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https://www.readbyqxmd.com/read/29151135/classification-of-g-protein-coupled-receptors-based-on-a-rich-generation-of-convolutional-neural-network-n-gram-transformation-and-multiple-sequence-alignments
#1
Man Li, Cheng Ling, Qi Xu, Jingyang Gao
Sequence classification is crucial in predicting the function of newly discovered sequences. In recent years, the prediction of the incremental large-scale and diversity of sequences has heavily relied on the involvement of machine-learning algorithms. To improve prediction accuracy, these algorithms must confront the key challenge of extracting valuable features. In this work, we propose a feature-enhanced protein classification approach, considering the rich generation of multiple sequence alignment algorithms, N-gram probabilistic language model and the deep learning technique...
November 18, 2017: Amino Acids
https://www.readbyqxmd.com/read/29150140/visual-pathways-from-the-perspective-of-cost-functions-and-multi-task-deep-neural-networks
#2
REVIEW
H Steven Scholte, Max M Losch, Kandan Ramakrishnan, Edward H F de Haan, Sander M Bohte
Vision research has been shaped by the seminal insight that we can understand the higher-tier visual cortex from the perspective of multiple functional pathways with different goals. In this paper, we try to give a computational account of the functional organization of this system by reasoning from the perspective of multi-task deep neural networks. Machine learning has shown that tasks become easier to solve when they are decomposed into subtasks with their own cost function. We hypothesize that the visual system optimizes multiple cost functions of unrelated tasks and this causes the emergence of a ventral pathway dedicated to vision for perception, and a dorsal pathway dedicated to vision for action...
October 7, 2017: Cortex; a Journal Devoted to the Study of the Nervous System and Behavior
https://www.readbyqxmd.com/read/29149880/analytical-performance-of-envisia-a-genomic-classifier-for-usual-interstitial-pneumonia
#3
Yoonha Choi, Jiayi Lu, Zhanzhi Hu, Daniel G Pankratz, Huimin Jiang, Manqiu Cao, Cristina Marchisano, Jennifer Huiras, Grazyna Fedorowicz, Mei G Wong, Jessica R Anderson, Edward Y Tom, Joshua Babiarz, Urooj Imtiaz, Neil M Barth, P Sean Walsh, Giulia C Kennedy, Jing Huang
BACKGROUND: Clinical guidelines specify that diagnosis of interstitial pulmonary fibrosis (IPF) requires identification of usual interstitial pneumonia (UIP) pattern. While UIP can be identified by high resolution CT of the chest, the results are often inconclusive, making surgical lung biopsy necessary to reach a definitive diagnosis (Raghu et al., Am J Respir Crit Care Med 183(6):788-824, 2011). The Envisia genomic classifier differentiates UIP from non-UIP pathology in transbronchial biopsies (TBB), potentially allowing patients to avoid an invasive procedure (Brown et al...
November 17, 2017: BMC Pulmonary Medicine
https://www.readbyqxmd.com/read/29149847/individuals-explanations-for-their-persistent-or-recurrent-low-back-pain-a-cross-sectional-survey
#4
Jenny Setchell, Nathalia Costa, Manuela Ferreira, Joanna Makovey, Mandy Nielsen, Paul W Hodges
BACKGROUND: Most people experience low back pain (LBP), and it is often ongoing or recurrent. Contemporary research knowledge indicates individual's pain beliefs have a strong effect on their pain experience and management. This study's primary aim was to determine the discourses (patterns of thinking) underlying people's beliefs about what causes their LBP to persist. The secondary aim was to investigate what they believed was the source of this thinking. METHODS: We used a primarily qualitative survey design: 130 participants answered questions about what caused their LBP to persist, and where they learned about these causes...
