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https://www.readbyqxmd.com/read/28531339/limtox-a-web-tool-for-applied-text-mining-of-adverse-event-and-toxicity-associations-of-compounds-drugs-and-genes
#1
Andres Cañada, Salvador Capella-Gutierrez, Obdulia Rabal, Julen Oyarzabal, Alfonso Valencia, Martin Krallinger
A considerable effort has been devoted to retrieve systematically information for genes and proteins as well as relationships between them. Despite the importance of chemical compounds and drugs as a central bio-entity in pharmacological and biological research, only a limited number of freely available chemical text-mining/search engine technologies are currently accessible. Here we present LimTox (Literature Mining for Toxicology), a web-based online biomedical search tool with special focus on adverse hepatobiliary reactions...
May 22, 2017: Nucleic Acids Research
https://www.readbyqxmd.com/read/28531125/characterizing-dynamic-walking-patterns-and-detecting-falls-with-wearable-sensors-using-gaussian-process-methods
#2
Taehwan Kim, Jeongho Park, Seongman Heo, Keehoon Sung, Jooyoung Park
By incorporating a growing number of sensors and adopting machine learning technologies, wearable devices have recently become a prominent health care application domain. Among the related research topics in this field, one of the most important issues is detecting falls while walking. Since such falls may lead to serious injuries, automatically and promptly detecting them during daily use of smartphones and/or smart watches is a particular need. In this paper, we investigate the use of Gaussian process (GP) methods for characterizing dynamic walking patterns and detecting falls while walking with built-in wearable sensors in smartphones and/or smartwatches...
May 20, 2017: Sensors
https://www.readbyqxmd.com/read/28530547/application-of-machine-learning-approaches-for-protein-protein-interactions-prediction
#3
Mengying Zhang, Qiang Su, Yi Lu, Manman Zhao, Bing Niu
BACKGROUND: Proteomics endeavors to study the structures, functions and interactions of proteins. Information of the protein-protein interactions (PPIs) helps to improve our knowledge of the functions and the 3D structures of proteins. Thus determining the PPIs is essential for the study of the proteomics. OBJECTIVE: In this review, in order to study the application of machine learning in predicting PPI, some machine learning approaches such as support vector machine (SVM), artificial neural networks (ANNs) and random forest (RF) were selected, and the examples of its applications in PPIs were listed...
May 22, 2017: Medicinal Chemistry
https://www.readbyqxmd.com/read/28529536/mid-infrared-spectroscopy-combined-with-chemometrics-to-detect-sclerotinia-stem-rot-on-oilseed-rape-brassica-napus-l-leaves
#4
Chu Zhang, Xuping Feng, Jian Wang, Fei Liu, Yong He, Weijun Zhou
BACKGROUND: Detection of plant diseases in a fast and simple way is crucial for timely disease control. Conventionally, plant diseases are accurately identified by DNA, RNA or serology based methods which are time consuming, complex and expensive. Mid-infrared spectroscopy is a promising technique that simplifies the detection procedure for the disease. Mid-infrared spectroscopy was used to identify the spectral differences between healthy and infected oilseed rape leaves. Two different sample sets from two experiments were used to explore and validate the feasibility of using mid-infrared spectroscopy in detecting Sclerotinia stem rot (SSR) on oilseed rape leaves...
2017: Plant Methods
https://www.readbyqxmd.com/read/28528295/a-machine-learning-graph-based-approach-for-3d-segmentation-of-bruch-s-membrane-opening-from-glaucomatous-sd-oct-volumes
#5
Mohammad Saleh Miri, Michael D Abràmoff, Young H Kwon, Milan Sonka, Mona K Garvin
Bruch's membrane opening-minimum rim width (BMO-MRW) is a recently proposed structural parameter which estimates the remaining nerve fiber bundles in the retina and is superior to other conventional structural parameters for diagnosing glaucoma. Measuring this structural parameter requires identification of BMO locations within spectral domain-optical coherence tomography (SD-OCT) volumes. While most automated approaches for segmentation of the BMO either segment the 2D projection of BMO points or identify BMO points in individual B-scans, in this work, we propose a machine-learning graph-based approach for true 3D segmentation of BMO from glaucomatous SD-OCT volumes...
May 6, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28528170/disparities-in-digital-reporting-of-illness-a-demographic-and-socioeconomic-assessment
#6
Samuel Henly, Gaurav Tuli, Sheryl Kluberg, Jared B Hawkins, Quynh Nguyen, Aranka Anema, Adyasha Maharana, John S Brownstein, Elaine O Nsoesie
Although digital reports of disease are currently used by public health officials for disease surveillance and decision making, little is known about environmental factors and compositional characteristics that may influence reporting patterns. The objective of this study is to quantify the association between climate, demographic and socio-economic factors on digital reporting of disease at the US county level. We reference approximately 1.5 million foodservice business reviews between 2004 and 2014, and use census data, machine learning methods and regression models to assess whether digital reporting of disease is associated with climate, socio-economic and demographic factors...
