1,723,057 research outputs found
Tutorial "Journal/Author Name Estimator" y "Open Journal Matcher" (Recursos y herramientas para evaluar revistas científicas)
No full textTutorial sobre las plataformas Journal Author Name Estimator y Open Journal Matcher dentro de la serie "Recursos y herramientas para evaluar revistas científicas"Peer reviewe
Matcher: An Open-Source Application for Translating Large Structure/Property Datasets into Insights for Drug Design
No full textTo solve recurring problems in drug discovery, matched molecular pair (MMP) analysis is used
to understand relationships between chemical structure and function. For the MMP analysis of
large datasets (>10,000 compounds), available tools lack flexible search and visualization
functionality and require computational expertise. Here we present Matcher, an open-source
application for MMP analysis, with novel search algorithms and fully automated querying-to-visualization that requires no programming expertise. Matcher enables unprecedented control
over the search and clustering of MMP transformations based on both variable fragment and
constant environment structure, which is critical for disentangling relevant and irrelevant data to
a given problem. Users can exert such control through a built-in chemical sketcher, and with a
few mouse clicks can navigate between resulting MMP transformations, statistics, property
distribution graphs and structures with raw experimental data, for confident and accelerated
decision making. Matcher can be used with any collection of structure/property data; here we
demonstrate usage with a public ChEMBL dataset of about 20,000 small molecules with
CYP3A4 and/or hERG inhibition data. Users can reproduce all examples demonstrated herein
via unique links within Matcher’s interface – a functionality that anyone can use to preserve and
share their own analyses. Matcher and all its dependencies are open-source with permissive
licenses and trivial containerized deployment, and is freely available at
https://github.com/Merck/Matcher. Matcher makes large structure/property datasets more
transparent than ever before and accelerates the data-driven solution of common problems in
drug discovery
A Deductive Pattern Matcher
No full textThis paper describes the design of a pattern matcher for a knowledge representation system called LOOM. The pattern matcher has a very rich pattern-forming language, and is logic-based, with a deductive mechanism which includes a truth-maintenance component as an integral part of the pattern-matching logic. The technology behind the LOOM matcher uses an inference engine called a classifier to perform the matches. The LOOM matcher is more expressive and more complete than previous classi cationbased pattern-matchers, and is expected to be significantly more efficient
Yet Another Matcher
No full textDiscovering correspondences between schema elements is a crucial task for data integration. Most matching tools are semi-automatic, e.g. an expert must tune some parameters (thresholds, weights, etc.). They mainly use several methods to combine and aggregate similarity measures. However, their quality results often decrease when one requires to integrate a new similarity measure or when matching particular domain schemas. This paper describes YAM (Yet Another Matcher), which is a matcher factory. Indeed, it enables the generation of a dedicated matcher for a given schema matching scenario, according to user inputs. Our approach is based on machine learning since schema matchers can be seen as classifiers. Several bunches of experiments run against matchers generated by YAM and traditional matching tools show how our approach (i) is able to generate the best matcher for a given scenario and (ii) easily integrates user preferences, namely recall and precision tradeoff
Yet Another Matcher
No full textDiscovering correspondences between schema elements is a crucial task for data integration. Most matching tools are semi-automatic, e.g. an expert must tune some parameters (thresholds, weights, etc.). They mainly use several methods to combine and aggregate similarity measures. However, their quality results often decrease when one requires to integrate a new similarity measure or when matching particular domain schemas. This paper describes YAM (Yet Another Matcher), which is a matcher factory. Indeed, it enables the generation of a dedicated matcher for a given schema matching scenario, according to user inputs. Our approach is based on machine learning since schema matchers can be seen as classifiers. Several bunches of experiments run against matchers generated by YAM and traditional matching tools show how our approach (i) is able to generate the best matcher for a given scenario and (ii) easily integrates user preferences, namely recall and precision tradeoff
Yet Another Matcher
No full textDiscovering correspondences between schema elements is a crucial task for data integration. Most matching tools are semi-automatic, e.g. an expert must tune some parameters (thresholds, weights, etc.). They mainly use several methods to combine and aggregate similarity measures. However, their quality results often decrease when one requires to integrate a new similarity measure or when matching particular domain schemas. This paper describes YAM (Yet Another Matcher), which is a matcher factory. Indeed, it enables the generation of a dedicated matcher for a given schema matching scenario, according to user inputs. Our approach is based on machine learning since schema matchers can be seen as classifiers. Several bunches of experiments run against matchers generated by YAM and traditional matching tools show how our approach (i) is able to generate the best matcher for a given scenario and (ii) easily integrates user preferences, namely recall and precision tradeoff
NMR Matcher
No full textNMR Matcher is an artificial intelligence analysis program using the Python language. The program translates 3D chemical small molecules into numerical expressions that can be understood by a computer. The program was developed based on the fact that in chemical reactions the product structures are often different from the predicted ones, and using NMR Matcher one can quickly evaluate whether the predicted structures and the product profiles agree. In the process of using NMR Macher, the user will input the structure of the predicted molecule and the NMR spectrum of the product. The spectrum input can contain both HNMR and CNMR 1D spectra. The program will compare the predicted NMR data with the input product NMR pattern based on the structure of the input molecule, and determine whether the reaction proceeds as predicted based on the similarity to the predicted product pattern. The purpose of NMR Matcher is to help undergraduate students better understand NMR patterns, improve experimental efficiency, and reduce human error. In conclusion, the NMR Matcher is a useful tool for undergraduate students in chemistry research and will help them to improve their research experience
The Clarivate Manuscript Matcher
No full textThe Clarivate Manuscript Matcher is a text prediction tool that can used by authors to identify potential journals to approach about publishing your manuscript. This short video introduction covers the essentials of how to register for an account and how to use the Clarivate Manuscript Matcher
A Holistic Schema Matcher
No full textThe capability of matching the schemas of the databases has led to its wide use in e-commerce, data integration, and data warehousing. In this paper we develop a holistic schema matcher, which provides a match between two schemas by combining the match results from the element-level techniques as well as instance-level techniques. The experimental results demonstrate good performance of the proposed matcher.</jats:p
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