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Machine learning

Fabio Montagna, Marco Buiatti, Simone Benatti, Davide Rossi, Elisabetta Farella, Luca Benini
EEG is a standard non-invasive technique used in neural disease diagnostics and neurosciences. Frequency-tagging is an increasingly popular experimental paradigm that efficiently tests brain function by measuring EEG responses to periodic stimulation. Recently, frequency-tagging paradigms have proven successful with low stimulation frequencies (0.5 - 6 Hz), but the EEG signal is intrinsically noisy in this frequency range, requiring heavy signal processing and significant human intervention for response estimation...
June 21, 2017: Methods: a Companion to Methods in Enzymology
Filippo Piccinini, Tamas Balassa, Abel Szkalisity, Csaba Molnar, Lassi Paavolainen, Kaisa Kujala, Krisztina Buzas, Marie Sarazova, Vilja Pietiainen, Ulrike Kutay, Kevin Smith, Peter Horvath
High-content, imaging-based screens now routinely generate data on a scale that precludes manual verification and interrogation. Software applying machine learning has become an essential tool to automate analysis, but these methods require annotated examples to learn from. Efficiently exploring large datasets to find relevant examples remains a challenging bottleneck. Here, we present Advanced Cell Classifier (ACC), a graphical software package for phenotypic analysis that addresses these difficulties. ACC applies machine-learning and image-analysis methods to high-content data generated by large-scale, cell-based experiments...
June 16, 2017: Cell Systems
Tadashi Takeuchi, Hiroshi Ohno, Naoko Satoh-Takayama
Although acute peritonitis is a common and severe complication associated with peritoneal dialysis, the culture-based test used as the diagnostic criterion for this disease is often too slow to allow appropriate point-of-care diagnosis of specific bacterial infection. To address this problem, Zhang et al. report the efficacy of a novel set of immune biomarkers derived from a machine-learning algorithm applied to patient data. This fingerprint could predict major pathogenic causes of peritonitis.
July 2017: Kidney International
Yujian Li, Ting Zhang
The choice of kernel has an important effect on the performance of a support vector machine (SVM). The effect could be reduced by NEUROSVM, an architecture using multilayer perceptron for feature extraction and SVM for classification. In binary classification, a general linear kernel NEUROSVM can be theoretically simplified as an input layer, many hidden layers, and an SVM output layer. As a feature extractor, the sub-network composed of the input and hidden layers is first trained together with a virtual ordinary output layer by backpropagation, then with the output of its last hidden layer taken as input of the SVM classifier for further training separately...
June 21, 2017: Neural Networks: the Official Journal of the International Neural Network Society
Stephen D Dertinger
In many respects the evolution of baseball statistics mirrors advances made in the field of genetic toxicology. From its inception, baseball and statistics have been inextricably linked. Generations of players and fans have used a number of relatively simple measurements to describe team and individual player's current performance, as well as for historical record-keeping purposes. Over the years, baseball analytics has progressed in several important ways. Early advances were based on deriving more meaningful metrics from simpler forerunners...
June 24, 2017: Environmental and Molecular Mutagenesis
Hongyan Zhu, Bingquan Chu, Chu Zhang, Fei Liu, Linjun Jiang, Yong He
We investigated the feasibility and potentiality of presymptomatic detection of tobacco disease using hyperspectral imaging, combined with the variable selection method and machine-learning classifiers. Images from healthy and TMV-infected leaves with 2, 4, and 6 days post infection were acquired by a pushbroom hyperspectral reflectance imaging system covering the spectral range of 380-1023 nm. Successive projections algorithm was evaluated for effective wavelengths (EWs) selection. Four texture features, including contrast, correlation, entropy, and homogeneity were extracted according to grey-level co-occurrence matrix (GLCM)...
June 23, 2017: Scientific Reports
Yue-Jiao Gong, Jun Zhang, Yicong Zhou
Most learning methods contain optimization as a substep, where the nondifferentiability and multimodality of objectives push forward the interplay of evolutionary optimization algorithms and machine learning models. The recently emerged evolutionary multimodal optimization (MMOP) technique enables the learning of diverse sets of effective parameters for the models simultaneously, providing new opportunities to the applications requiring both accuracy and diversity, such as ensemble, interactive, and interpretive learning...
