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https://www.readbyqxmd.com/read/29240876/machine-learning-for-classifying-tuberculosis-drug-resistance-from-dna-sequencing-data
#1
Yang Yang, Katherine E Niehaus, Timothy M Walker, Zamin Iqbal, A Sarah Walker, Daniel J Wilson, Tim E Peto, Derrick W Crook, E Grace Smith, Tingting Zhu, David A Clifton
Motivation: Correct and rapid determination of Mycobacterium tuberculosis (MTB) resistance against available tuberculosis (TB) drugs is essential for the control and management of TB. Conventional molecular diagnostic test assumes that the presence of any well-studied single nucleotide polymorphisms is sufficient to cause resistance, which yields low sensitivity for resistance classification. Methods: Given the availability of DNA sequencing data from MTB, we developed machine learning models for a cohort of 1839 UK bacterial isolates to classify MTB resistance against eight anti-TB drugs (isoniazid, rifampicin, ethambutol, pyrazinamide, ciprofloxacin, moxifloxacin, ofloxacin, streptomycin) and to classify multi-drug resistance...
December 12, 2017: Bioinformatics
https://www.readbyqxmd.com/read/29239858/prediction-of-breast-cancer-risk-using-a-machine-learning-approach-embedded-with-a-locality-preserving-projection-algorithm
#2
Morteza Heidari, Abolfazl Zargari Khuzani, Alan B Hollingsworth, Gopichandh Danala, Seyedehnafiseh Mirniaharikandehei, Yuchen Qiu, Hong Liu, Bin Zheng
In order to automatically identify a set of effective mammographic image features and build an optimal breast cancer risk stratification model, this study aims to investigate advantages of applying a machine learning approach embedded with a locally preserving projection (LPP) based feature combination and regeneration algorithm to predict short-term breast cancer risk. A dataset involving negative mammograms acquired from 500 women was assembled. This dataset was divided into two age-matched classes of 250 high risk cases in which cancer was detected in the next subsequent mammography screening and 250 low risk cases, which remained negative...
December 14, 2017: Physics in Medicine and Biology
https://www.readbyqxmd.com/read/29238891/can-human-experts-predict-solubility-better-than-computers
#3
Samuel Boobier, Anne Osbourn, John B O Mitchell
In this study, we design and carry out a survey, asking human experts to predict the aqueous solubility of druglike organic compounds. We investigate whether these experts, drawn largely from the pharmaceutical industry and academia, can match or exceed the predictive power of algorithms. Alongside this, we implement 10 typical machine learning algorithms on the same dataset. The best algorithm, a variety of neural network known as a multi-layer perceptron, gave an RMSE of 0.985 log S units and an R2 of 0.706...
December 13, 2017: Journal of Cheminformatics
https://www.readbyqxmd.com/read/29238404/pmlb-a-large-benchmark-suite-for-machine-learning-evaluation-and-comparison
#4
Randal S Olson, William La Cava, Patryk Orzechowski, Ryan J Urbanowicz, Jason H Moore
Background: The selection, development, or comparison of machine learning methods in data mining can be a difficult task based on the target problem and goals of a particular study. Numerous publicly available real-world and simulated benchmark datasets have emerged from different sources, but their organization and adoption as standards have been inconsistent. As such, selecting and curating specific benchmarks remains an unnecessary burden on machine learning practitioners and data scientists...
2017: BioData Mining
https://www.readbyqxmd.com/read/29238346/generating-a-tolerogenic-cell-therapy-knowledge-graph-from-literature
#5
Andre Lamurias, João D Ferreira, Luka A Clarke, Francisco M Couto
Tolerogenic cell therapies provide an alternative to conventional immunosuppressive treatments of autoimmune disease and address, among other goals, the rejection of organ or stem cell transplants. Since various methodologies can be followed to develop tolerogenic therapies, it is important to be aware and up to date on all available studies that may be relevant to their improvement. Recently, knowledge graphs have been proposed to link various sources of information, using text mining techniques. Knowledge graphs facilitate the automatic retrieval of information about the topics represented in the graph...
