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https://www.readbyqxmd.com/read/28292249/a-feature-and-algorithm-selection-method-for-improving-the-prediction-of-protein-structural-class
#1
Qianwu Ni, Lei Chen
Correct prediction of protein structural class is beneficial to investigation on protein functions, regulations and interactions. In recent years, several computational methods have been proposed in this regard. In this study, a feature and algorithm selection method was presented for improving the accuracy of protein structural class prediction. The amino acid compositions and physiochemical features were adopted to represent features and thirty-eight machine learning algorithms collected in Weka were employed...
March 14, 2017: Combinatorial Chemistry & High Throughput Screening
https://www.readbyqxmd.com/read/28269470/can-we-make-a-carpet-smart-enough-to-detect-falls
#2
Fadi Muheidat, Harry W Tyrer
In this paper, we have enhanced smart carpet, which is a floor based personnel detector system, to detect falls using a faster but low cost processor. Our hardware front end reads 128 sensors, with sensors output a voltage due to a person walking or falling on the carpet. The processor is Jetson TK1, which provides more computing power than before. We generated a dataset with volunteers who walked and fell to test our algorithms. Data obtained allowed examining data frames (a frame is a single scan of the carpet sensors) read from the data acquisition system...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28254089/determinants-and-development-of-a-web-based-child-mortality-prediction-model-in-resource-limited-settings-a-data-mining-approach
#3
Brook Tesfaye, Suleman Atique, Noah Elias, Legesse Dibaba, Syed-Abdul Shabbir, Mihiretu Kebede
BACKGROUND: Improving child health and reducing child mortality rate are key health priorities in developing countries. This study aimed to identify determinant sand develop, a web-based child mortality prediction model in Ethiopian local language using classification data mining algorithm. METHODS: Decision tree (using J48 algorithm) and rule induction (using PART algorithm) techniques were applied on 11,654 records of Ethiopian demographic and health survey data...
March 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28227720/can-we-make-a-carpet-smart-enough-to-detect-falls
#4
Fadi Muheidat, Harry W Tyrer, Fadi Muheidat, Harry W Tyrer, Harry W Tyrer, Fadi Muheidat
In this paper, we have enhanced smart carpet, which is a floor based personnel detector system, to detect falls using a faster but low cost processor. Our hardware front end reads 128 sensors, with sensors output a voltage due to a person walking or falling on the carpet. The processor is Jetson TK1, which provides more computing power than before. We generated a dataset with volunteers who walked and fell to test our algorithms. Data obtained allowed examining data frames (a frame is a single scan of the carpet sensors) read from the data acquisition system...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28220414/quantitative-analysis-of-ligand-induced-endocytosis-of-flagellin-sensing-2-using-automated-image-segmentation
#5
Michelle E Leslie, Antje Heese
Plants are equipped with a suite of plant pattern recognition receptors (PRRs) that must be properly trafficked to and from the plasma membrane (PM), which serves as the host-pathogen interface, for robust detection of invading pathogenic microbes. Recognition of bacterial flagellin, or the derived peptide flg22, is facilitated by the PM-localized PRR, FLAGELLIN SENSING 2 (FLS2). Upon flg22 binding, FLS2 is rapidly internalized from the PM into endosomal compartments and subsequently degraded. To understand better the integration of FLS2 endocytosis and signaling outputs, we developed methods for the quantitative analysis of FLS2 trafficking using freely available bioimage informatic tools...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28072829/predicting-the-metabolic-sites-by-flavin-containing-monooxygenase-on-drug-molecules-using-svm-classification-on-computed-quantum-mechanics-and-circular-fingerprints-molecular-descriptors
#6
Chien-Wei Fu, Thy-Hou Lin
As an important enzyme in Phase I drug metabolism, the flavin-containing monooxygenase (FMO) also metabolizes some xenobiotics with soft nucleophiles. The site of metabolism (SOM) on a molecule is the site where the metabolic reaction is exerted by an enzyme. Accurate prediction of SOMs on drug molecules will assist the search for drug leads during the optimization process. Here, some quantum mechanics features such as the condensed Fukui function and attributes from circular fingerprints (called Molprint2D) are computed and classified using the support vector machine (SVM) for predicting some potential SOMs on a series of drugs that can be metabolized by FMO enzymes...
2017: PloS One
https://www.readbyqxmd.com/read/28048279/su-c-207b-05-tissue-segmentation-of-computed-tomography-images-using-a-random-forest-algorithm-a-feasibility-study
#7
D Polan, S Brady, R Kaufman
PURPOSE: Develop an automated Random Forest algorithm for tissue segmentation of CT examinations. METHODS: Seven materials were classified for segmentation: background, lung/internal gas, fat, muscle, solid organ parenchyma, blood/contrast, and bone using Matlab and the Trainable Weka Segmentation (TWS) plugin of FIJI. The following classifier feature filters of TWS were investigated: minimum, maximum, mean, and variance each evaluated over a pixel radius of 2n, (n = 0-4)...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/27989606/improving-diagnostic-recognition-of-primary-hyperparathyroidism-with-machine-learning
#8
Yash R Somnay, Mark Craven, Kelly L McCoy, Sally E Carty, Tracy S Wang, Caprice C Greenberg, David F Schneider
BACKGROUND: Parathyroidectomy offers the only cure for primary hyperparathyroidism, but today only 50% of primary hyperparathyroidism patients are referred for operation, in large part, because the condition is widely under-recognized. The diagnosis of primary hyperparathyroidism can be especially challenging with mild biochemical indices. Machine learning is a collection of methods in which computers build predictive algorithms based on labeled examples. With the aim of facilitating diagnosis, we tested the ability of machine learning to distinguish primary hyperparathyroidism from normal physiology using clinical and laboratory data...
