A New Structure Representation for RNA and Fast RNA Substructure Search

2016 ◽  
Author(s):  
Abdullah N. Arslan ◽  
Jithendar Anandan ◽  
Eric Fry ◽  
Rabindra Pandey ◽  
Keith Monschke
Keyword(s):  
Structure Representation ◽  
Substructure Search

Efficient RNA structure comparison algorithms

2017 ◽  
Vol 15 (06) ◽  
pp. 1740009 ◽  
Author(s):  
Abdullah N. Arslan ◽  
Jithendar Anandan ◽  
Eric Fry ◽  
Keith Monschke ◽  
Nitin Ganneboina ◽  
...  
Keyword(s):  
Rna Structure ◽  
Search Algorithm ◽  
Suffix Array ◽  
Rna Structures ◽  
Structure Comparison ◽  
Structure Representation ◽  
Substructure Search ◽  
Common Substructure ◽  
Search Tool ◽  
Comparison Algorithms

Recently proposed relative addressing-based ([Formula: see text]) RNA secondary structure representation has important features by which an RNA structure database can be stored into a suffix array. A fast substructure search algorithm has been proposed based on binary search on this suffix array. Using this substructure search algorithm, we present a fast algorithm that finds the largest common substructure of given multiple RNA structures in [Formula: see text] format. The multiple RNA structure comparison problem is NP-hard in its general formulation. We introduced a new problem for comparing multiple RNA structures. This problem has more strict similarity definition and objective, and we propose an algorithm that solves this problem efficiently. We also develop another comparison algorithm that iteratively calls this algorithm to locate nonoverlapping large common substructures in compared RNAs. With the new resulting tools, we improved the RNASSAC website (linked from http://faculty.tamuc.edu/aarslan ). This website now also includes two drawing tools: one specialized for preparing RNA substructures that can be used as input by the search tool, and another one for automatically drawing the entire RNA structure from a given structure sequence.

Comparison of search techniques (printed and computerised) with specific reference to the RTECS databank

1985 ◽  
Vol 10 (2) ◽  
pp. 79-86 ◽  
Author(s):  
Anne Costigan ◽  
Frances E. Wood ◽  
David Bawden
Keyword(s):  
Computer System ◽  
Comparative Evaluation ◽  
Computer Systems ◽  
Toxic Effects ◽  
Specific Reference ◽  
Substructure Search ◽  
Search Techniques ◽  
General Relevance ◽  
Text Searching

A comparative evaluation of three implementations of a large databank, the NIOSH Registry of Toxic Effects of Chem ical Substances, has been carried out. The three implementa tions are: a printed index, a text searching computer system, and a computerised chemical databank system, with substruc ture searching facilities. Seven test queries were used, with the aim of drawing conclusions of general relevance to chemical databank searching. The computer systems were shown to have advantages over printed indexes for several of the queries, including those involving an element of browsing. Substructure search facilities were especially advantageous. Aspects of indexing of data present, and the criteria for inclusion of types of data, were also highlighted.

Statistical Assessment of the Effects of Grain-Structure Representation and Micro-Properties on the Behavior of Bonded Block Models for Brittle Rock Damage Prediction

2021 ◽  
Vol 13 (14) ◽  
pp. 7889
Author(s):  
Carlos Efrain Contreras Inga ◽  
Gabriel Walton ◽  
Elizabeth Holley
Keyword(s):  
Grain Size ◽  
Grain Shape ◽  
Grain Structure ◽  
Minor Effect ◽  
Unique Contribution ◽  
Structure Generation ◽  
Structure Representation ◽  
The Real ◽  
Study Results ◽  
Block Models

