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https://www.readbyqxmd.com/read/28330163/probing-an-optimal-class-distribution-for-enhancing-prediction-and-feature-characterization-of-plant-virus-encoded-rna-silencing-suppressors
#1
Abhigyan Nath, Karthikeyan Subbiah
To counter the host RNA silencing defense mechanism, many plant viruses encode RNA silencing suppressor proteins. These groups of proteins share very low sequence and structural similarities among them, which consequently hamper their annotation using sequence similarity-based search methods. Alternatively the machine learning-based methods can become a suitable choice, but the optimal performance through machine learning-based methods is being affected by various factors such as class imbalance, incomplete learning, selection of inappropriate features, etc...
June 2016: 3 Biotech
https://www.readbyqxmd.com/read/28329835/machine-learning-sentiment-analysis-and-tweets-an-examination-of-alzheimer-s-disease-stigma-on-twitter
#2
Nels Oscar, Pamela A Fox, Racheal Croucher, Riana Wernick, Jessica Keune, Karen Hooker
Objectives: Social scientists need practical methods for harnessing large, publicly available datasets that inform the social context of aging. We describe our development of a semi-automated text coding method and use a content analysis of Alzheimer's disease (AD) and dementia portrayal on Twitter to demonstrate its use. The approach improves feasibility of examining large publicly available datasets. Method: Machine learning techniques modeled stigmatization expressed in 31,150 AD-related tweets collected via Twitter's search API based on 9 AD-related keywords...
March 2, 2017: Journals of Gerontology. Series B, Psychological Sciences and Social Sciences
https://www.readbyqxmd.com/read/28329014/prediction-of-chronic-damage-in-systemic-lupus-erythematosus-by-using-machine-learning-models
#3
Fulvia Ceccarelli, Marco Sciandrone, Carlo Perricone, Giulio Galvan, Francesco Morelli, Luis Nunes Vicente, Ilaria Leccese, Laura Massaro, Enrica Cipriano, Francesca Romana Spinelli, Cristiano Alessandri, Guido Valesini, Fabrizio Conti
OBJECTIVE: The increased survival in Systemic Lupus Erythematosus (SLE) patients implies the development of chronic damage, occurring in up to 50% of cases. Its prevention is a major goal in the SLE management. We aimed at predicting chronic damage in a large monocentric SLE cohort by using neural networks. METHODS: We enrolled 413 SLE patients (M/F 30/383; mean age ± SD 46.3±11.9 years; mean disease duration ± SD 174.6 ± 112.1 months). Chronic damage was assessed by the SLICC/ACR Damage Index (SDI)...
2017: PloS One
https://www.readbyqxmd.com/read/28328964/the-acute-mania-of-king-george-iii-a-computational-linguistic-analysis
#4
Vassiliki Rentoumi, Timothy Peters, Jonathan Conlin, Peter Garrard
We used a computational linguistic approach, exploiting machine learning techniques, to examine the letters written by King George III during mentally healthy and apparently mentally ill periods of his life. The aims of the study were: first, to establish the existence of alterations in the King's written language at the onset of his first manic episode; and secondly to identify salient sources of variation contributing to the changes. Effects on language were sought in two control conditions (politically stressful vs...
2017: PloS One
https://www.readbyqxmd.com/read/28328520/a-natural-language-processing-framework-for-assessing-hospital-readmissions-for-patients-with-copd
#5
Ankur Agarwal, Christopher Baechle, Ravi Behara, Xingquan Zhu
With the passage of recent federal legislation many medical institutions are now responsible for reaching target hospital readmission rates. Chronic diseases account for many hospital readmissions and Chronic Obstructive Pulmonary Disease has been recently added to the list of diseases for which the United States government penalizes hospitals incurring excessive readmissions. Though there have been efforts to statistically predict those most in danger of readmission, few have focused primarily on unstructured clinical notes...