December 15, 2016: Surgery
https://www.readbyqxmd.com/read/27854309/qsar-study-for-carcinogenic-potency-of-aromatic-amines-based-on-gep-and-mlps
#9
Fucheng Song, Anling Zhang, Hui Liang, Lianhua Cui, Wenlian Li, Hongzong Si, Yunbo Duan, Honglin Zhai
A new analysis strategy was used to classify the carcinogenicity of aromatic amines. The physical-chemical parameters are closely related to the carcinogenicity of compounds. Quantitative structure activity relationship (QSAR) is a method of predicting the carcinogenicity of aromatic amine, which can reveal the relationship between carcinogenicity and physical-chemical parameters. This study accessed gene expression programming by APS software, the multilayer perceptrons by Weka software to predict the carcinogenicity of aromatic amines, respectively...
November 15, 2016: International Journal of Environmental Research and Public Health
https://www.readbyqxmd.com/read/27730017/harnessing-ontology-and-machine-learning-for-rso-classification
#10
Bin Liu, Li Yao, Dapeng Han
Classification is an important part of resident space objects (RSOs) identification, which is a main focus of space situational awareness. Owing to the absence of some features caused by the limited and uncertain observations, RSO classification remains a difficult task. In this paper, an ontology for RSO classification named OntoStar is built upon domain knowledge and machine learning rules. Then data describing RSO are represented by OntoStar. A demo shows how an RSO is classified based on OntoStar. It is also shown in the demo that traceable and comprehensible reasons for the classification can be given, hence the classification can be checked and validated...
2016: SpringerPlus
https://www.readbyqxmd.com/read/27706661/locally-linear-embedding-and-neighborhood-rough-set-based-gene-selection-for-gene-expression-data-classification
#11
L Sun, J-C Xu, W Wang, Y Yin
Cancer subtype recognition and feature selection are important problems in the diagnosis and treatment of tumors. Here, we propose a novel gene selection approach applied to gene expression data classification. First, two classical feature reduction methods including locally linear embedding (LLE) and rough set (RS) are summarized. The advantages and disadvantages of these algorithms were analyzed and an optimized model for tumor gene selection was developed based on LLE and neighborhood RS (NRS). Bhattacharyya distance was introduced to delete irrelevant genes, pair-wise redundant analysis was performed to remove strongly correlated genes, and the wavelet soft threshold was determined to eliminate noise in the gene datasets...
August 30, 2016: Genetics and Molecular Research: GMR
https://www.readbyqxmd.com/read/27665113/hypergraph-based-feature-selection-technique-for-medical-diagnosis
#12
Nivethitha Somu, M R Gauthama Raman, Kannan Kirthivasan, V S Shankar Sriram
The impact of internet and information systems across various domains have resulted in substantial generation of multidimensional datasets. The use of data mining and knowledge discovery techniques to extract the original information contained in the multidimensional datasets play a significant role in the exploitation of complete benefit provided by them. The presence of large number of features in the high dimensional datasets incurs high computational cost in terms of computing power and time. Hence, feature selection technique has been commonly used to build robust machine learning models to select a subset of relevant features which projects the maximal information content of the original dataset...
November 2016: Journal of Medical Systems
https://www.readbyqxmd.com/read/27662651/sequence-based-prediction-of-antioxidant-proteins-using-a-classifier-selection-strategy
#13
Lina Zhang, Chengjin Zhang, Rui Gao, Runtao Yang, Qing Song
Antioxidant proteins perform significant functions in maintaining oxidation/antioxidation balance and have potential therapies for some diseases. Accurate identification of antioxidant proteins could contribute to revealing physiological processes of oxidation/antioxidation balance and developing novel antioxidation-based drugs. In this study, an ensemble method is presented to predict antioxidant proteins with hybrid features, incorporating SSI (Secondary Structure Information), PSSM (Position Specific Scoring Matrix), RSA (Relative Solvent Accessibility), and CTD (Composition, Transition, Distribution)...
2016: PloS One
https://www.readbyqxmd.com/read/27649187/multimodal-learning-and-intelligent-prediction-of-symptom-development-in-individual-parkinson-s-patients
#14
Andrzej W Przybyszewski, Mark Kon, Stanislaw Szlufik, Artur Szymanski, Piotr Habela, Dariusz M Koziorowski
We still do not know how the brain and its computations are affected by nerve cell deaths and their compensatory learning processes, as these develop in neurodegenerative diseases (ND). Compensatory learning processes are ND symptoms usually observed at a point when the disease has already affected large parts of the brain. We can register symptoms of ND such as motor and/or mental disorders (dementias) and even provide symptomatic relief, though the structural effects of these are in most cases not yet understood...