May 17, 2017: Preventive Medicine
https://www.readbyqxmd.com/read/28526878/a-data-mining-approach-using-cortical-thickness-for-diagnosis-and-characterization-of-essential-tremor
#7
J Ignacio Serrano, Juan P Romero, Ma Dolores Del Castillo, Eduardo Rocon, Elan D Louis, Julián Benito-León
Essential tremor (ET) is one of the most prevalent movement disorders. Being that it is a common disorder, its diagnosis is considered routine. However, misdiagnoses may occur regularly. Over the past decade, several studies have identified brain morphometric changes in ET, but these changes remain poorly understood. Here, we tested the informativeness of measuring cortical thickness for the purposes of ET diagnosis, applying feature selection and machine learning methods to a study sample of 18 patients with ET and 18 age- and sex-matched healthy control subjects...
May 19, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28525590/sdm-a-server-for-predicting-effects-of-mutations-on-protein-stability
#8
Arun Prasad Pandurangan, Bernardo Ochoa-Montaño, David B Ascher, Tom L Blundell
Here, we report a webserver for the improved SDM, used for predicting the effects of mutations on protein stability. As a pioneering knowledge-based approach, SDM has been highlighted as the most appropriate method to use in combination with many other approaches. We have updated the environment-specific amino-acid substitution tables based on the current expanded PDB (a 5-fold increase in information), and introduced new residue-conformation and interaction parameters, including packing density and residue depth...
May 19, 2017: Nucleic Acids Research
https://www.readbyqxmd.com/read/28525568/scenery-a-web-application-for-causal-network-reconstruction-from-cytometry-data
#9
Georgios Papoutsoglou, Giorgos Athineou, Vincenzo Lagani, Iordanis Xanthopoulos, Angelika Schmidt, Szabolcs Éliás, Jesper Tegnér, Ioannis Tsamardinos
Flow and mass cytometry technologies can probe proteins as biological markers in thousands of individual cells simultaneously, providing unprecedented opportunities for reconstructing networks of protein interactions through machine learning algorithms. The network reconstruction (NR) problem has been well-studied by the machine learning community. However, the potentials of available methods remain largely unknown to the cytometry community, mainly due to their intrinsic complexity and the lack of comprehensive, powerful and easy-to-use NR software implementations specific for cytometry data...
May 19, 2017: Nucleic Acids Research
https://www.readbyqxmd.com/read/28524769/machine-learning-for-epigenetics-and-future-medical-applications
#10
Lawrence B Holder, M Muksitul Haque, Michael K Skinner
Understanding epigenetic processes holds immense promise for medical applications. Advances in Machine Learning (ML) are critical to realize this promise. Previous studies used epigenetic data sets associated with the germline transmission of epigenetic transgenerational inheritance of disease and novel ML approaches to predict genome-wide locations of critical epimutations. A combination of Active Learning (ACL) and Imbalanced Class Learning (ICL) was used to address past problems with ML to develop a more efficient feature selection process and address the imbalance problem in all genomic data sets...
May 19, 2017: Epigenetics: Official Journal of the DNA Methylation Society
https://www.readbyqxmd.com/read/28522849/carcinopred-el-novel-models-for-predicting-the-carcinogenicity-of-chemicals-using-molecular-fingerprints-and-ensemble-learning-methods
#11
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/28522360/sex-with-no-regrets-how-sexual-reproduction-uses-a-no-regret-learning-algorithm-for-evolutionary-advantage
#12
Omer Edhan, Ziv Hellman, Dana Sherill-Rofe
The question of 'why sex' has long been a puzzle. The randomness of recombination, which potentially produces low fitness progeny, contradicts notions of fitness landscape hill climbing. We use the concept of evolution as an algorithm for learning unpredictable environments to provide a possible answer. While sex and asex both implement similar machine learning no-regret algorithms in the context of random samples that are small relative to a vast genotype space, the algorithm of sex constitutes a more efficient goal-directed walk through this space...
May 15, 2017: Journal of Theoretical Biology
https://www.readbyqxmd.com/read/28522086/correlates-of-sleep-quality-in-midlife-and-beyond-a-machine-learning-analysis
#13
Katherine A Kaplan, Prajesh P Hardas, Susan Redline, Jamie M Zeitzer
OBJECTIVES: In older adults, traditional metrics derived from polysomnography (PSG) are not well correlated with subjective sleep quality. Little is known about whether the association between PSG and subjective sleep quality changes with age, or whether quantitative electroencephalography (qEEG) is associated with sleep quality. Therefore, we examined the relationship between subjective sleep quality and objective sleep characteristics (standard PSG and qEEG) across middle to older adulthood...