June 20, 2017: IEEE Transactions on Neural Networks and Learning Systems
M Amac Guvensan, A Oguz Kansiz, N Cihan Camgoz, H Irem Turkmen, A Gokhan Yavuz, M Elif Karsligil
Automatic detection of fall events is vital to providing fast medical assistance to the causality, particularly when the injury causes loss of consciousness. Optimization of the energy consumption of mobile applications, especially those which run 24/7 in the background, is essential for longer use of smartphones. In order to improve energy-efficiency without compromising on the fall detection performance, we propose a novel 3-tier architecture that combines simple thresholding methods with machine learning algorithms...
June 23, 2017: Sensors
Nguyen-Quoc-Khanh Le, Quang-Thai Ho, Yu-Yen Ou
In several years, deep learning is a modern machine learning technique using in a variety of fields with state-of-the-art performance. Therefore, utilization of deep learning to enhance performance is also an important solution for current bioinformatics field. In this study, we try to use deep learning via convolutional neural networks and position specific scoring matrices to identify electron transport proteins, which is an important molecular function in transmembrane proteins. Our deep learning method can approach a precise model for identifying of electron transport proteins with achieved sensitivity of 80...
June 22, 2017: Journal of Computational Chemistry
M Manczinger, V Bodnár, B T Papp, B Sz Bolla, K Szabó, B Balázs, E Csányi, E Szél, G Erős, L Kemény
As drug development is extremely expensive, the identification of novel indications for in-market drugs is financially attractive. Multiple algorithms are used to support such drug repurposing, but highly reliable methods combining simulation of intracellular networks and machine learning are currently not available. We developed an algorithm that simulates drug effects on the flow of information through protein-protein interaction networks, and uses Support Vector Machine to identify potentially effective drugs in our model disease, psoriasis...
June 23, 2017: Clinical Pharmacology and Therapeutics
Yuan Luo, William K Thompson, Timothy M Herr, Zexian Zeng, Mark A Berendsen, Siddhartha R Jonnalagadda, Matthew B Carson, Justin Starren
The goal of pharmacovigilance is to detect, monitor, characterize and prevent adverse drug events (ADEs) with pharmaceutical products. This article is a comprehensive structured review of recent advances in applying natural language processing (NLP) to electronic health record (EHR) narratives for pharmacovigilance. We review methods of varying complexity and problem focus, summarize the current state-of-the-art in methodology advancement, discuss limitations and point out several promising future directions...
June 22, 2017: Drug Safety: An International Journal of Medical Toxicology and Drug Experience
Gabriela Sehnem Heck, Val Oliveira Pintro, Richard Rene Pereira, Mauricio Boff de Ávila, Nayara Maria Bernhardt Levin, Walter Filgueira de Azevedo
BACKGROUND: Calculation of ligand-binding affinity is an open problem in computational medicinal chemistry. The ability to computationally predict affinities has a beneficial impact in the early stages of drug development, since it allows a mathematical model to assess protein-ligand interactions. Due to the availability of structural and binding information, machine learning methods have been applied to generate scoring functions with good predictive power. OBJECTIVE: Our goal here is to review recent developments in the application of machine learning methods to predict ligand- binding affinity...
June 22, 2017: Current Medicinal Chemistry
Hong Tao, Chenping Hou, Feiping Nie, Jubo Zhu, Dongyun Yi
With the advent of multi-view data, multi-view learning has become an important research direction in machine learning and image processing. Considering the difficulty of obtaining labeled data in many machine learning applications, we focus on the multi-view semi-supervised classification problem. In this paper, we propose an algorithm named Multi-View Semi-Supervised Classification via Adaptive Regression (MVAR) to address this problem. Specifically, regression based loss functions with ℓ2,1 matrix norm are adopted for each view and the final objective function is formulated as the linear weighted combination of all the loss functions...
June 19, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Stefano Castellana, Caterina Fusilli, Gianluigi Mazzoccoli, Tommaso Biagini, Daniele Capocefalo, Massimo Carella, Angelo Luigi Vescovi, Tommaso Mazza
24,189 are all the possible non-synonymous amino acid changes potentially affecting the human mitochondrial DNA. Only a tiny subset was functionally evaluated with certainty so far, while the pathogenicity of the vast majority was only assessed in-silico by software predictors. Since these tools proved to be rather incongruent, we have designed and implemented APOGEE, a machine-learning algorithm that outperforms all existing prediction methods in estimating the harmfulness of mitochondrial non-synonymous genome variations...