November 17, 2017: BMC Musculoskeletal Disorders
https://www.readbyqxmd.com/read/29149684/combining-benford-s-law-and-machine-learning-to-detect-money-laundering-an-actual-spanish-court-case
#5
Elena Badal-Valero, José A Alvarez-Jareño, Jose M Pavía
OBJECTIVES: This paper is based on the analysis of the database of operations from a macro-case on money laundering orchestrated between a core company and a group of its suppliers, 26 of which had already been identified by the police as fraudulent companies. In the face of a well-founded suspicion that more companies have perpetrated criminal acts and in order to make better use of what are very limited police resources, we aim to construct a tool to detect money laundering criminals...
November 11, 2017: Forensic Science International
https://www.readbyqxmd.com/read/29149245/high-dimensional-therapeutic-inference-in-the-focally-damaged-human-brain
#6
Tianbo Xu, Hans Rolf Jäger, Masud Husain, Geraint Rees, Parashkev Nachev
Though consistency across the population renders the extraordinarily complex functional anatomy of the human brain surveyable, the inverse inference-from common functional maps to individual behaviour-is constrained by marked individual deviation from the population mean. Such inference is fundamental to the evaluation of therapeutic interventions in focal brain injury, where the impact of an induced structural change in the brain is quantified by its behavioural consequences, inevitably refracted through the lens of lesion-outcome relations...
November 15, 2017: Brain: a Journal of Neurology
https://www.readbyqxmd.com/read/29149087/taxonomic-classification-for-living-organisms-using-convolutional-neural-networks
#7
Saed Khawaldeh, Usama Pervaiz, Mohammed Elsharnoby, Alaa Eddin Alchalabi, Nayel Al-Zubi
Taxonomic classification has a wide-range of applications such as finding out more about evolutionary history. Compared to the estimated number of organisms that nature harbors, humanity does not have a thorough comprehension of to which specific classes they belong. The classification of living organisms can be done in many machine learning techniques. However, in this study, this is performed using convolutional neural networks. Moreover, a DNA encoding technique is incorporated in the algorithm to increase performance and avoid misclassifications...
November 17, 2017: Genes
https://www.readbyqxmd.com/read/29148090/assessment-of-cumulative-health-risk-in-the-world-trade-center-general-responder-cohort
#8
Ghalib A Bello, Susan L Teitelbaum, Roberto G Lucchini, Christopher R Dasaro, Moshe Shapiro, Julia R Kaplan, Michael A Crane, Denise J Harrison, Benjamin J Luft, Jacqueline M Moline, Iris G Udasin, Andrew C Todd
BACKGROUND: Multiple comorbidities have been reported among rescue/recovery workers responding to the 9/11/2001 WTC disaster. In this study, we developed an index that quantifies the cumulative physiological burden of comorbidities and predicts life expectancy in this cohort. METHODS: A machine learning approach (gradient boosting) was used to model the relationship between mortality and several clinical parameters (laboratory test results, blood pressure, pulmonary function measures)...
November 17, 2017: American Journal of Industrial Medicine
https://www.readbyqxmd.com/read/29147562/cognitive-computing-and-escience-in-health-and-life-science-research-artificial-intelligence-and-obesity-intervention-programs
#9
REVIEW
Thomas Marshall, Tiffiany Champagne-Langabeer, Darla Castelli, Deanna Hoelscher
Objective: To present research models based on artificial intelligence and discuss the concept of cognitive computing and eScience as disruptive factors in health and life science research methodologies. Methods: The paper identifies big data as a catalyst to innovation and the development of artificial intelligence, presents a framework for computer-supported human problem solving and describes a transformation of research support models. This framework includes traditional computer support; federated cognition using machine learning and cognitive agents to augment human intelligence; and a semi-autonomous/autonomous cognitive model, based on deep machine learning, which supports eScience...
