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https://www.readbyqxmd.com/read/28526968/computer-aided-diagnosis-of-lung-nodules-in-computed-tomography-by-using-phylogenetic-diversity-genetic-algorithm-and-svm
#1
Antonio Oseas de Carvalho Filho, Aristófanes Corrêa Silva, Anselmo Cardoso de Paiva, Rodolfo Acatauassú Nunes, Marcelo Gattass
Lung cancer is pointed as the major cause of death among patients with cancer throughout the world. This work is intended to develop a methodology for diagnosis of lung nodules using images from the Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). The proposed methodology uses image processing and pattern recognition techniques. In order to differentiate between the patterns of malignant and benign nodules, we used phylogenetic diversity by means of particular indexes, that are: intensive quadratic entropy, extensive quadratic entropy, average taxonomic distinctness, total taxonomic distinctness, and pure diversity indexes...
May 19, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28526841/organic-carbon-prediction-in-soil-cores-using-vnir-and-mir-techniques-in-an-alpine-landscape
#2
Xiaolin Jia, Songchao Chen, Yuanyuan Yang, Lianqing Zhou, Wu Yu, Zhou Shi
Diffuse reflectance spectroscopy (DRS), including visible and near-infrared (VNIR) and mid-infrared (MIR) radiation, is a rapid, accurate and cost-effective technique for estimating soil organic carbon (SOC). We examined 24 soil cores (0-100 cm) from the Sygera Mountains on the Qinghai-Tibet Plateau, considering field-moist intact VNIR, air-dried ground VNIR and air-dried ground MIR spectra at 5-cm intervals. Preprocessed spectra were used to predict the SOC in the soil cores using partial least squares regression (PLSR) and a support vector machine (SVM)...
May 19, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28526460/semi-supervised-medical-entity-recognition-a-study-on-spanish-and-swedish-clinical-corpora
#3
Alicia Pérez, Rebecka Weegar, Arantza Casillas, Koldo Gojenola, Maite Oronoz, Hercules Dalianis
OBJECTIVE: The goal of this study is to investigate entity recognition within Electronic Health Records (EHRs) focusing on Spanish and Swedish. Of particular importance is a robust representation of the entities. In our case, we utilized unsupervised methods to generate such representations. METHODS: The significance of this work stands on its experimental layout. The experiments were carried out under the same conditions for both languages. Several classification approaches were explored: maximum probability, CRF, Perceptron and SVM...
May 16, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28523350/comparison-of-a-radiomic-biomarker-with-volumetric-analysis-for-decoding-tumour-phenotypes-of-lung-adenocarcinoma-with-different-disease-specific-survival
#4
Mei Yuan, Yu-Dong Zhang, Xue-Hui Pu, Yan Zhong, Hai Li, Jiang-Fen Wu, Tong-Fu Yu
OBJECTIVES: To compare a multi-feature-based radiomic biomarker with volumetric analysis in discriminating lung adenocarcinomas with different disease-specific survival on computed tomography (CT) scans. METHODS: This retrospective study obtained institutional review board approval and was Health Insurance Portability and Accountability Act (HIPAA) compliant. Pathologically confirmed lung adenocarcinoma (n = 431) manifested as subsolid nodules on CT were identified...
May 18, 2017: European Radiology
https://www.readbyqxmd.com/read/28522849/carcinopred-el-novel-models-for-predicting-the-carcinogenicity-of-chemicals-using-molecular-fingerprints-and-ensemble-learning-methods
#5
Li Zhang, Haixin Ai, Wen Chen, Zimo Yin, Huan Hu, Junfeng Zhu, Jian Zhao, Qi Zhao, Hongsheng Liu
Carcinogenicity refers to a highly toxic end point of certain chemicals, and has become an important issue in the drug development process. In this study, three novel ensemble classification models, namely Ensemble SVM, Ensemble RF, and Ensemble XGBoost, were developed to predict carcinogenicity of chemicals using seven types of molecular fingerprints and three machine learning methods based on a dataset containing 1003 diverse compounds with rat carcinogenicity. Among these three models, Ensemble XGBoost is found to be the best, giving an average accuracy of 70...
