keyword
MENU ▼
Read by QxMD icon Read
search

machining learning

keyword
https://www.readbyqxmd.com/read/28214930/investigation-of-optical-neuro-monitoring-technique-for-detection-of-maintenance-and-emergence-states-during-general-anesthesia
#1
Gabriela Hernandez-Meza, Meltem Izzetoglu, Mary Osbakken, Michael Green, Hawa Abubakar, Kurtulus Izzetoglu
The American Society of Anesthesiologist recommends peripheral physiological monitoring during general anesthesia, which offers no information regarding the effects of anesthetics on the brain. Since no "gold standard" method exists for this evaluation, such a technique is needed to ensure patient comfort, procedure quality and safety. In this study we investigated functional near infrared spectroscopy (fNIRS) as possible monitor of anesthetic effects on the prefrontal cortex. Anesthetic drugs, such as sevoflurane, suppress the cerebral metabolism and alter the cerebral blood flow...
February 18, 2017: Journal of Clinical Monitoring and Computing
https://www.readbyqxmd.com/read/28214535/multilevel-ensemble-model-for-prediction-of-iga-and-igg-antibodies
#2
Divya Khanna, Prashant Singh Rana
Identification of antigen for inducing specific class of antibody is prime objective in peptide based vaccine designs, immunodiagnosis, and antibody productions. It's urge to introduce a reliable system with high accuracy and efficiency for prediction. In the present study, a novel multilevel ensemble model is developed for prediction of antibodies IgG and IgA. Epitope length is important in training the model and it is efficient to use variable length of epitopes. In this ensemble approach, seven different machine learning models are combined to predict variable length of epitopes (4 to 50)...
February 15, 2017: Immunology Letters
https://www.readbyqxmd.com/read/28213145/how-are-you-feeling-a-personalized-methodology-for-predicting-mental-states-from-temporally-observable-physical-and-behavioral-information
#3
Suppawong Tuarob, Conrad S Tucker, Soundar Kumara, C Lee Giles, Aaron L Pincus, David E Conroy, Nilam Ram
It is believed that anomalous mental states such as stress and anxiety not only cause suffering for the individuals, but also lead to tragedies in some extreme cases. The ability to predict the mental state of an individual at both current and future time periods could prove critical to healthcare practitioners. Currently, the practical way to predict an individual's mental state is through mental examinations that involve psychological experts performing the evaluations. However, such methods can be time and resource consuming, mitigating their broad applicability to a wide population...
February 14, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28212880/automatic-detection-of-hypoxia-in-renal-tissue-stained-with-hif-alpha
#4
Aline Rodrigues Buzin, Nayana Damiani Macedo, Isabela Bastos Binotti Abreu De Araujo, Breno Valentim Nogueira, Tadeu Uggere De Andrade, Denise Coutinho Endringer, Dominik Lenz
OBJECTIVE: The objective of this study was the identification of the stain HIF-alpha using the Image Cytometry, and to help to count the positive cells (with HIF-alpha) and the negative cells (without HIF-alpha) from the same sample. METHOD: 17 images of renal tissues from male rats of Winstar lineage; overall, there were 12.587 objects (cells) in the images for analysis. The acquired images were then analyzed through the free softwares CellProfiler (version 2.1...
February 14, 2017: Journal of Immunological Methods
https://www.readbyqxmd.com/read/28212605/erratum-to-mirnacle-machine-learning-with-smote-and-random-forest-for-improving-selectivity-in-pre-mirna-ab-initio-prediction
#5
Yuri Bento Marques, Alcione de Paiva Oliveira, Ana Tereza Ribeiro Vasconcelos, Fabio Ribeiro Cerqueira
No abstract text is available yet for this article.
February 17, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28212101/fast-solving-quasi-optimal-ls-s-%C3%A2-vm-based-on-an-extended-candidate-set
#6
Yuefeng Ma, Xun Liang, James T Kwok, Jianping Li, Xiaoping Zhou, Haiyan Zhang
The semisupervised least squares support vector machine (LS-S³VM) is an important enhancement of least squares support vector machines in semisupervised learning. Given that most data collected from the real world are without labels, semisupervised approaches are more applicable than standard supervised approaches. Although a few training methods for LS-S³VM exist, the problem of deriving the optimal decision hyperplane efficiently and effectually has not been solved. In this paper, a fully weighted model of LS-S³VM is proposed, and a simple integer programming (IP) model is introduced through an equivalent transformation to solve the model...
February 14, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28212054/machine-learning-for-medical-imaging
#7
Bradley J Erickson, Panagiotis Korfiatis, Zeynettin Akkus, Timothy L Kline
Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algorithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region...