June 22, 2017: PLoS Computational Biology
Daniel G Pankratz, Yoonha Choi, Urooj Imtiaz, Grażyna M Fedorowicz, Jessica D Anderson, Thomas V Colby, Jeffrey L Myers, David A Lynch, Kevin K Brown, Kevin R Flaherty, Mark P Steele, Steve D Groshong, Ganesh Raghu, Neil M Barth, P Sean Walsh, Jing Huang, Giulia C Kennedy, Fernando J Martinez
RATIONALE: Usual interstitial pneumonia (UIP) is the histopathologic hallmark of idiopathic pulmonary fibrosis. While UIP can be detected by high-resolution computed tomography (HRCT) of the chest, HRCT results are frequently inconclusive, and pathology from transbronchial biopsy (TBB) has poor sensitivity. Surgical lung biopsy (SLB) may be necessary for a definitive diagnosis. OBJECTIVES: To develop a genomic classifier in tissue obtained by TBB that distinguishes UIP from non-UIP, trained against central pathology as the reference standard...
June 22, 2017: Annals of the American Thoracic Society
Kelly C Vranas, Jeffrey K Jopling, Timothy E Sweeney, Meghan C Ramsey, Arnold S Milstein, Christopher G Slatore, Gabriel J Escobar, Vincent X Liu
OBJECTIVES: Identifying subgroups of ICU patients with similar clinical needs and trajectories may provide a framework for more efficient ICU care through the design of care platforms tailored around patients' shared needs. However, objective methods for identifying these ICU patient subgroups are lacking. We used a machine learning approach to empirically identify ICU patient subgroups through clustering analysis and evaluate whether these groups might represent appropriate targets for care redesign efforts...
June 21, 2017: Critical Care Medicine
Han G Yi, Zilong Xie, Rachel Reetzke, Alexandros G Dimakis, Bharath Chandrasekaran
INTRODUCTION: Scalp-recorded electrophysiological responses to complex, periodic auditory signals reflect phase-locked activity from neural ensembles within the auditory system. These responses, referred to as frequency-following responses (FFRs), have been widely utilized to index typical and atypical representation of speech signals in the auditory system. One of the major limitations in FFR is the low signal-to-noise ratio at the level of single trials. For this reason, the analysis relies on averaging across thousands of trials...
June 2017: Brain and Behavior
Thomas Desautels, Jacob Calvert, Jana Hoffman, Qingqing Mao, Melissa Jay, Grant Fletcher, Chris Barton, Uli Chettipally, Yaniv Kerem, Ritankar Das
Algorithm-based clinical decision support (CDS) systems associate patient-derived health data with outcomes of interest, such as in-hospital mortality. However, the quality of such associations often depends on the availability of site-specific training data. Without sufficient quantities of data, the underlying statistical apparatus cannot differentiate useful patterns from noise and, as a result, may underperform. This initial training data burden limits the widespread, out-of-the-box, use of machine learning-based risk scoring systems...
2017: Biomedical Informatics Insights
Vincent Luboz, Mathieu Bailet, Christelle Boichon Grivot, Michel Rochette, Bruno Diot, Marek Bucki, Yohan Payan
Ischial pressure ulcer is an important risk for every paraplegic person and a major public health issue. Pressure ulcers appear following excessive compression of buttock's soft tissues by bony structures, and particularly in ischial and sacral bones. Current prevention techniques are mainly based on daily skin inspection to spot red patches or injuries. Nevertheless, most pressure ulcers occur internally and are difficult to detect early. Estimating internal strains within soft tissues could help to evaluate the risk of pressure ulcer...
June 15, 2017: Journal of Tissue Viability
Jing Xing, Wenchao Lu, Rongfeng Liu, Yulan Wang, Yiqian Xie, Hao Zhang, Zhe Shi, Hao Jiang, Yu-Chih Liu, Kaixian Chen, Hualiang Jiang, Cheng Luo, Mingyue Zheng
Bromodomain-containing protein 4 (BRD4) is implicated in the pathogenesis of a number of different cancers, inflammatory diseases and heart failure. Much effort has been dedicated toward discovering novel scaffold BRD4 inhibitors (BRD4is) with different selectivity profiles and potential anti-resistance properties. Structure-based drug design (SBDD) and virtual screening (VS) are the most frequently used approaches. Here, we demonstrate a novel, structure-based VS approach that uses machine-learning algorithms trained on the priori structure and activity knowledge to predict the likelihood that a compound is a BRD4i based on its binding pattern with BRD4...
June 21, 2017: Journal of Chemical Information and Modeling
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