May 18, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28515267/presurgical-thalamic-hubness-predicts-surgical-outcome-in-temporal-lobe-epilepsy
#6
Xiaosong He, Gaelle E Doucet, Dorian Pustina, Michael R Sperling, Ashwini D Sharan, Joseph I Tracy
OBJECTIVE: To characterize the presurgical brain functional architecture presented in patients with temporal lobe epilepsy (TLE) using graph theoretical measures of resting-state fMRI data and to test its association with surgical outcome. METHODS: Fifty-six unilateral patients with TLE, who subsequently underwent anterior temporal lobectomy and were classified as obtaining a seizure-free (Engel class I, n = 35) vs not seizure-free (Engel classes II-IV, n = 21) outcome at 1 year after surgery, and 28 matched healthy controls were enrolled...
May 17, 2017: Neurology
https://www.readbyqxmd.com/read/28513853/association-between-textural-and-morphological-tumor-indices-on-baseline-pet-ct-and-early-metabolic-response-on-interim-pet-ct-in-bulky-malignant-lymphomas
#7
Fayçal Ben Bouallègue, Yassine Al Tabaa, Marilyne Kafrouni, Guillaume Cartron, Fabien Vauchot, Denis Mariano-Goulart
PURPOSE: We investigated whether metabolic, textural and morphological tumoral indices evaluated on baseline PET-CT were predictive of early metabolic response on interim PET-CT in a cohort of patients with bulky Hodgkin and non-Hodgkin malignant lymphomas. METHODS: This retrospective study included 57 patients referred for initial PET-CT examination. In-house dedicated software was used to delineate tumor contours using a fixed 30% threshold of SUV max and then to compute tumoral metabolic parameters (SUV max, mean, peak, standard deviation, skewness and kurtosis, metabolic tumoral volume (MTV), total lesion glycolysis, and area under the curve of the cumulative histogram), textural parameters (Moran's and Geary's indices, energy, entropy, contrast, correlation derived from the gray-level co-occurrence matrix, area under the curve of the power spectral density, auto-correlation distance, and granularity), and shape parameters (surface, asphericity, convexity, surfacic extension, and 2D and 3D fractal dimensions)...
May 17, 2017: Medical Physics
https://www.readbyqxmd.com/read/28510428/statistical-analysis-and-prediction-of-covalent-ligand-targeted-cysteine-residues
#8
Weilin Zhang, Jianfeng Pei, Luhua Lai
Targeted covalent compounds or drugs have good potency as they can bind to a specific target for a long time with low doses. Most currently known covalent ligands were discovered by chance or by modifying existing non-covalent compounds to make them covalently attached to a nearby reactive residue. Computational methods for novel covalent ligand binding prediction are highly demanded. We performed statistical analysis on protein complexes with covalent ligands attached to cysteine residues. We found that covalent modified cysteine residues have unique features compared to those not attached to covalent ligands, including lower pKa, higher exposure and higher ligand binding affinity...
May 16, 2017: Journal of Chemical Information and Modeling
https://www.readbyqxmd.com/read/28508191/investigations-of-severity-level-measurements-for-diabetic-macular-oedema-using-machine-learning-algorithms
#9
S Murugeswari, R Sukanesh
BACKGROUND: The macula is an important part of the human visual system and is responsible for clear and colour vision. Macular oedema happens when fluid and protein deposit on or below the macula of the eye and cause the macula to thicken and swell. Normally, it occurs due to diabetes called diabetic macular oedema. Diabetic macular oedema (DME) is one of the main causes of visual impairment in patients. AIM: The aims of the present study are to detect and localize abnormalities in blood vessels with respect to macula in order to prevent vision loss for the diabetic patients...
May 15, 2017: Irish Journal of Medical Science
https://www.readbyqxmd.com/read/28504840/clinical-utility-of-a-short-resting-state-mri-scan-in-differentiating-bipolar-from-unipolar-depression
#10
M Li, T Das, W Deng, Q Wang, Y Li, L Zhao, X Ma, Y Wang, H Yu, X Li, Y Meng, L Palaniyappan, T Li
OBJECTIVE: Depression in bipolar disorder (BipD) requires a therapeutic approach that is from treating unipolar major depressive disorder (UniD), but to date, no reliable methods could separate these two disorders. The aim of this study was to establish the clinical validity and utility of a non-invasive functional MRI-based method to classify BipD from UniD. METHOD: The degree of connectivity (degree centrality or DC) of every small unit (voxel) with every other unit of the brain was estimated in 22 patients with BipD and 22 age, gender, and depressive severity-matched patients with UniD and 22 healthy controls...
