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The long noncoding RNAs MEG3 is involved in the pathogenesis and metastasis of cancer, which has been found to target the distal regulatory elements (DREs) of TGF‐β pathway genes TGFBR1, TGFB2, and SMAD2 by forming DNA‐RNA triplex. Here, the molecular mechanism of MEG3 binding to its cognate or noncognate DNA partners is investigated systematically via an integrative chemometric approach. Here, we...
In this study, a novel deep learning-based framework for classifying the digital mammograms is introduced. The development of this methodology is based on deep learning strategies that model the presence of the tumour tissues with level sets. It is difficult to robustly segment mammogram image due to low contrast between normal and lesion tissues. Therefore, Chan-Vese level set method is used to extract...
Human mobility has been studied extensively in various biomedical contexts with applications in clinical rehabilitation, disease diagnosis, health risk prognosis, and general performance assessments. In this paper, we present ATOMHP (Analytical Technologies to Objectively Measure Human Performance) Kinect: a system to objectively quantify human performance using the Microsoft Kinect as a single camera...
As the diagnosis of lung cancer, lung mass for the diagnosis of the disease is meaningful, chest radiography has low price, low radiation, popularity and other characteristics, it is a significant attempt for the location of chest masses on chest radiography using deep learning method. In this paper we have established a labeled lung mass database, and presented a state of the art deep learning methodology...
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...
Lung cancer is one of the most common types of cancer originated from malignant lung nodules. Early detection of lung nodule is key in prevention of lung cancer. In this paper, we developed an online content-based image retrieval (CBIR) system to assist novice radiologists in identifying lung nodules. The system takes advantages of cloud computing and deep learning to retrieve similar lung nodules...
Accurate early lung cancer detection is essential towards precision oncology and would effectively improve the patients' survival rate. In this work, we explore the lung cancer early detection capacity by learning from deep spatial lung features. A 3D CNN network architecture is constructed with segmented CT lung volumes as training and testing samples. The new model extracts and projects 3D features...
While cancer is a heterogeneous complex of distinct diseases, the common underlying mechanism for uncontrolled tumor growth is due to mutations in proto-oncogenes and the loss of the regulatory function of tumor suppression genes. In this paper we propose a novel deep learning model for predicting tumor suppression genes (TSGs) and proto-oncogenes (OGs) from their Protein Data Bank (PDB) three dimensional...
Obesity has been linked to several types of cancer. Access to adequate health information activates people's participation in managing their own health, which ultimately improves their health outcomes. Nevertheless, the existing online information about the relationship between obesity and cancer is heterogeneous and poorly organized. A formal knowledge representation can help better organize and...
In molecular biology, the selection of feature genes and tumor clustering are the hotspots and difficulties in bioinformatics research. The traditional PCA method based on the minimization of the squares of the loss function is sensitive to the outliers and noise. Therefore, it is necessary to design a new method to weaken the effects of errors and noise. In this paper, we propose a novel PCA method...
Cancer genome projects are characterizing the genome, epigenome and transcriptome of a large number of samples using the latest high-throughput sequencing assays. The generated data sets pose several challenges for traditional statistical and machine learning methods. In this work we are interested in the task of deriving the most informative genes from a cancer gene expression data set. For that...
Convolutional Neural Networks (CNN) have brought a revolutionary improvement to image analysis, especially in the image classification field. The technique of natural image classification using the CNN method has been deliberately utilized for medical image classification with some advanced engineering. However, so far in most of the cases CNN model classifies images based on global features extraction...
Cancerous masses detection in dense background is a particularly challenging task for even experienced radiologists due to their similarity of intensity with the overlapped normal dense tissues, obscured boundaries and low contrast between mass and surrounding regions. This paper proposes a novel approach for the identification of cancerous regions located in a dense part of a breast. Careful analysis...
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...
The classification of cancer subtypes is of great importance in cancer disease diagnosis and therapy. Many supervised learning methods have been applied to classification of cancer subtypes in the past few years, especially of deep learning based methods. Recently, a deep forest model has been proposed as an alternative of deep neural networks to learn hyper-representations by using cascade ensemble...
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...
Automated segmentation of cell nuclei is crucial for the early diagnosis of cancer as the characteristics of the cell nuclei are mainly associated with the assessment of malignancy. Only a few research work has been done on automated segmentation of cell nuclei on cytology pleural effusion images, which is poorly handled by previous methods. In addition, cytology pleural effusion image itself is still...
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...
Real-world datasets are often imbalanced, with an important class having many fewer examples than other classes. In medical data, normal examples typically greatly outnumber disease examples. A classifier learned from imbalanced data, will tend to be very good at the predicting examples in the larger (normal) class, yet the smaller (disease) class is typically of more interest. Imbalance is dealt...
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