February 17, 2017: Radiographics: a Review Publication of the Radiological Society of North America, Inc
https://www.readbyqxmd.com/read/28211521/computer-aided-designing-of-immunosuppressive-peptides-based-on-il-10-inducing-potential
#8
Gandharva Nagpal, Salman Sadullah Usmani, Sandeep Kumar Dhanda, Harpreet Kaur, Sandeep Singh, Meenu Sharma, Gajendra P S Raghava
In the past, numerous methods have been developed to predict MHC class II binders or T-helper epitopes for designing the epitope-based vaccines against pathogens. In contrast, limited attempts have been made to develop methods for predicting T-helper epitopes/peptides that can induce a specific type of cytokine. This paper describes a method, developed for predicting interleukin-10 (IL-10) inducing peptides, a cytokine responsible for suppressing the immune system. All models were trained and tested on experimentally validated 394 IL-10 inducing and 848 non-inducing peptides...
February 17, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28211015/fifty-years-of-computer-analysis-in-chest-imaging-rule-based-machine-learning-deep-learning
#9
REVIEW
Bram van Ginneken
Half a century ago, the term "computer-aided diagnosis" (CAD) was introduced in the scientific literature. Pulmonary imaging, with chest radiography and computed tomography, has always been one of the focus areas in this field. In this study, I describe how machine learning became the dominant technology for tackling CAD in the lungs, generally producing better results than do classical rule-based approaches, and how the field is now rapidly changing: in the last few years, we have seen how even better results can be obtained with deep learning...
February 16, 2017: Radiological Physics and Technology
https://www.readbyqxmd.com/read/28208736/time-elastic-generative-model-for-acceleration-time-series-in-human-activity-recognition
#10
Mario Munoz-Organero, Ramona Ruiz-Blazquez
Body-worn sensors in general and accelerometers in particular have been widely used in order to detect human movements and activities. The execution of each type of movement by each particular individual generates sequences of time series of sensed data from which specific movement related patterns can be assessed. Several machine learning algorithms have been used over windowed segments of sensed data in order to detect such patterns in activity recognition based on intermediate features (either hand-crafted or automatically learned from data)...
February 8, 2017: Sensors
https://www.readbyqxmd.com/read/28208697/hybrid-analytical-and-data-driven-modeling-for-feed-forward-robot-control-%C3%A2
#11
René Felix Reinhart, Zeeshan Shareef, Jochen Jakob Steil
Feed-forward model-based control relies on models of the controlled plant, e.g., in robotics on accurate knowledge of manipulator kinematics or dynamics. However, mechanical and analytical models do not capture all aspects of a plant's intrinsic properties and there remain unmodeled dynamics due to varying parameters, unmodeled friction or soft materials. In this context, machine learning is an alternative suitable technique to extract non-linear plant models from data. However, fully data-based models suffer from inaccuracies as well and are inefficient if they include learning of well known analytical models...
February 8, 2017: Sensors
https://www.readbyqxmd.com/read/28207752/soilgrids250m-global-gridded-soil-information-based-on-machine-learning
#12
Tomislav Hengl, Jorge Mendes de Jesus, Gerard B M Heuvelink, Maria Ruiperez Gonzalez, Milan Kilibarda, Aleksandar Blagotić, Wei Shangguan, Marvin N Wright, Xiaoyuan Geng, Bernhard Bauer-Marschallinger, Mario Antonio Guevara, Rodrigo Vargas, Robert A MacMillan, Niels H Batjes, Johan G B Leenaars, Eloi Ribeiro, Ichsani Wheeler, Stephan Mantel, Bas Kempen
This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca...
2017: PloS One
https://www.readbyqxmd.com/read/28203290/mapping-and-classifying-molecules-from-a-high-throughput-structural-database
#13
Sandip De, Felix Musil, Teresa Ingram, Carsten Baldauf, Michele Ceriotti
High-throughput computational materials design promises to greatly accelerate the process of discovering new materials and compounds, and of optimizing their properties. The large databases of structures and properties that result from computational searches, as well as the agglomeration of data of heterogeneous provenance leads to considerable challenges when it comes to navigating the database, representing its structure at a glance, understanding structure-property relations, eliminating duplicates and identifying inconsistencies...
2017: Journal of Cheminformatics
https://www.readbyqxmd.com/read/28203249/autism-spectrum-disorder-detection-from-semi-structured-and-unstructured-medical-data
#14
Jianbo Yuan, Chester Holtz, Tristram Smith, Jiebo Luo
Autism spectrum disorder (ASD) is a developmental disorder that significantly impairs patients' ability to perform normal social interaction and communication. Moreover, the diagnosis procedure of ASD is highly time-consuming, labor-intensive, and requires extensive expertise. Although there exists no known cure for ASD, there is consensus among clinicians regarding the importance of early intervention for the recovery of ASD patients. Therefore, to benefit autism patients by enhancing their access to treatments such as early intervention, we aim to develop a robust machine learning-based system for autism detection by using Natural Language Processing techniques based on information extracted from medical forms of potential ASD patients...