September 14, 2016: Sensors
https://www.readbyqxmd.com/read/27577447/unlocking-data-for-statistical-analyses-and-data-mining-generic-case-extraction-of-clinical-items-from-i2b2-and-transmart
#15
Daniel Firnkorn, Sebastian Merker, Matthias Ganzinger, Thomas Muley, Petra Knaup
In medical science, modern IT concepts are increasingly important to gather new findings out of complex diseases. Data Warehouses (DWH) as central data repository systems play a key role by providing standardized, high-quality and secure medical data for effective analyses. However, DWHs in medicine must fulfil various requirements concerning data privacy and the ability to describe the complexity of (rare) disease phenomena. Here, i2b2 and tranSMART are free alternatives representing DWH solutions especially developed for medical informatics purposes...
2016: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/27554131/gene-polymorphisms-as-a-predictor-of-body-weight-loss-after-roux-en-y-gastric-bypass-surgery-among-obese-women
#16
Patrícia Fátima Souza Novais, Thabata Koester Weber, Ney Lemke, Rozangela Verlengia, Alex Harley Crisp, Irineu Rasera-Junior, Maria Rita Marques de Oliveira
This study aimed to investigate the association between twelve gene polymorphisms and body weight loss, 12 months after Roux-en-Y gastric bypass (RYGB) surgery. Three hundred and fifty-one obese women participated in this study. The statistical software WEKA was used to identify which gene polymorphisms were potential predictors of postoperative percentage of excess weight loss (%EWL). Our results indicate that the only gene polymorphism that predicted %EWL was rs3813929, which is related to the serotonin receptor gene (5-HT2C)...
November 2016: Obesity Research & Clinical Practice
https://www.readbyqxmd.com/read/27530679/tissue-segmentation-of-computed-tomography-images-using-a-random-forest-algorithm-a-feasibility-study
#17
Daniel F Polan, Samuel L Brady, Robert A Kaufman
There is a need for robust, fully automated whole body organ segmentation for diagnostic CT. This study investigates and optimizes a Random Forest algorithm for automated organ segmentation; explores the limitations of a Random Forest algorithm applied to the CT environment; and demonstrates segmentation accuracy in a feasibility study of pediatric and adult patients. To the best of our knowledge, this is the first study to investigate a trainable Weka segmentation (TWS) implementation using Random Forest machine-learning as a means to develop a fully automated tissue segmentation tool developed specifically for pediatric and adult examinations in a diagnostic CT environment...
September 7, 2016: Physics in Medicine and Biology
https://www.readbyqxmd.com/read/27498205/applying-data-mining-techniques-to-improve-breast-cancer-diagnosis
#18
Joana Diz, Goreti Marreiros, Alberto Freitas
In the field of breast cancer research, and more than ever, new computer aided diagnosis based systems have been developed aiming to reduce diagnostic tests false-positives. Within this work, we present a data mining based approach which might support oncologists in the process of breast cancer classification and diagnosis. The present study aims to compare two breast cancer datasets and find the best methods in predicting benign/malignant lesions, breast density classification, and even for finding identification (mass / microcalcification distinction)...
September 2016: Journal of Medical Systems
https://www.readbyqxmd.com/read/27494993/computational-strategies-to-explore-antimalarial-thiazine-alkaloid-lead-compounds-based-on-an-australian-marine-sponge-plakortis-lita
#19
Lilly Aswathy, Radhakrishnan S Jisha, Vijay H Masand, Jayant M Gajbhiye, Indira G Shibi
In this work, an attempt was made to propose new leads based on the natural scaffold Thiaplakortone-A active against malaria. The 2D QSAR studies suggested that three descriptors correlate with the anti-malarial activity with an R(2) value of 0.814. Robustness, reliability, and predictive power of the model were tested by internal validation, external validation, Y-scrambling, and applicability domain analysis. HQSAR studies were carried out as an additional tool to find the sub-structural fingerprints. The CoMFA and CoMSIA models gave Q(2) values of 0...
August 22, 2016: Journal of Biomolecular Structure & Dynamics
https://www.readbyqxmd.com/read/27473741/artificial-neural-network-approach-in-laboratory-test-reporting-%C3%A2-learning-algorithms
#20
Ferhat Demirci, Pinar Akan, Tuncay Kume, Ali Riza Sisman, Zubeyde Erbayraktar, Suleyman Sevinc
OBJECTIVES: In the field of laboratory medicine, minimizing errors and establishing standardization is only possible by predefined processes. The aim of this study was to build an experimental decision algorithm model open to improvement that would efficiently and rapidly evaluate the results of biochemical tests with critical values by evaluating multiple factors concurrently. METHODS: The experimental model was built by Weka software (Weka, Waikato, New Zealand) based on the artificial neural network method...
August 2016: American Journal of Clinical Pathology
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