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https://www.readbyqxmd.com/read/30502182/untargeted-food-contaminant-detection-using-uhplc-hrms-combined-with-multivariate-analysis-feasibility-study-on-tea
#1
Grégoire Delaporte, Mathieu Cladière, Delphine Jouan-Rimbaud Bouveresse, Valérie Camel
Powerful data pretreatment strategies inspired from the field of metabolomics were adapted to chemical food safety context to enable samples discrimination by multivariate methods based on low abundance ions. A highly automated workflow was produced. The open-source XCMS package was used and efficient data filtration strategies were set up. Data were treated using Independent Components Analysis, and data mining strategies developed to automatically detect and annotate ions of low abundance by coupling blind data exploration strategies with a broad scale database approach...
March 30, 2019: Food Chemistry
https://www.readbyqxmd.com/read/30453740/accelerating-metabolite-identification-in-natural-product-research-toward-an-ideal-combination-of-lc-hrms-ms-and-nmr-profiling-in-silico-databases-and-chemometrics
#2
Jean-Luc Wolfender, Jean-Marc Nuzillard, Justin J J van der Hooft, Jean-Hugues Renault, Samuel Bertrand
The rapid innovations in metabolite profiling, bioassays and chemometrics have led to a paradigm shift in natural product (NP) research. Indeed, having partial or full structure information about possibly "all" specialized metabolites and an estimation of their levels in plants or microorganisms provides a way to perform pharmacognostic or chemical ecology investigations from a new and holistic perspective. The increasing amount of accurate metabolome data that can be acquired on massive sample sets, notably through data-dependent LC-HRMS/MS and NMR profiling, allows the mapping of natural extracts at an unprecedented level of precision...
November 19, 2018: Analytical Chemistry
https://www.readbyqxmd.com/read/30416468/detecting-temporal-cognition-in-text-comparison-of-judgements-by-self-expert-and-machine
#3
Erin I Walsh, Janie Busby Grant
Background: There is a growing research focus on temporal cognition, due to its importance in memory and planning, and links with psychological wellbeing. Researchers are increasingly using diary studies, experience sampling and social media data to study temporal thought. However, it remains unclear whether such reports can be accurately interpreted for temporal orientation. In this study, temporal orientation judgements about text reports of thoughts were compared across human coding, automatic text mining, and participant self-report...
2018: Frontiers in Psychology
https://www.readbyqxmd.com/read/30408959/application-of-bioactivity-profile-based-fingerprints-for-building-machine-learning-models
#4
Noé Sturm, Jiangming Sun, Yves Vandriessche, Andreas Mayr, G Uuml Nter Klambauer, Lars Carlsson, Ola Engkvist, Hongming Chen
The volume of high throughput screening data has considerably increased since the beginning of the automated biochemical and cell-based assays era. This information-rich data source provides tremendous repurposing opportunities for data mining. It was recently shown that biochemical or cell-based assay results can be compiled into so called high-throughput fingerprints (HTSFPs) as a new type of descriptor describing molecular bioactivity profiles which can be applied in virtual screening, iterative screening, and target deconvolution...
November 9, 2018: Journal of Chemical Information and Modeling
https://www.readbyqxmd.com/read/30404000/revealing-the-critical-regulators-of-cell-identity-in-the-mouse-cell-atlas
#5
Shengbao Suo, Qian Zhu, Assieh Saadatpour, Lijiang Fei, Guoji Guo, Guo-Cheng Yuan
Recent progress in single-cell technologies has enabled the identification of all major cell types in mouse. However, for most cell types, the regulatory mechanism underlying their identity remains poorly understood. By computational analysis of the recently published mouse cell atlas data, we have identified 202 regulons whose activities are highly variable across different cell types, and more importantly, predicted a small set of essential regulators for each major cell type in mouse. Systematic validation by automated literature and data mining provides strong additional support for our predictions...
