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Siti Salwa Md Noor, Kaleena Michael, Stephen Marshall, Jinchang Ren
In our preliminary study, the reflectance signatures obtained from hyperspectral imaging (HSI) of normal and abnormal corneal epithelium tissues of porcine show similar morphology with subtle differences. Here we present image enhancement algorithms that can be used to improve the interpretability of data into clinically relevant information to facilitate diagnostics. A total of 25 corneal epithelium images without the application of eye staining were used. Three image feature extraction approaches were applied for image classification: (i) image feature classification from histogram using a support vector machine with a Gaussian radial basis function (SVM-GRBF); (ii) physical image feature classification using deep-learning Convolutional Neural Networks (CNNs) only; and (iii) the combined classification of CNNs and SVM-Linear...
November 16, 2017: Sensors
Maram A Wahba, Amira S Ashour, Sameh A Napoleon, Mustafa M Abd Elnaby, Yanhui Guo
Purpose: Basal cell carcinoma is one of the most common malignant skin lesions. Automated lesion identification and classification using image processing techniques is highly required to reduce the diagnosis errors. Methods: In this study, a novel technique is applied to classify skin lesion images into two classes, namely the malignant Basal cell carcinoma and the benign nevus. A hybrid combination of bi-dimensional empirical mode decomposition and gray-level difference method features is proposed after hair removal...
December 2017: Health Information Science and Systems
Xiaoqiang Yang
The aim of the present study was to identify risk genes in myocardial infarction. Microarray data GSE34198, containing data from the peripheral blood of 49 myocardial infarction samples and 48 corresponding control samples, were downloaded from the Gene Expression Omnibus database to screen the differentially expressed genes (DEGs). The DEGs were used to construct a protein‑protein interaction (PPI) network of patient samples, from which the feature genes were identified using the neighboring score method...
November 14, 2017: Molecular Medicine Reports
Kuan-Chien Tseng, Yi-Fan Chiang-Hsieh, Hsuan Pai, Chi-Nga Chow, Shu-Chuan Lee, Han-Qin Zheng, Po-Li Kuo, Guan-Zhen Li, Yu-Cheng Hung, Na-Sheng Lin, Wen-Chi Chang
Motivation: MicroRNAs (miRNAs) are endogenous non-coding small RNAs (of about 22 nucleotides), which play an important role in the post-transcriptional regulation of gene expression via either mRNA cleavage or translation inhibition. Several machine learning-based approaches have been developed to identify novel miRNAs from next generation sequencing (NGS) data. Typically, precursor/genomic sequences are required as references for most methods. However, the non-availability of genomic sequences is often a limitation in miRNA discovery in non-model plants...
November 9, 2017: Bioinformatics
Zhichao Feng, Pengfei Rong, Peng Cao, Qingyu Zhou, Wenwei Zhu, Zhimin Yan, Qianyun Liu, Wei Wang
OBJECTIVE: To evaluate the diagnostic performance of machine-learning based quantitative texture analysis of CT images to differentiate small (≤ 4 cm) angiomyolipoma without visible fat (AMLwvf) from renal cell carcinoma (RCC). METHODS: This single-institutional retrospective study included 58 patients with pathologically proven small renal mass (17 in AMLwvf and 41 in RCC groups). Texture features were extracted from the largest possible tumorous regions of interest (ROIs) by manual segmentation in preoperative three-phase CT images...
November 13, 2017: European Radiology
Chip M Lynch, Behnaz Abdollahi, Joshua D Fuqua, Alexandra R de Carlo, James A Bartholomai, Rayeanne N Balgemann, Victor H van Berkel, Hermann B Frieboes
Outcomes for cancer patients have been previously estimated by applying various machine learning techniques to large datasets such as the Surveillance, Epidemiology, and End Results (SEER) program database. In particular for lung cancer, it is not well understood which types of techniques would yield more predictive information, and which data attributes should be used in order to determine this information. In this study, a number of supervised learning techniques is applied to the SEER database to classify lung cancer patients in terms of survival, including linear regression, Decision Trees, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), and a custom ensemble...