December 2017: EURASIP Journal on Bioinformatics & Systems Biology
https://www.readbyqxmd.com/read/28201916/artificial-neural-network-for-the-configuration-problem-in-solids
#15
Hyunjun Ji, Yousung Jung
A machine learning approach based on the artificial neural network (ANN) is applied for the configuration problem in solids. The proposed method provides a direct mapping from configuration vectors to energies. The benchmark conducted for the M1 phase of Mo-V-Te-Nb oxide showed that only a fraction of configurations needs to be calculated, thus the computational burden significantly decreased, by a factor of 20-50, with R(2) = 0.96 and MAD = 0.12 eV. It is shown that ANN can also handle the effects of geometry relaxation when properly trained, resulting in R(2) = 0...
February 14, 2017: Journal of Chemical Physics
https://www.readbyqxmd.com/read/28199357/home-detection-of-freezing-of-gait-using-support-vector-machines-through-a-single-waist-worn-triaxial-accelerometer
#16
Daniel Rodríguez-Martín, Albert Samà, Carlos Pérez-López, Andreu Català, Joan M Moreno Arostegui, Joan Cabestany, Àngels Bayés, Sheila Alcaine, Berta Mestre, Anna Prats, M Cruz Crespo, Timothy J Counihan, Patrick Browne, Leo R Quinlan, Gearóid ÓLaighin, Dean Sweeney, Hadas Lewy, Joseph Azuri, Gabriel Vainstein, Roberta Annicchiarico, Alberto Costa, Alejandro Rodríguez-Molinero
Among Parkinson's disease (PD) symptoms, freezing of gait (FoG) is one of the most debilitating. To assess FoG, current clinical practice mostly employs repeated evaluations over weeks and months based on questionnaires, which may not accurately map the severity of this symptom. The use of a non-invasive system to monitor the activities of daily living (ADL) and the PD symptoms experienced by patients throughout the day could provide a more accurate and objective evaluation of FoG in order to better understand the evolution of the disease and allow for a more informed decision-making process in making adjustments to the patient's treatment plan...
2017: PloS One
https://www.readbyqxmd.com/read/28198674/sequence-specific-bias-correction-for-rna-seq-data-using-recurrent-neural-networks
#17
Yao-Zhong Zhang, Rui Yamaguchi, Seiya Imoto, Satoru Miyano
BACKGROUND: The recent success of deep learning techniques in machine learning and artificial intelligence has stimulated a great deal of interest among bioinformaticians, who now wish to bring the power of deep learning to bare on a host of bioinformatical problems. Deep learning is ideally suited for biological problems that require automatic or hierarchical feature representation for biological data when prior knowledge is limited. In this work, we address the sequence-specific bias correction problem for RNA-seq data redusing Recurrent Neural Networks (RNNs) to model nucleotide sequences without pre-determining sequence structures...
January 25, 2017: BMC Genomics
https://www.readbyqxmd.com/read/28198471/machine-learned-approximations-to-density-functional-theory-hamiltonians
#18
Ganesh Hegde, R Chris Bowen
Large scale Density Functional Theory (DFT) based electronic structure calculations are highly time consuming and scale poorly with system size. While semi-empirical approximations to DFT result in a reduction in computational time versus ab initio DFT, creating such approximations involves significant manual intervention and is highly inefficient for high-throughput electronic structure screening calculations. In this letter, we propose the use of machine-learning for prediction of DFT Hamiltonians. Using suitable representations of atomic neighborhoods and Kernel Ridge Regression, we show that an accurate and transferable prediction of DFT Hamiltonians for a variety of material environments can be achieved...
February 15, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28198354/noise-robust-unsupervised-spike-sorting-based-on-discriminative-subspace-learning-with-outlier-handling
#19
Mohammad Reza Keshtkaran, Zhi Yang
OBJECTIVE: Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high...
February 15, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28197643/machine-learning-principles-can-improve-hip-fracture-prediction
#20
Christian Kruse, Pia Eiken, Peter Vestergaard
Apply machine learning principles to predict hip fractures and estimate predictor importance in Dual-energy X-ray absorptiometry (DXA)-scanned men and women. Dual-energy X-ray absorptiometry data from two Danish regions between 1996 and 2006 were combined with national Danish patient data to comprise 4722 women and 717 men with 5 years of follow-up time (original cohort n = 6606 men and women). Twenty-four statistical models were built on 75% of data points through k-5, 5-repeat cross-validation, and then validated on the remaining 25% of data points to calculate area under the curve (AUC) and calibrate probability estimates...
February 14, 2017: Calcified Tissue International
keyword
keyword
79875
1
2
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read
×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"