November 6, 2018: Cell Reports
https://www.readbyqxmd.com/read/30400529/research-on-the-strategy-of-motion-constraint-aided-zupt-for-the-sins-positioning-system-of-a-shearer
#6
Hai Yang, Wei Li, Tao Luo, Haibo Liang, He Zhang, Yaxiong Gu, Chengming Luo
The accurate measurement of position and orientation for shearers is a key technology in realizing an automated, fully-mechanized, coal mining face. Since Global Positioning System (GPS) signal cannot arrive at the coal mine underground, wireless sensor network positioning system cannot operate stably in the coal mine; thus a strap-down inertial navigation system (SINS) is used to measure the position and orientation of the shearer. Aiming at the problem of the SINS accumulative error, this paper proposes a positioning error correction method based on the motion constraint-aided SINS zero velocity updated (ZUPT) model...
November 22, 2017: Micromachines
https://www.readbyqxmd.com/read/30337066/automated-ontology-generation-framework-powered-by-linked-biomedical-ontologies-for-disease-drug-domain
#7
Mazen Alobaidi, Khalid Mahmood Malik, Maqbool Hussain
OBJECTIVE AND BACKGROUND: The exponential growth of the unstructured data available in biomedical literature, and Electronic Health Record (EHR), requires powerful novel technologies and architectures to unlock the information hidden in the unstructured data. The success of smart healthcare applications such as clinical decision support systems, disease diagnosis systems, and healthcare management systems depends on knowledge that is understandable by machines to interpret and infer new knowledge from it...
October 2018: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/30311556/design-framework-for-a-data-mart-in-the-neonatal-intensive-care-unit
#8
Rudresh Deepak Shirwaikar, Dinesh Acharya U, Krishnamoorthi Makkithaya, Surulivelrajan Mallayaswamy, Leslie Edward Simon Lewis
Neonates who are critically ill are cared for in a neonatal intensive care unit (NICU) for continuous monitoring of their conditions. Physiological parameters such as heart rate, respiratory wave form, blood oxygen saturation, and body temperature are constantly monitored in the NICU. However, NICUs are not always equipped with a computer system for analyzing such data, identifying critical events, and providing decision support for a neonatologist. Therefore, a specialized computer system, commonly known as a data mart, should be developed for the NICU...
2018: Critical Reviews in Biomedical Engineering
https://www.readbyqxmd.com/read/30306941/similarity-detection-between-virtual-patients-and-medical-curriculum-using-r
#9
Martin Komenda, Jakub Ščavnický, Petra Růžičková, Matěj Karolyi, Petr Štourač, Daniel Schwarz
This paper presents the domain of information sciences, applied informatics and biomedical engineering, proposing to develop methods for an automated detection of similarities between two particular virtual learning environments - virtual patients at Akutne.cz and the OPTIMED curriculum management system - in order to provide support to clinically oriented stages of medical and healthcare studies. For this purpose, the authors used large amounts of text-based data collected by the system for mapping medical curricula and through the system for virtual patient authoring and delivery...
2018: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/30306896/twister-a-tool-for-reducing-screening-time-in-systematic-literature-reviews
#10
Karl Kreiner, Dieter Hayn, Günter Schreier
Systematic reviews are widely used as a tool for decision making to establish new clinical guidelines. Reviews can be time-consuming, potentially leaving authors with thousands of citations to screen. Software tools for assisting reviewers in this process are available, however, only few use text mining techniques to reduce screening time. In this work, we introduce Twister, a web-based tool for semi-automated literature reviews with broad research questions. We discuss how two text mining techniques can be used to (a) extract data elements from clinical abstracts and (b) how citations can be clustered based on a key phrase-extraction to help reviewers reduce screening time...
2018: Studies in Health Technology and Informatics
https://www.readbyqxmd.com/read/30295720/a-survey-of-ontology-learning-techniques-and-applications
#11
Muhammad Nabeel Asim, Muhammad Wasim, Muhammad Usman Ghani Khan, Waqar Mahmood, Hafiza Mahnoor Abbasi
Ontologies have gained a lot of popularity and recognition in the semantic web because of their extensive use in Internet-based applications. Ontologies are often considered a fine source of semantics and interoperability in all artificially smart systems. Exponential increase in unstructured data on the web has made automated acquisition of ontology from unstructured text a most prominent research area. Several methodologies exploiting numerous techniques of various fields (machine learning, text mining, knowledge representation and reasoning, information retrieval and natural language processing) are being proposed to bring some level of automation in the process of ontology acquisition from unstructured text...