2017: Frontiers in Immunology
https://www.readbyqxmd.com/read/29238336/combination-of-classifiers-identifies-fungal-specific-activation-of-lysosome-genes-in-human-monocytes
#6
João P Leonor Fernandes Saraiva, Cristina Zubiria-Barrera, Tilman E Klassert, Maximilian J Lautenbach, Markus Blaess, Ralf A Claus, Hortense Slevogt, Rainer König
Blood stream infections can be caused by several pathogens such as viruses, fungi and bacteria and can cause severe clinical complications including sepsis. Delivery of appropriate and quick treatment is mandatory. However, it requires a rapid identification of the invading pathogen. The current gold standard for pathogen identification relies on blood cultures and these methods require a long time to gain the needed diagnosis. The use of in situ experiments attempts to identify pathogen specific immune responses but these often lead to heterogeneous biomarkers due to the high variability in methods and materials used...
2017: Frontiers in Microbiology
https://www.readbyqxmd.com/read/29238314/computational-psychometrics-for-the-measurement-of-collaborative-problem-solving-skills
#7
Stephen T Polyak, Alina A von Davier, Kurt Peterschmidt
This paper describes a psychometrically-based approach to the measurement of collaborative problem solving skills, by mining and classifying behavioral data both in real-time and in post-game analyses. The data were collected from a sample of middle school children who interacted with a game-like, online simulation of collaborative problem solving tasks. In this simulation, a user is required to collaborate with a virtual agent to solve a series of tasks within a first-person maze environment. The tasks were developed following the psychometric principles of Evidence Centered Design (ECD) and are aligned with the Holistic Framework developed by ACT...
2017: Frontiers in Psychology
https://www.readbyqxmd.com/read/29238300/supervised-estimation-of-granger-based-causality-between-time-series
#8
Danilo Benozzo, Emanuele Olivetti, Paolo Avesani
Brain effective connectivity aims to detect causal interactions between distinct brain units and it is typically studied through the analysis of direct measurements of the neural activity, e.g., magneto/electroencephalography (M/EEG) signals. The literature on methods for causal inference is vast. It includes model-based methods in which a generative model of the data is assumed and model-free methods that directly infer causality from the probability distribution of the underlying stochastic process. Here, we firstly focus on the model-based methods developed from the Granger criterion of causality, which assumes the autoregressive model of the data...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/29237823/examining-the-correlates-and-drivers-of-human-population-distributions-across-low-and-middle-income-countries
#9
Jeremiah J Nieves, Forrest R Stevens, Andrea E Gaughan, Catherine Linard, Alessandro Sorichetta, Graeme Hornby, Nirav N Patel, Andrew J Tatem
Geographical factors have influenced the distributions and densities of global human population distributions for centuries. Climatic regimes have made some regions more habitable than others, harsh topography has discouraged human settlement, and transport links have encouraged population growth. A better understanding of these types of relationships enables both improved mapping of population distributions today and modelling of future scenarios. However, few comprehensive studies of the relationships between population spatial distributions and the range of drivers and correlates that exist have been undertaken at all, much less at high spatial resolutions, and particularly across the low- and middle-income countries...
December 2017: Journal of the Royal Society, Interface
https://www.readbyqxmd.com/read/29237580/detecting-smoking-events-using-accelerometer-data-collected-via-smartwatch-technology-validation-study
#10
Casey A Cole, Dien Anshari, Victoria Lambert, James F Thrasher, Homayoun Valafar
BACKGROUND: Smoking is the leading cause of preventable death in the world today. Ecological research on smoking in context currently relies on self-reported smoking behavior. Emerging smartwatch technology may more objectively measure smoking behavior by automatically detecting smoking sessions using robust machine learning models. OBJECTIVE: This study aimed to examine the feasibility of detecting smoking behavior using smartwatches. The second aim of this study was to compare the success of observing smoking behavior with smartwatches to that of conventional self-reporting...