March 17, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28328516/anrad-a-neuromorphic-anomaly-detection-framework-for-massive-concurrent-data-streams
#6
Qiuwen Chen, Ryan Luley, Qing Wu, Morgan Bishop, Richard W Linderman, Qinru Qiu
The evolution of high performance computing technologies has enabled the large-scale implementation of neuromorphic models and pushed the research in computational intelligence into a new era. Among the machine learning applications, unsupervised detection of anomalous streams is especially challenging due to the requirements of detection accuracy and real-time performance. Designing a computing framework that harnesses the growing computing power of the multicore systems while maintaining high sensitivity and specificity to the anomalies is an urgent research topic...
March 17, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28328209/multidk-a-multiple-descriptor-multiple-kernel-approach-for-molecular-discovery-and-its-application-to-the-discovery-of-organic-flow-battery-electrolytes
#7
Sung-Jin Kim, Adrián Jinich, Alán Aspuru-Guzik
We propose a multiple descriptor multiple kernel (MultiDK) method for efficient molecular discovery using machine learning. We show that the MultiDK method improves both the speed and accuracy of molecular property prediction. We apply the method to the discovery of electrolyte molecules for aqueous redox flow batteries. Using \emph{multiple-type - as opposed to single-type - descriptors}, we obtain more relevant features for machine learning. Following the principle of 'wisdom of the crowds', the combination of multiple-type descriptors significantly boosts prediction performance...
March 22, 2017: Journal of Chemical Information and Modeling
https://www.readbyqxmd.com/read/28328162/predictive-model-for-inflammation-grades-of-chronic-hepatitis-b-large-scale-analysis-of-clinical-parameters-and-gene-expressions
#8
Weichen Zhou, Yanyun Ma, Jun Zhang, Jingyi Hu, Menghan Zhang, Yi Wang, Yi Li, Lijun Wu, Yida Pan, Yitong Zhang, Xiaonan Zhang, Xinxin Zhang, Zhanqing Zhang, Jiming Zhang, Hai Li, Lungen Lu, Li Jin, Jiucun Wang, Zhenghong Yuan, Jie Liu
BACKGROUND: Liver biopsy is the gold standard to assess pathological features (e.g. inflammation grades) for hepatitis B virus infected patients, although it's invasive and traumatic; meanwhile, several gene profiles of chronic hepatitis B (CHB) have been separately described in relatively small HBV-infected samples. We aimed to analyze correlations among inflammation grades, gene expressions and clinical parameters (serum alanine amino transaminase, aspartate amino transaminase, and HBV-DNA) in large-scale CHB samples, and to predict inflammation grades by using clinical parameters and/or gene expressions...
March 22, 2017: Liver International: Official Journal of the International Association for the Study of the Liver
https://www.readbyqxmd.com/read/28328004/red-ml-a-novel-effective-rna-editing-detection-method-based-on-machine-learning
#9
Heng Xiong, Dongbing Liu, Qiye Li, Mengyue Lei, Liqin Xu, Liang Wu, Zongji Wang, Shancheng Ren, Wangsheng Li, Min Xia, Lihua Lu, Haorong Lu, Yong Hou, Shida Zhu, Xin Liu, Yinghao Sun, Jian Wang, Huanming Yang, Kui Wu, Xun Xu, Leo J Lee
Background: With the advancement of second generation sequencing techniques, our ability to detect and quantify RNA editing on a global scale has been vastly improved. As a result, RNA editing is now being studied under a growing number of biological conditions so that its biochemical mechanisms and functional roles can be further understood. However, a major barrier that prevents RNA editing from being a routine RNA-seq analysis, similar to gene expression and splicing analysis for example, is the lack of user-friendly and effective computational tools...
March 2, 2017: GigaScience
https://www.readbyqxmd.com/read/28327989/using-and-understanding-cross-validation-strategies-perspectives-on-saeb-et%C3%A2-al
#10
Max A Little, Gael Varoquaux, Sohrab Saeb, Luca Lonini, Arun Jayaraman, David C Mohr, Konrad P Kording
This three-part review takes a detailed look at the complexities of cross validation, fostered by the peer review of Saeb et al.'s paper entitled The need to approximate the use-case in clinical machine learning. It contains perspectives by reviewers and by the original authors that touch upon cross-validation: the suitability of different strategies and their interpretation.