December 2017: Health Information Science and Systems
https://www.readbyqxmd.com/read/29147555/what-can-machine-learning-do-for-antimicrobial-peptides-and-what-can-antimicrobial-peptides-do-for-machine-learning
#10
REVIEW
Ernest Y Lee, Michelle W Lee, Benjamin M Fulan, Andrew L Ferguson, Gerard C L Wong
Antimicrobial peptides (AMPs) are a diverse class of well-studied membrane-permeating peptides with important functions in innate host defense. In this short review, we provide a historical overview of AMPs, summarize previous applications of machine learning to AMPs, and discuss the results of our studies in the context of the latest AMP literature. Much work has been recently done in leveraging computational tools to design new AMP candidates with high therapeutic efficacies for drug-resistant infections...
December 6, 2017: Interface Focus
https://www.readbyqxmd.com/read/29147518/machine-learning-molecular-dynamics-for-the-simulation-of-infrared-spectra
#11
Michael Gastegger, Jörg Behler, Philipp Marquetand
Machine learning has emerged as an invaluable tool in many research areas. In the present work, we harness this power to predict highly accurate molecular infrared spectra with unprecedented computational efficiency. To account for vibrational anharmonic and dynamical effects - typically neglected by conventional quantum chemistry approaches - we base our machine learning strategy on ab initio molecular dynamics simulations. While these simulations are usually extremely time consuming even for small molecules, we overcome these limitations by leveraging the power of a variety of machine learning techniques, not only accelerating simulations by several orders of magnitude, but also greatly extending the size of systems that can be treated...
October 1, 2017: Chemical Science
https://www.readbyqxmd.com/read/29146692/prediction-of-90-y-radioembolization-outcome-from-pre-therapeutic-factors-with-random-survival-forests
#12
Michael Ingrisch, Franziska Schöppe, Karolin Johanna Paprottka, Matthias Fabritius, Frederik F Strobl, Enrico de Toni, Harun Ilhan, Andrei Todica, Marlies Michl, Philipp Paprottka
To predict outcome of (90)Y radioembolization (RE) in patients with intrahepatic tumors from pre-therapeutic baseline parameters and to identify predictive variables using a machine-learning approach based on random survival forests (RSF). Materials and Methods: In this retrospective study, 366 patients with primary (n = 92) or secondary (n = 274) liver tumors who had received (90)Y radioembolization were analyzed. A random survival forest was trained to predict individual risk from baseline values of cholinesterase (CHE), bilirubin, type of primary tumor, age at radioembolization, hepatic tumor burden, presence of extrahepatic disease (EHD) and sex...
November 16, 2017: Journal of Nuclear Medicine: Official Publication, Society of Nuclear Medicine
https://www.readbyqxmd.com/read/29146561/recurrent-neural-networks-with-specialized-word-embeddings-for-health-domain-named-entity-recognition
#13
Iñigo Jauregi Unanue, Ehsan Zare Borzeshi, Massimo Piccardi
BACKGROUND: Previous state-of-the-art systems on Drug Name Recognition (DNR) and Clinical Concept Extraction (CCE) have focused on a combination of text "feature engineering" and conventional machine learning algorithms such as conditional random fields and support vector machines. However, developing good features is inherently heavily time-consuming. Conversely, more modern machine learning approaches such as recurrent neural networks (RNNs) have proved capable of automatically learning effective features from either random assignments or automated word "embeddings"...
November 13, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/29146284/supervised-learning-techniques-and-their-ability-to-classify-a-change-of-direction-task-strategy-using-kinematic-and-kinetic-features
#14
Chris Richter, Enda King, Eanna Falvey, Andrew Franklyn-Miller
This study examines the ability of commonly used supervised learning techniques to classify the execution of a maximum effort change of direction task into predefined movement pattern as well as the influence of fuzzy executions and the impact of selected features (e.g. peak knee flexion) towards classification accuracy. The experiment utilized kinematic and kinetic data from 323 male subjects with chronic athletic groin pain. All subjects undertook a biomechanical assessment and had been divided previously into 3 different movement strategies in an earlier paper...