May 15, 2017: Acta Psychiatrica Scandinavica
https://www.readbyqxmd.com/read/28504361/alterations-of-resting-state-fmri-measurements-in-individuals-with-cervical-dystonia
#11
Zhihao Li, Cecília N Prudente, Randall Stilla, K Sathian, H A Jinnah, Xiaoping Hu
Cervical dystonia (CD) is a neurological disorder with typical symptoms of involuntary and abnormal movements and postures of the head. CD-associated alterations of functional brain networks have not been well characterized. Previous studies of CD using resting-state functional MRI (rfMRI) are limited in two aspects: (i) the analyses were not directly focused on the functional brain network related to head movement and (ii) rfMRI measurements other than functional connectivity (FC) were not investigated. The present study examined alterations of FC in CD by capitalizing on newly identified brain regions supporting isometric head rotation (Prudente et al...
May 15, 2017: Human Brain Mapping
https://www.readbyqxmd.com/read/28501513/qsar-studies-of-the-bioactivity-of-hepatitis-c-virus-hcv-ns3-4a-protease-inhibitors-by-multiple-linear-regression-mlr-and-support-vector-machine-svm
#12
Zijian Qin, Maolin Wang, Aixia Yan
In this study, quantitative structure-activity relationship (QSAR) models using various descriptor sets and training/test set selection methods were explored to predict the bioactivity of hepatitis C virus (HCV) NS3/4A protease inhibitors by using a multiple linear regression (MLR) and a support vector machine (SVM) method. 512 HCV NS3/4A protease inhibitors and their IC50 values which were determined by the same FRET assay were collected from the reported literature to build a dataset. All the inhibitors were represented with selected nine global and 12 2D property-weighted autocorrelation descriptors calculated from the program CORINA Symphony...
May 3, 2017: Bioorganic & Medicinal Chemistry Letters
https://www.readbyqxmd.com/read/28497044/predicting-the-types-of-ion-channel-targeted-conotoxins-based-on-avc-svm-model
#13
Wang Xianfang, Wang Junmei, Wang Xiaolei, Zhang Yue
The conotoxin proteins are disulfide-rich small peptides. Predicting the types of ion channel-targeted conotoxins has great value in the treatment of chronic diseases, epilepsy, and cardiovascular diseases. To solve the problem of information redundancy existing when using current methods, a new model is presented to predict the types of ion channel-targeted conotoxins based on AVC (Analysis of Variance and Correlation) and SVM (Support Vector Machine). First, the F value is used to measure the significance level of the feature for the result, and the attribute with smaller F value is filtered by rough selection...
2017: BioMed Research International
https://www.readbyqxmd.com/read/28496502/the-effect-of-creative-tasks-on-electrocardiogram-using-linear-and-nonlinear-features-in-combination-with-classification-approaches
#14
Sahar Zakeri, Ataollah Abbasi, Ateke Goshvarpour
Objective: Interest in the subject of creativity and its impacts on human life is growing extensively. However, only a few surveys pay attention to the relation between creativity and physiological changes. This paper presents a novel approach to distinguish between creativity states from electrocardiogram signals. Nineteen linear and nonlinear features of the cardiac signal were extracted to detect creativity states. Method: ECG signals of 52 participants were recorded while doing three tasks of Torrance Tests of Creative Thinking (TTCT/ figural B)...
January 2017: Iranian Journal of Psychiatry
https://www.readbyqxmd.com/read/28494996/prediction-of-labor-onset-type-spontaneous-vs-induced-role-of-electrohysterography
#15
Jose Alberola-Rubio, Javier Garcia-Casado, Gema Prats-Boluda, Yiyao Ye-Lin, Domingo Desantes, Javier Valero, Alfredo Perales
BACKGROUND AND OBJECTIVE: Induction of labor (IOL) is a medical procedure used to initiate uterine contractions to achieve delivery. IOL entails medical risks and has a significant impact on both the mother's and newborn's well-being. The assistance provided by an automatic system to help distinguish patients that will achieve labor spontaneously from those that will need late-term IOL would help clinicians and mothers to take an informed decision about prolonging pregnancy. With this aim, we developed and evaluated predictive models using not only traditional obstetrical data but also electrophysiological parameters derived from the electrohysterogram (EHG)...