January 1, 2018: Database: the Journal of Biological Databases and Curation
https://www.readbyqxmd.com/read/30290126/design-of-the-lifearc-index-set-and-retrospective-review-of-its-performance-a-collection-for-sharing
#12
Kristian Birchall, Andy Merritt, Afrah Sattikar, Catherine Kettleborough, Barbara Saxty
Building, curating, and maintaining a compound collection is an expensive operation, beyond the scope of most academic organizations. Here we describe the selection criteria used to compile the LifeArc diversity set from commercial suppliers and the process we undertook to generate our representative LifeArc index set. The aim was to avoid a "junk in, junk out" screen collection to increase chemical tractability going forward, while maximizing diversity. Using historical LifeArc screening data, we demonstrate that the index set was predictive of ligandability and that progressable hits could be identified by mining associated clusters within our larger diversity set...
October 5, 2018: SLAS Discovery
https://www.readbyqxmd.com/read/30259179/hann-a-hybrid-model-for-liver-syndrome-classification-by-feature-assortment-optimization
#13
L Anand, S P Syed Ibrahim
Early detection of any sort of disease is mandatory for effective medical treatment. Medical diagnosis relies heavily on Data Mining for automated disease classification and detection. It relies on data mining algorithms to examine medical data. Liver diseases have become more common these days with many new patients being diagnosed with Heptasis B and C. Early diagnosis of Liver Disorder is essential for treatment. It can be achieved by setting up intelligent systems for early diagnose and prognosis of Liver diseases...
September 27, 2018: Journal of Medical Systems
https://www.readbyqxmd.com/read/30256117/identifying-high-priority-proteins-across-the-human-diseasome-using-semantic-similarity
#14
Edward Lau, Vidya Venkatraman, Cody T Thomas, Joseph C Wu, Jennifer E Van Eyk, Maggie P Y Lam
Identifying the genes and proteins associated with a biological process or disease is a central goal of the biomedical research enterprise. However, relatively few systematic approaches are available that provide objective evaluation of the genes or proteins known to be important to a research topic, and hence researchers often rely on subjective evaluation of domain experts and laborious manual literature review. Computational bibliometric analysis, in conjunction with text mining and data curation, attempts to automate this process and return prioritized proteins in any given research topic...
October 9, 2018: Journal of Proteome Research
https://www.readbyqxmd.com/read/30255805/pharmacological-risk-factors-associated-with-hospital-readmission-rates-in-a-psychiatric-cohort-identified-using-prescriptome-data-mining
#15
Khader Shameer, M Mercedes Perez-Rodriguez, Roy Bachar, Li Li, Amy Johnson, Kipp W Johnson, Benjamin S Glicksberg, Milo R Smith, Ben Readhead, Joseph Scarpa, Jebakumar Jebakaran, Patricia Kovatch, Sabina Lim, Wayne Goodman, David L Reich, Andrew Kasarskis, Nicholas P Tatonetti, Joel T Dudley
BACKGROUND: Worldwide, over 14% of individuals hospitalized for psychiatric reasons have readmissions to hospitals within 30 days after discharge. Predicting patients at risk and leveraging accelerated interventions can reduce the rates of early readmission, a negative clinical outcome (i.e., a treatment failure) that affects the quality of life of patient. To implement individualized interventions, it is necessary to predict those individuals at highest risk for 30-day readmission. In this study, our aim was to conduct a data-driven investigation to find the pharmacological factors influencing 30-day all-cause, intra- and interdepartmental readmissions after an index psychiatric admission, using the compendium of prescription data (prescriptome) from electronic medical records (EMR)...