December 2017: International Journal of Medical Informatics
Anton O Oliynyk, Lawrence A Adutwum, Brent W Rudyk, Harshil Pisavadia, Sogol Lotfi, Viktor Hlukhyy, James J Harynuk, Arthur Mar, Jakoah Brgoch
A method to predict the crystal structure of equiatomic ternary compositions based only on the constituent elements was developed using cluster resolution feature selection (CR-FS) and support vector machine (SVM) classification. The supervised machine-learning model was first trained with 1037 individual compounds that adopt the most populated ternary 1:1:1 structure types (TiNiSi-, ZrNiAl-, PbFCl-, LiGaGe-, YPtAs-, UGeTe-, and LaPtSi-type), and then validated using an additional 519 compounds. The CR-FS algorithm improves class discrimination and indicates that 113 variables including size, electronegativity, number of valence electrons, and position on the periodic table (group number) influence the structure preference...
November 13, 2017: Journal of the American Chemical Society
Nafissa Sadi-Ahmed, Baya Kacha, Hamza Taleb, Malika Kedir-Talha
In this study, we proposed an approach able to predict whether a pregnant woman with contractions would give birth earlier than expected (i.e., before the 37 (t h) week of gestation (WG)). It only processes non-invasive electrohysterographic (EHG) signals fully automatically without assistance of an expert or an additional medical system. We used term and preterm EHG signals of 30-minutes duration collected between the 27 (t h) and the 32 (n d) WG. Preterm deliveries (< 37W G) had occurred in average 4...
November 11, 2017: Journal of Medical Systems
Gaurav Nanda, Kirsten Vallmuur, Mark Lehto
INTRODUCTION: Classical Machine Learning (ML) models have been found to assign the external-cause-of-injury codes (E-codes) based on injury narratives with good overall accuracy but often struggle with rare categories, primarily due to lack of enough training cases and heavily skewed nature of injurdata. In this paper, we have: a) studied the effect of increasing the size of training data on the prediction performance of three classical ML models: Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM) and Logistic Regression (LR), and b) studied the effect of filtering based on prediction strength of LR model when the model is trained on very-small (10,000 cases) and very-large (450,000 cases) training sets...
November 8, 2017: Accident; Analysis and Prevention
Xun Liu, Ningshan Li, Linsheng Lv, Yongmei Fu, Cailian Cheng, Caixia Wang, Yuqiu Ye, Shaomin Li, Tanqi Lou
BACKGROUND: Accurate assessment of kidney function is clinically important, but estimates of glomerular filtration rate (GFR) by regression are imprecise. METHODS: We hypothesized that ensemble learning could improve precision. A total of 1419 participants were enrolled, with 1002 in the development dataset and 417 in the external validation dataset. GFR was independently estimated from age, sex and serum creatinine using an artificial neural network (ANN), support vector machine (SVM), regression, and ensemble learning...
November 9, 2017: Journal of Translational Medicine
Noor Kamal Al-Qazzaz, Sawal Hamid Bin Mohd Ali, Siti Anom Ahmad, Mohd Shabiul Islam, Javier Escudero
Stroke survivors are more prone to developing cognitive impairment and dementia. Dementia detection is a challenge for supporting personalized healthcare. This study analyzes the electroencephalogram (EEG) background activity of 5 vascular dementia (VaD) patients, 15 stroke-related patients with mild cognitive impairment (MCI), and 15 control healthy subjects during a working memory (WM) task. The objective of this study is twofold. First, it aims to enhance the discrimination of VaD, stroke-related MCI patients, and control subjects using fuzzy neighborhood preserving analysis with QR-decomposition (FNPAQR); second, it aims to extract and investigate the spectral features that characterize the post-stroke dementia patients compared to the control subjects...
November 8, 2017: Medical & Biological Engineering & Computing
Benjamin Wittevrongel, Elia Van Wolputte, Marc M Van Hulle
When encoding visual targets using various lagged versions of a pseudorandom binary sequence of luminance changes, the EEG signal recorded over the viewer's occipital pole exhibits so-called code-modulated visual evoked potentials (cVEPs), the phase lags of which can be tied to these targets. The cVEP paradigm has enjoyed interest in the brain-computer interfacing (BCI) community for the reported high information transfer rates (ITR, in bits/min). In this study, we introduce a novel decoding algorithm based on spatiotemporal beamforming, and show that this algorithm is able to accurately identify the gazed target...