March 17, 2017: GigaScience
https://www.readbyqxmd.com/read/28327985/the-need-to-approximate-the-use-case-in-clinical-machine-learning
#11
Sohrab Saeb, Luca Lonini, Arun Jayaraman, David C Mohr, Konrad P Kording
Background: The availability of smartphone and wearable sensor technology is leading to a rapid accumulation of human subject data, and machine learning is emerging as a technique to map that data into clinical predictions. As machine learning algorithms are increasingly used to support clinical decision making, it is vital to reliably quantify their prediction accuracy. Cross-validation is the standard approach where the accuracy of such algorithms is evaluated on data the algorithm has not seen during training...
March 15, 2017: GigaScience
https://www.readbyqxmd.com/read/28325604/early-prediction-of-radiotherapy-induced-parotid-shrinkage-and-toxicity-based-on-ct-radiomics-and-fuzzy-classification
#12
Marco Pota, Elisa Scalco, Giuseppe Sanguineti, Alessia Farneti, Giovanni Mauro Cattaneo, Giovanna Rizzo, Massimo Esposito
MOTIVATION: Patients under radiotherapy for head-and-neck cancer often suffer of long-term xerostomia, and/or consistent shrinkage of parotid glands. In order to avoid these drawbacks, adaptive therapy can be planned for patients at risk, if the prediction is obtained timely, before or during the early phase of treatment. Artificial intelligence can address the problem, by learning from examples and building classification models. In particular, fuzzy logic has shown its suitability for medical applications, in order to manage uncertain data, and to build transparent rule-based classifiers...
March 18, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28325450/classification-of-nervous-system-withdrawn-and-approved-drugs-with-toxprint-features-via-machine-learning-strategies
#13
Aytun Onay, Melih Onay, Osman Abul
BACKGROUND AND OBJECTIVES: Early-phase virtual screening of candidate drug molecules plays a key role in pharmaceutical industry from data mining and machine learning to prevent adverse effects of the drugs. Computational classification methods can distinguish approved drugs from withdrawn ones. We focused on 6 data sets including maximum 110 approved and 110 withdrawn drugs for all and nervous system diseases to distinguish approved drugs from withdrawn ones. METHODS: In this study, we used support vector machines (SVMs) and ensemble methods (EMs) such as boosted and bagged trees to classify drugs into approved and withdrawn categories...
April 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28325448/diagnosis-of-autism-through-eeg-processed-by-advanced-computational-algorithms-a-pilot-study
#14
Enzo Grossi, Chiara Olivieri, Massimo Buscema
BACKGROUND: Multi-Scale Ranked Organizing Map coupled with Implicit Function as Squashing Time algorithm(MS-ROM/I-FAST) is a new, complex system based on Artificial Neural networks (ANNs) able to extract features of interest in computerized EEG through the analysis of few minutes of their EEG without any preliminary pre-processing. A proof of concept study previously published showed accuracy values ranging from 94%-98% in discerning subjects with Mild Cognitive Impairment and/or Alzheimer's Disease from healthy elderly people...
April 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28325049/antibody-subclass-detection-using-graphene-nanopore
#15
Amir Barati Farimani, Mohammad Heiranian, Kyoungmin Min, Narayana R Aluru
Solid-state nanopores are promising for label free protein detection. The large thickness, ranging from several tens of nanometers to micrometers and larger, of solid-state nanopores prohibits atomic scale scanning or interrogation of proteins. Here, a single-atom thick graphene nanopore is shown to be highly capable of sensing and discriminating between different subclasses of IgG antibodies despite their minor and subtle variation in atomic structure. Extensive molecular dynamics (MD) simulations, rigorous statistical analysis with a total aggregate simulation time of 2...