December 13, 2017: JMIR MHealth and UHealth
https://www.readbyqxmd.com/read/29237237/clinical-judgement-in-the-era-of-big-data-and-predictive-analytics
#11
Benjamin Chin-Yee, Ross Upshur
Clinical judgement is a central and longstanding issue in the philosophy of medicine which has generated significant interest over the past few decades. In this article, we explore different approaches to clinical judgement articulated in the literature, focusing in particular on data-driven, mathematical approaches which we contrast with narrative, virtue-based approaches to clinical reasoning. We discuss the tension between these different clinical epistemologies and further explore the implications of big data and machine learning for a philosophy of clinical judgement...
December 13, 2017: Journal of Evaluation in Clinical Practice
https://www.readbyqxmd.com/read/29236733/evolutionary-design-optimization-of-traffic-signals-applied-to-quito-city
#12
Rolando Armas, Hernán Aguirre, Fabio Daolio, Kiyoshi Tanaka
This work applies evolutionary computation and machine learning methods to study the transportation system of Quito from a design optimization perspective. It couples an evolutionary algorithm with a microscopic transport simulator and uses the outcome of the optimization process to deepen our understanding of the problem and gain knowledge about the system. The work focuses on the optimization of a large number of traffic lights deployed on a wide area of the city and studies their impact on travel time, emissions and fuel consumption...
2017: PloS One
https://www.readbyqxmd.com/read/29236680/uv-vis-and-cielab-based-chemometric-characterization-of-manihot-esculenta-carotenoid-contents
#13
Telma Afonso, Rodolfo Moresco, Virgilio G Uarrota, Bruno Bachiega Navarro, Eduardo da C Nunes, Marcelo Maraschin, Miguel Rocha
Vitamin A deficiency is a prevalent health problem in many areas of the world, where cassava genotypes with high pro-vitamin A content have been identified as a strategy to address this issue. In this study, we found a positive correlation between the color of the root pulp and the total carotenoid contents and, importantly, showed how CIELAB color measurements can be used as a non-destructive and fast technique to quantify the amount of carotenoids in cassava root samples, as opposed to traditional methods...
December 13, 2017: Journal of Integrative Bioinformatics
https://www.readbyqxmd.com/read/29236484/constructing-grids-for-molecular-quantum-dynamics-using-an-autoencoder
#14
Julius Philipp Paul Zauleck, Regina de Vivie-Riedle
A challenge for molecular quantum dynamics (QD) calculations is the curse of dimensionality with respect to the nuclear degrees of freedom. A common approach that works especially well for fast reactive processes is to reduce the dimensionality of the system to a few most relevant coordinates. Identifying these can become a very difficult task, since they often are highly unintuitive. We present a machine learning approach that utilizes an autoencoder that is trained to find a low-dimensional representation of a set of molecular configurations...
December 13, 2017: Journal of Chemical Theory and Computation
https://www.readbyqxmd.com/read/29235914/improving-predictive-accuracy-in-elections
#15
David Sathiaraj, William M Cassidy, Eric Rohli
The problem of accurately predicting vote counts in elections is considered in this article. Typically, small-sample polls are used to estimate or predict election outcomes. In this study, a machine-learning hybrid approach is proposed. This approach utilizes multiple sets of static data sources, such as voter registration data, and dynamic data sources, such as polls and donor data, to develop individualized voter scores for each member of the population. These voter scores are used to estimate expected vote counts under different turnout scenarios...
December 2017: Big Data
https://www.readbyqxmd.com/read/29235868/a-metabolomic-signature-of-endometrial-cancer
#16
Jacopo Troisi, Laura Sarno, Annamaria Landolfi, Giovanni Scala, Pasquale Martinelli, Roberta Venturella, Annalisa Di Cello, Fulvio Zullo, Maurizio Guida
Endometrial cancer (EC) is the most common cancer of the female reproductive tract in developed Countries. At the moment, no effective screening system is available. Here, we evaluate the diagnostic performance of a serum metabolomic signature. Two enrollments were carried out: one constituted of 168 subjects: 88 with EC and 80 healthy women, was used for building the classification models. The second (used to establish the performance of the classification algorithm) was constituted of 120 subjects: 30 with EC, 30 with ovarian cancer, 10 with benign endometrial disease and 50 healthy controls...