November 8, 2017: Scientific Reports
Wei Deng, Liangping Luo, Xiaoyi Lin, Tianqi Fang, Dexiang Liu, Guo Dan, Hanwei Chen
Objective: We aimed to propose an automatic method based on Support Vector Machine (SVM) and Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to segment the tumor lesions of head and neck cancer (HNC). Materials and Methods: 120 DCE-MRI samples were collected. Five curve features and two principal components of the normalized time-intensity curve (TIC) in 80 samples were calculated as the dataset in training three SVM classifiers. The other 40 samples were used as the testing dataset...
2017: Contrast Media & Molecular Imaging
Abdulmajid Murad, Jae-Young Pyun
Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs) address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data...
November 6, 2017: Sensors
Leili Shahriyari
One of the main challenges in machine learning (ML) is choosing an appropriate normalization method. Here, we examine the effect of various normalization methods on analyzing FPKM upper quartile (FPKM-UQ) RNA sequencing data sets. We collect the HTSeq-FPKM-UQ files of patients with colon adenocarcinoma from TCGA-COAD project. We compare three most common normalization methods: scaling, standardizing using z-score and vector normalization by visualizing the normalized data set and evaluating the performance of 12 supervised learning algorithms on the normalized data set...
November 3, 2017: Briefings in Bioinformatics
Fadhl M Alkawaa, Kumardeep Chaudhary, Lana X Garmire
Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if the deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+) and 67 negative estrogen receptor (ER-), to test the accuracies of autoencoder, a deep learning (DL) framework, as well as six widely used machine learning models, namely Random Forest (RF), Support Vector Machines (SVM), Recursive Partitioning and Regression Trees (RPART), Linear Discriminant Analysis (LDA), Prediction Analysis for Microarrays (PAM), and Generalized Boosted Models (GBM)...
November 7, 2017: Journal of Proteome Research
X S Zhao, L L Bao, Q Ning, J C Ji, X W Zhao
The discovery of biomarkers from high-dimensional data is a very challenging task in cancer diagnoses. On the one hand, biomarker discovery is the so-called high-dimensional small-sample problem. On the other hand, these data are redundant and noisy. In recent years, biomarker discovery from high-throughput biological data has become an increasingly important emerging topic in the field of bioinformatics. In this study, we propose a binary differential evolution algorithm for feature selection. Firstly, we suggest using a two-stage approach, where three filter methods including the Fisher score, T-statistics, and Information gain are used to generate the feature pool for input to differential evolution (DE)...
November 6, 2017: Molecular Informatics
Hancan Zhu, Guanghua He, Ze Wang
Arterial spin-labeling (ASL) perfusion MRI is a non-invasive method for quantifying cerebral blood flow (CBF). Standard ASL CBF calibration mainly relies on pair-wise subtraction of the spin-labeled images and controls images at each voxel separately, ignoring the abundant spatial correlations in ASL data. To address this issue, we previously proposed a multivariate support vector machine (SVM) learning-based algorithm for ASL CBF quantification (SVMASLQ). But the original SVMASLQ was designed to do CBF quantification for all image voxels simultaneously, which is not ideal for considering local signal and noise variations...
November 6, 2017: Medical & Biological Engineering & Computing
Xiao-Dong Huang, Chun-Yan Wang, Xin-Min Fan, Jin-Liang Zhang, Chun Yang, Zhen-Di Wang
Developing an accurate, rapid and economic oil source recognition method is essential for water recourses protection. Concentration-synchronous-matrix-fluorescence (CSMF) spectroscopy combined with 2D wavelet packet and probabilistic neural network (PNN) was proposed for source recognition of crude oil and petroleum products samples in this study. 2D wavelet packet was used to extract wavelet packet coefficients as the feature vectors from CSMF contour image and four algorithms, Back-propagation (BP) neural network, Radial based function neural network (RBFNN), Support vector Machine (SVM) and probabilistic neural network (PNN) were carried out for pattern recognition...
November 2, 2017: Science of the Total Environment
Shengli Zhang, Xin Duan
Predicting protein subcellular location with support vector machine has been a popular research area recently because of the dramatic explosion of bioinformation. Though substantial achievements have been obtained, few researchers considered the problem of data imbalance before classification, which will lead to low accuracy for some categories. So in this work, we combined oversampling method with SVM to deal with the protein subcellular localization of unbalanced data sets. To capture valuable information of a protein, a PseAAC (Pseudo Amino Acid Composition) has been extracted from PSSM(Position-Specific Scoring Matrix) as a feature vector, and then be selected by principal component analysis (PCA)...
October 31, 2017: Journal of Theoretical Biology
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