March 21, 2017: Journal of Physical Chemistry Letters
https://www.readbyqxmd.com/read/28324937/data-driven-estimation-of-blood-pressure-using-photoplethysmographic-signals
#16
Shi Chao Gao, Peter Wittek, Li Zhao, Wen Jun Jiang
Noninvasive measurement of blood pressure by optical methods receives considerable interest, but the complexity of the measurement and the difficulty of adjusting parameters restrict applications. We develop a method for estimating the systolic and diastolic blood pressure using a single-point optical recording of a photoplethysmographic (PPG) signal. The estimation is data-driven, we use automated machine learning algorithms instead of mathematical models. Combining supervised learning with a discrete wavelet transform, the method is insensitive to minor irregularities in the PPG waveform, hence both pulse oximeters and smartphone cameras can record the signal...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28324301/neuroimaging-in-epilepsy
#17
REVIEW
Erik H Middlebrooks, Lawrence Ver Hoef, Jerzy P Szaflarski
In recent years, the field of neuroimaging has undergone dramatic development. Specifically, of importance for clinicians and researchers managing patients with epilepsies, new methods of brain imaging in search of the seizure-producing abnormalities have been implemented, and older methods have undergone additional refinement. Methodology to predict seizure freedom and cognitive outcome has also rapidly progressed. In general, the image data processing methods are very different and more complicated than even a decade ago...
April 2017: Current Neurology and Neuroscience Reports
https://www.readbyqxmd.com/read/28323285/utilization-of-machine-learning-for-prediction-of-post-traumatic-stress-a-re-examination-of-cortisol-in-the-prediction-and-pathways-to-non-remitting-ptsd
#18
I R Galatzer-Levy, S Ma, A Statnikov, R Yehuda, A Y Shalev
To date, studies of biological risk factors have revealed inconsistent relationships with subsequent post-traumatic stress disorder (PTSD). The inconsistent signal may reflect the use of data analytic tools that are ill equipped for modeling the complex interactions between biological and environmental factors that underlay post-traumatic psychopathology. Further, using symptom-based diagnostic status as the group outcome overlooks the inherent heterogeneity of PTSD, potentially contributing to failures to replicate...
March 21, 2017: Translational Psychiatry
https://www.readbyqxmd.com/read/28323040/-machine-learning-based-identification-of-endogenous-cellular-microrna-sponges-against-viral-micrornas
#19
Soowon Kang, Seunghyun Park, Sungroh Yoon, Hyeyoung Min
A "miRNA sponge" is an artificial oligonucleotide-based miRNA inhibitor containing multiple binding sites for a specific miRNA. Each miRNA sponge can bind and sequester several miRNA copies, thereby decreasing the cellular levels of the target miRNA. In addition to developing artificial miRNA sponges, scientists have sought endogenous RNA transcripts and found that long non-coding RNAs, competing endogenous RNAs, pseudogenes, circular RNAs, and coding RNAs could act as miRNA sponges under precise conditions...
March 17, 2017: Methods: a Companion to Methods in Enzymology
https://www.readbyqxmd.com/read/28322859/decoding-the-encoding-of-functional-brain-networks-an-fmri-classification-comparison-of-non-negative-matrix-factorization-nmf-independent-component-analysis-ica-and-sparse-coding-algorithms
#20
Jianwen Xie, Pamela K Douglas, Ying Nian Wu, Arthur L Brody, Ariana E Anderson
BACKGROUND: Brain networks in fMRI are typically identified using spatial independent component analysis (ICA), yet other mathematical constraints provide alternate biologically-plausible frameworks for generating brain networks. Non-negative Matrix Factorization (NMF) would suppress negative BOLD signal by enforcing positivity. Spatial sparse coding algorithms (L1 Regularized Learning and K-SVD) would impose local specialization and a discouragement of multitasking, where the total observed activity in a single voxel originates from a restricted number of possible brain networks...
March 17, 2017: Journal of Neuroscience Methods
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