December 13, 2017: Journal of Proteome Research
https://www.readbyqxmd.com/read/29235174/big-data-and-medicine-a-big-deal
#17
Viktor Mayer-Schönberger, Erik Ingelsson
Big Data promises huge benefits for medical research. Looking beyond superficial increases in the amount of data collected, we identify three key areas where Big Data differs from conventional analyses of data samples: (1) data is captured more comprehensively relative to the phenomenon under study; this reduces some bias but surfaces important tradeoffs, such as between data quantity and data quality; (2) data is often analyzed using machine learning tools, such as neural networks rather than conventional statistical methods resulting in systems that over time capture insights implicit in data, but remain black boxes, rarely revealing causal connections; and (3) the purpose of the analyses of data is no longer simply answering existing questions, but hinting at novel ones and generating promising new hypotheses...
December 13, 2017: Journal of Internal Medicine
https://www.readbyqxmd.com/read/29235070/predpsych-a-toolbox-for-predictive-machine-learning-based-approach-in-experimental-psychology-research
#18
Atesh Koul, Cristina Becchio, Andrea Cavallo
Recent years have seen an increased interest in machine learning-based predictive methods for analyzing quantitative behavioral data in experimental psychology. While these methods can achieve relatively greater sensitivity compared to conventional univariate techniques, they still lack an established and accessible implementation. The aim of current work was to build an open-source R toolbox - "PredPsych" - that could make these methods readily available to all psychologists. PredPsych is a user-friendly, R toolbox based on machine-learning predictive algorithms...
December 12, 2017: Behavior Research Methods
https://www.readbyqxmd.com/read/29234997/learning-epistatic-interactions-from-sequence-activity-data-to-predict-enantioselectivity
#19
Julian Zaugg, Yosephine Gumulya, Alpeshkumar K Malde, Mikael Bodén
Enzymes with a high selectivity are desirable for improving economics of chemical synthesis of enantiopure compounds. To improve enzyme selectivity mutations are often introduced near the catalytic active site. In this compact environment epistatic interactions between residues, where contributions to selectivity are non-additive, play a significant role in determining the degree of selectivity. Using support vector machine regression models we map mutations to the experimentally characterised enantioselectivities for a set of 136 variants of the epoxide hydrolase from the fungus Aspergillus niger (AnEH)...
December 12, 2017: Journal of Computer-aided Molecular Design
https://www.readbyqxmd.com/read/29234807/development-and-validation-of-a-deep-learning-system-for-diabetic-retinopathy-and-related-eye-diseases-using-retinal-images-from-multiethnic-populations-with-diabetes
#20
Daniel Shu Wei Ting, Carol Yim-Lui Cheung, Gilbert Lim, Gavin Siew Wei Tan, Nguyen D Quang, Alfred Gan, Haslina Hamzah, Renata Garcia-Franco, Ian Yew San Yeo, Shu Yen Lee, Edmund Yick Mun Wong, Charumathi Sabanayagam, Mani Baskaran, Farah Ibrahim, Ngiap Chuan Tan, Eric A Finkelstein, Ecosse L Lamoureux, Ian Y Wong, Neil M Bressler, Sobha Sivaprasad, Rohit Varma, Jost B Jonas, Ming Guang He, Ching-Yu Cheng, Gemmy Chui Ming Cheung, Tin Aung, Wynne Hsu, Mong Li Lee, Tien Yin Wong
Importance: A deep learning system (DLS) is a machine learning technology with potential for screening diabetic retinopathy and related eye diseases. Objective: To evaluate the performance of a DLS in detecting referable diabetic retinopathy, vision-threatening diabetic retinopathy, possible glaucoma, and age-related macular degeneration (AMD) in community and clinic-based multiethnic populations with diabetes. Design, Setting, and Participants: Diagnostic performance of a DLS for diabetic retinopathy and related eye diseases was evaluated using 494 661 retinal images...
December 12, 2017: JAMA: the Journal of the American Medical Association
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