explored subject in bioinformatics because of its hugeness to move forward humans understanding of inherent causing cancer mechanism. In this paper, we aim to classify leukaemia cells. Our approach relies...
Cancer classification is routinely done using gene expression data. With microarray technology, monitoring thousands of genes is an easy task. The reliable and precise classification of different tumour types is very important in cancer classification and drug discovery which is useful in providing better treatment. Microarray gene expression data analysis is extensively used for human cancer diagnosis...
This paper describes the challenge of real-time tumor tissue identification dealt with by the HypErspectraL Imaging Cancer Detection (HELICoiD) European project. This project was funded by the Research Executive Agency, through the Future and Emerging Technologies (FET-Open) programme, under the 7th Framework Programme of the European Union. It involved four universities, three industrial partners...
This paper proposes a method for construction of classifiers for discharge summaries. First, morphological analysis is applied to a set of summaries and a term matrix is generated. Second, correspond analysis is applied to the classification labels and the term matrixand generates two dimensional coordinates. By measuring thedistance between categories and the assigned points, ranking of key wordswill...
The paper presents the results of research on the use of Deep Neural Networks (DNN) for automatic classification of the skin lesions. The authors have focused on the most effective kind of DNNs for image processing, namely Convolutional Neural Networks (CNN). In particular, three kinds of CNN were analyzed: VGG19, Residual Networks (ResNet) and the hybrid of VGG19 CNN with the Support Vector Machine...
Some improvements in the classification of masses in the breast are proposed in this paper. First, for the purpose of enriching the information concerning the shape of the mass, a new morphological feature is extracted. Then, the textural features of the region of interest (ROI) are extracted by combining the undecimated wavelet transform (UWT) and the gray level co-occurrence matrix (GLCM). Finally,...
Detection of pulmonary nodules has played a significant role in lung cancer diagnosis because nodules are the first suspicious symptoms for the likelihood of cancer. Margin characteristics of the pulmonary nodules provide essential radiological features to determine the possibility of malignancy. In general, benign nodules hold quite smooth margins whilst malignant ones hold irregular margins. The...
This paper proposes a method for construction of classifiers for discharge summaries. First, morphological analysis is applied to a set of summaries and a term matrix is generated. Second, correspond analysis is applied to the classification labels and the term matrix and generates two dimensional coordinates. By measuring the distance between categories and the assigned points, ranking of key words...
Rough Set is a mathematical tool to find patterns hidden in data with uncertainty. A major step for reduction of high dimension data, present in various forms, is selection of appropriate features. In this work we propose a new indiscernibility relation based on clusters, and compare its effectiveness with that of classical Rough Set based indiscernibility. In particular, we study the proposed Rough...
Breast cancer is one of the most common cancer in women worldwide. It is typically diagnosed via histopathological microscopy imaging, for which image analysis can aid physicians for more effective diagnosis. Given a large variability in tissue appearance, to better capture discriminative traits, images can be acquired at different optical magnifications. In this paper, we propose an approach which...
In silico diagnosis through microRNA expression profiling experiments is a promising direction in the clinical practices of bioinformatics science. The task is computationally defined as a classification problem where a query experiment is required to be assigned into one of the predefined diseases using a model learned from previously labeled samples. While several powerful machine learning models...
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