June 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28493868/vertical-ground-reaction-force-marker-for-parkinson-s-disease
#16
Md Nafiul Alam, Amanmeet Garg, Tamanna Tabassum Khan Munia, Reza Fazel-Rezai, Kouhyar Tavakolian
Parkinson's disease (PD) patients regularly exhibit abnormal gait patterns. Automated differentiation of abnormal gait from normal gait can serve as a potential tool for early diagnosis as well as monitoring the effect of PD treatment. The aim of current study is to differentiate PD patients from healthy controls, on the basis of features derived from plantar vertical ground reaction force (VGRF) data during walking at normal pace. The current work presents a comprehensive study highlighting the efficacy of different machine learning classifiers towards devising an accurate prediction system...
2017: PloS One
https://www.readbyqxmd.com/read/28492486/a-human-activity-recognition-system-based-on-dynamic-clustering-of-skeleton-data
#17
Alessandro Manzi, Paolo Dario, Filippo Cavallo
Human activity recognition is an important area in computer vision, with its wide range of applications including ambient assisted living. In this paper, an activity recognition system based on skeleton data extracted from a depth camera is presented. The system makes use of machine learning techniques to classify the actions that are described with a set of a few basic postures. The training phase creates several models related to the number of clustered postures by means of a multiclass Support Vector Machine (SVM), trained with Sequential Minimal Optimization (SMO)...
May 11, 2017: Sensors
https://www.readbyqxmd.com/read/28492483/pcvmzm-using-the-probabilistic-classification-vector-machines-model-combined-with-a-zernike-moments-descriptor-to-predict-protein-protein-interactions-from-protein-sequences
#18
Yanbin Wang, Zhuhong You, Xiao Li, Xing Chen, Tonghai Jiang, Jingting Zhang
Protein-protein interactions (PPIs) are essential for most living organisms' process. Thus, detecting PPIs is extremely important to understand the molecular mechanisms of biological systems. Although many PPIs data have been generated by high-throughput technologies for a variety of organisms, the whole interatom is still far from complete. In addition, the high-throughput technologies for detecting PPIs has some unavoidable defects, including time consumption, high cost, and high error rate. In recent years, with the development of machine learning, computational methods have been broadly used to predict PPIs, and can achieve good prediction rate...
May 11, 2017: International Journal of Molecular Sciences
https://www.readbyqxmd.com/read/28492480/detection-of-soil-nitrogen-using-near-infrared-sensors-based-on-soil-pretreatment-and-algorithms
#19
Pengcheng Nie, Tao Dong, Yong He, Fangfang Qu
Soil nitrogen content is one of the important growth nutrient parameters of crops. It is a prerequisite for scientific fertilization to accurately grasp soil nutrient information in precision agriculture. The information about nutrients such as nitrogen in the soil can be obtained quickly by using a near-infrared sensor. The data can be analyzed in the detection process, which is nondestructive and non-polluting. In order to investigate the effect of soil pretreatment on nitrogen content by near infrared sensor, 16 nitrogen concentrations were mixed with soil and the soil samples were divided into three groups with different pretreatment...
May 11, 2017: Sensors
https://www.readbyqxmd.com/read/28489045/discontinuity-detection-in-the-shield-metal-arc-welding-process
#20
José Alberto Naves Cocota, Gabriel Carvalho Garcia, Adilson Rodrigues da Costa, Milton Sérgio Fernandes de Lima, Filipe Augusto Santos Rocha, Gustavo Medeiros Freitas
This work proposes a new methodology for the detection of discontinuities in the weld bead applied in Shielded Metal Arc Welding (SMAW) processes. The detection system is based on two sensors-a microphone and piezoelectric-that acquire acoustic emissions generated during the welding. The feature vectors extracted from the sensor dataset are used to construct classifier models. The approaches based on Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers are able to identify with a high accuracy the three proposed weld bead classes: desirable weld bead, shrinkage cavity and burn through discontinuities...
May 10, 2017: Sensors
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