September 14, 2018: BMC Medical Informatics and Decision Making
https://www.readbyqxmd.com/read/30239692/a-pipeline-to-translate-glycosaminoglycan-sequences-into-3d-models-application-to-the-exploration-of-glycosaminoglycan-conformational-space
#16
Olivier Clerc, Julien Mariethoz, Alain Rivet, Frédérique Lisacek, Serge Pérez, Sylvie Ricard-Blum
Mammalian glycosaminoglycans are linear complex polysaccharides comprising heparan sulfate, heparin, dermatan sulfate, chondroitin sulfate, keratan sulfate and hyaluronic acid. They bind to numerous proteins and these interactions mediate their biological activities. GAG-protein interaction data reported in the literature are curated mostly in MatrixDB database (http://matrixdb.univ-lyon1.fr/). However, a standard nomenclature and a machine-readable format of GAGs together with bioinformatics tools for mining these interaction data are lacking...
September 18, 2018: Glycobiology
https://www.readbyqxmd.com/read/30231499/deep-learning-in-drug-discovery-and-medicine-scratching-the-surface
#17
REVIEW
Dibyendu Dana, Satishkumar V Gadhiya, Luce G St Surin, David Li, Farha Naaz, Quaisar Ali, Latha Paka, Michael A Yamin, Mahesh Narayan, Itzhak D Goldberg, Prakash Narayan
The practice of medicine is ever evolving. Diagnosing disease, which is often the first step in a cure, has seen a sea change from the discerning hands of the neighborhood physician to the use of sophisticated machines to use of information gleaned from biomarkers obtained by the most minimally invasive of means. The last 100 or so years have borne witness to the enormous success story of allopathy, a practice that found favor over earlier practices of medical purgatory and homeopathy. Nevertheless, failures of this approach coupled with the omics and bioinformatics revolution spurred precision medicine, a platform wherein the molecular profile of an individual patient drives the selection of therapy...
September 18, 2018: Molecules: a Journal of Synthetic Chemistry and Natural Product Chemistry
https://www.readbyqxmd.com/read/30224962/homology-based-loop-modeling-yields-more-complete-crystallographic-protein-structures
#18
Bart van Beusekom, Krista Joosten, Maarten L Hekkelman, Robbie P Joosten, Anastassis Perrakis
Inherent protein flexibility, poor or low-resolution diffraction data or poorly defined electron-density maps often inhibit the building of complete structural models during X-ray structure determination. However, recent advances in crystallographic refinement and model building often allow completion of previously missing parts. This paper presents algorithms that identify regions missing in a certain model but present in homologous structures in the Protein Data Bank (PDB), and 'graft' these regions of interest...
September 1, 2018: IUCrJ
https://www.readbyqxmd.com/read/30217057/proteomic-deep-mining-the-venom-of-the-red-headed-krait-bungarus-flaviceps
#19
Alex Chapeaurouge, Andreza Silva, Paulo Carvalho, Ryan J R McCleary, Cassandra Marie Modahl, Jonas Perales, R Manjunatha Kini, Stephen P Mackessy
The use of -omics technologies allows for the characterization of snake venom composition at a fast rate and at high levels of detail. In the present study, we investigated the protein content of Red-headed Krait ( Bungarus flaviceps ) venom. This analysis revealed a high diversity of snake venom protein families, as evidenced by high-throughput mass spectrometric analysis. We found all six venom protein families previously reported in a transcriptome study of the venom gland of B. flaviceps , including phospholipases A₂ (PLA₂s), Kunitz-type serine proteinase inhibitors (KSPIs), three-finger toxins (3FTxs), cysteine-rich secretory proteins (CRISPs), snaclecs, and natriuretic peptides...
September 13, 2018: Toxins
https://www.readbyqxmd.com/read/30173820/pubcasefinder-a-case-report-based-phenotype-driven-differential-diagnosis-system-for-rare-diseases
#20
Toyofumi Fujiwara, Yasunori Yamamoto, Jin-Dong Kim, Orion Buske, Toshihisa Takagi
Recently, to speed up the differential-diagnosis process based on symptoms and signs observed from an affected individual in the diagnosis of rare diseases, researchers have developed and implemented phenotype-driven differential-diagnosis systems. The performance of those systems relies on the quantity and quality of underlying databases of disease-phenotype associations (DPAs). Although such databases are often developed by manual curation, they inherently suffer from limited coverage. To address this problem, we propose a text-mining approach to increase the coverage of DPA databases and consequently improve the performance of differential-diagnosis systems...
September 6, 2018: American Journal of Human Genetics
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