June 2017: Sleep Medicine
https://www.readbyqxmd.com/read/28521821/intratumoral-and-peritumoral-radiomics-for-the-pretreatment-prediction-of-pathological-complete-response-to-neoadjuvant-chemotherapy-based-on-breast-dce-mri
#14
Nathaniel M Braman, Maryam Etesami, Prateek Prasanna, Christina Dubchuk, Hannah Gilmore, Pallavi Tiwari, Donna Pletcha, Anant Madabhushi
BACKGROUND: In this study, we evaluated the ability of radiomic textural analysis of intratumoral and peritumoral regions on pretreatment breast cancer dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). METHODS: A total of 117 patients who had received NAC were retrospectively analyzed. Within the intratumoral and peritumoral regions of T1-weighted contrast-enhanced MRI scans, a total of 99 radiomic textural features were computed at multiple phases...
May 18, 2017: Breast Cancer Research: BCR
https://www.readbyqxmd.com/read/28521616/a-10-gene-classifier-for-indeterminate-thyroid-nodules-development-and-multicenter-accuracy-study
#15
Hernan E Gonzalez, Jose R Martínez, Sergio Vargas, Antonieta Solar, Loreto Pamela Véliz, Francisco Cruz, Tatiana Arias, Soledad Loyola, Eleonora Horvath, Hernán Tala, Eufrosina Traipe, Manuel Meneses, Luis Marin, Nelson Wohllk, Rene Eduardo Diaz, Jesús Véliz, Pedro Pineda, Patricia Arroyo, Natalia Mena, Milagros Bracamonte, Giovanna Miranda, Elsa Bruce, Soledad Urra
BACKGROUND: In most of the world, diagnostic surgery remains as the most frequent approach for indeterminate thyroid cytology. Although several molecular tests are available for central-lab testing in the US, there are no available kits for local laboratory testing. The aim of this study was to develop a prototype in-vitro diagnostic (IVD) gene classifier for diagnosis of indeterminate thyroid cytology. METHODS: In a first stage, the expression of 18 genes was determined by qPCR in a broad histopathological spectrum of fresh tissue biopsies (114)...
May 18, 2017: Thyroid: Official Journal of the American Thyroid Association
https://www.readbyqxmd.com/read/28521503/meeting-report-for-synthetic-biology-for-natural-products-2017-the-interface-of-meta-genomics-machine-learning-and-natural-product-discovery
#16
Michael J Smanski, David Mead, Claes Gustafsson, Michael G Thomas
No abstract text is available yet for this article.
May 19, 2017: ACS Synthetic Biology
https://www.readbyqxmd.com/read/28521242/a-structured-latent-model-for-ovarian-carcinoma-subtyping-from-histopathology-slides
#17
Aïcha BenTaieb, Hector Li-Chang, David Huntsman, Ghassan Hamarneh
Accurate subtyping of ovarian carcinomas is an increasingly critical and often challenging diagnostic process. This work focuses on the development of an automatic classification model for ovarian carcinoma subtyping. Specifically, we present a novel clinically inspired contextual model for histopathology image subtyping of ovarian carcinomas. A whole slide image is modelled using a collection of tissue patches extracted at multiple magnifications. An efficient and effective feature learning strategy is used for feature representation of a tissue patch...
May 9, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28520598/imaging-plus-x-multimodal-models-of-neurodegenerative-disease
#18
Neil P Oxtoby, Daniel C Alexander
PURPOSE OF REVIEW: This article argues that the time is approaching for data-driven disease modelling to take centre stage in the study and management of neurodegenerative disease. The snowstorm of data now available to the clinician defies qualitative evaluation; the heterogeneity of data types complicates integration through traditional statistical methods; and the large datasets becoming available remain far from the big-data sizes necessary for fully data-driven machine-learning approaches...
May 16, 2017: Current Opinion in Neurology
https://www.readbyqxmd.com/read/28520235/high-dimensional-neural-network-potentials-for-complex-systems
#19
Jörg Behler
Modern simulation techniques have reached a level of maturity, which allows addressing a wide range of problems in chemistry and materials science. Unfortunately, the application of first principles methods with predictive power is still limited to rather small systems, and in spite of the rapid evolution of computer hardware no fundamental change of this situation can be expected. Consequently, to reach an atomic level understanding of complex systems, the development of more efficient but equally reliable atomistic potentials has received considerable attention in recent years...
May 18, 2017: Angewandte Chemie
https://www.readbyqxmd.com/read/28516319/quantitative-prediction-of-drug-side-effects-based-on-drug-related-features
#20
Yanqing Niu, Wen Zhang
MOTIVATION: Unexpected side effects of drugs are great concern in the drug development, and the identification of side effects is an important task. Recently, machine learning methods are proposed to predict the presence or absence of interested side effects for drugs, but it is difficult to make the accurate prediction for all of them. METHODS: In this paper, we transform side effect profiles of drugs as their quantitative scores, by summing up their side effects with weights...
May 17, 2017: Interdisciplinary Sciences, Computational Life Sciences
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