October 31, 2017: Journal of Biomechanics
https://www.readbyqxmd.com/read/29145893/normalization-of-the-microbiota-in-patients-after-treatment-for-colonic-lesions
#15
Marc A Sze, Nielson T Baxter, Mack T Ruffin, Mary A M Rogers, Patrick D Schloss
BACKGROUND: Colorectal cancer is a worldwide health problem. Despite growing evidence that members of the gut microbiota can drive tumorigenesis, little is known about what happens to it after treatment for an adenoma or carcinoma. This study tested the hypothesis that treatment for adenoma or carcinoma alters the abundance of bacterial populations associated with disease to those associated with a normal colon. We tested this hypothesis by sequencing the 16S rRNA genes in the feces of 67 individuals before and after treatment for adenoma (N = 22), advanced adenoma (N = 19), and carcinoma (N = 26)...
November 16, 2017: Microbiome
https://www.readbyqxmd.com/read/29144388/hyperspectral-image-enhancement-and-mixture-deep-learning-classification-of-corneal-epithelium-injuries
#16
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
https://www.readbyqxmd.com/read/29143960/predicting-frequent-emergency-department-use-among-children-with-epilepsy-a-retrospective-cohort-study-using-electronic-health-data-from-2-centers
#17
Zachary M Grinspan, Anup D Patel, Baria Hafeez, Erika L Abramson, Lisa M Kern
OBJECTIVE: Among children with epilepsy, to develop and evaluate a model to predict emergency department (ED) use, an indicator of poor disease control and/or poor access to care. METHODS: We used electronic health record data from 2013 to predict ED use in 2014 at 2 centers, benchmarking predictive performance against machine learning algorithms. We evaluated algorithms by calculating the expected yearly ED visits among the 5% highest risk individuals. We estimated the breakeven cost per patient per year for an intervention that reduced ED visits by 10%...
November 16, 2017: Epilepsia
https://www.readbyqxmd.com/read/29142808/analyzing-microtomography-data-with-python-and-the-scikit-image-library
#18
Emmanuelle Gouillart, Juan Nunez-Iglesias, Stéfan van der Walt
The exploration and processing of images is a vital aspect of the scientific workflows of many X-ray imaging modalities. Users require tools that combine interactivity, versatility, and performance. scikit-image is an open-source image processing toolkit for the Python language that supports a large variety of file formats and is compatible with 2D and 3D images. The toolkit exposes a simple programming interface, with thematic modules grouping functions according to their purpose, such as image restoration, segmentation, and measurements...
2017: Advanced Structural and Chemical Imaging
https://www.readbyqxmd.com/read/29142749/urine-metabolomics-as-a-predictor-of-patient-tolerance-and-response-to-adjuvant-chemotherapy-in-colorectal-cancer
#19
Mark A Dykstra, Noah Switzer, Roman Eisner, Victor Tso, Rae Foshaug, Kathleen Ismond, Richard Fedorak, Haili Wang
Colorectal cancer is the third leading cause of cancer-associated mortality in the western world. The ability to predict a patient's response to chemotherapy may be of great value for clinicians and patients when planning cancer treatment. The aim of the current study was to develop a urine metabolomics-based biomarker panel to predict adverse events and response to chemotherapy in patients with colorectal cancer. A retrospective chart review of patients diagnosed with stage III or IV colorectal cancer between 2008 and 2012 was performed...
November 2017: Molecular and Clinical Oncology
https://www.readbyqxmd.com/read/29142741/statistical-sleep-pattern-modelling-for-sleep-quality-assessment-based-on-sound-events
#20
Hongle Wu, Takafumi Kato, Masayuki Numao, Ken-Ichi Fukui
A good sleep is important for a healthy life. Recently, several consumer sleep devices have emerged on the market claiming that they can provide personal sleep monitoring; however, many of them require additional hardware or there is a lack of scientific evidence regarding their reliability. In this paper we proposed a novel method to assess the sleep quality through sound events recorded in the bedroom. We used subjective sleep quality as training label, combined several machine learning approaches including kernelized self organizing map, hierarchical clustering and hidden Markov model, obtained the models to indicate the sleep pattern of specific quality level...
December 2017: Health Information Science and Systems
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