The ability to predict the mechanical behavior of brittle rocks using bonded block models (BBM) depends on the accuracy of the geometrical representation of the grain-structure and the applied micro-properties. This paper evaluates the capabilities of BBMs for predictive purposes using an approach that employs published micro-properties in combination with a Voronoi BBM that properly approximates the real rock grain-structure. The Wausau granite, with Unconfined Compressive Strength (UCS) of 226 MPa and average grain diameter of 2 mm, is used to evaluate the effectiveness of the predictive approach. Four published sets of micro-properties calibrated for granites with similar mineralogy to the Wausau granite are used for the assessment. The effect of grain-structure representation in Voronoi BBMs is analyzed, considering grain shape, grain size and mineral arrangement. A unique contribution of this work is the explicit consideration of the effect of stochastic grain-structure generation on the obtained results. The study results show that the macro-properties of a rock can be closely replicated using the proposed approach. When using this approach, the micro-properties have a greater impact on the realism of the predictions than the specific grain-structure representation. The grain shape and grain size representations have a minor effect on the predictions for cases that do not deviate substantially from the real average grain geometry. However, the stochastic effect introduced by the use of randomly-generated Voronoi grain-structures can be significant, and this effect should be considered in future studies.

An Evaluation of a Substructure Search Screen System Based on Bond-Centered Fragments

1974 ◽  
Vol 14 (1) ◽  
pp. 44-48 ◽  
Author(s):  
George W. Adamson ◽  
Judith A. Bush ◽  
Alice H. W. McLure ◽  
Michael F. Lynch
Keyword(s):  
Substructure Search ◽  
Screen System

scholarly journals ChemBioServer 2.0: an advanced web server for filtering, clustering and networking of chemical compounds facilitating both drug discovery and repurposing

2020 ◽  
Vol 36 (8) ◽  
pp. 2602-2604 ◽  
Author(s):  
Evangelos Karatzas ◽  
Juan Eiros Zamora ◽  
Emmanouil Athanasiadis ◽  
Dimitris Dellis ◽  
Zoe Cournia ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Perfect Match ◽  
Web Server ◽  
Drug Repurposing ◽  
Chemical Compounds ◽  
Structural Similarity ◽  
Substructure Search ◽  
Similarity Network ◽  
Compound Libraries ◽  
Network Analysis Metrics

Abstract Summary ChemBioServer 2.0 is the advanced sequel of a web server for filtering, clustering and networking of chemical compound libraries facilitating both drug discovery and repurposing. It provides researchers the ability to (i) browse and visualize compounds along with their physicochemical and toxicity properties, (ii) perform property-based filtering of compounds, (iii) explore compound libraries for lead optimization based on perfect match substructure search, (iv) re-rank virtual screening results to achieve selectivity for a protein of interest against different protein members of the same family, selecting only those compounds that score high for the protein of interest, (v) perform clustering among the compounds based on their physicochemical properties providing representative compounds for each cluster, (vi) construct and visualize a structural similarity network of compounds providing a set of network analysis metrics, (vii) combine a given set of compounds with a reference set of compounds into a single structural similarity network providing the opportunity to infer drug repurposing due to transitivity, (viii) remove compounds from a network based on their similarity with unwanted substances (e.g. failed drugs) and (ix) build custom compound mining pipelines. Availability and implementation http://chembioserver.vi-seem.eu.

scholarly journals WHITE-BOX CARTOONIZATION USING AN EXTENDED GAN FRAMEWORK

2021 ◽  
Vol 5 (12) ◽  
Author(s):  
Amey Thakur ◽  
Hasan Rizvi ◽  
Mega Satish
Keyword(s):  
Smooth Surface ◽  
Generative Models ◽  
Surface Representation ◽  
High Quality ◽  
Generative Adversarial Network ◽  
Structure Representation ◽  
Texture Representation ◽  
Adversarial Network ◽  
Representation Structure ◽  
New Framework

In the present study, we propose to implement a new framework for estimating generative models via an adversarial process to extend an existing GAN framework and develop a white-box controllable image cartoonization, which can generate high-quality cartooned images/videos from real-world photos and videos. The learning purposes of our system are based on three distinct representations: surface representation, structure representation, and texture representation. The surface representation refers to the smooth surface of the images. The structure representation relates to the sparse colour blocks and compresses generic content. The texture representation shows the texture, curves, and features in cartoon images. Generative Adversarial Network (GAN) framework decomposes the images into different representations and learns from them to generate cartoon images. This decomposition makes the framework more controllable and flexible which allows users to make changes based on the required output. This approach overcomes any previous system in terms of maintaining clarity, colours, textures, shapes of images yet showing the characteristics of cartoon images.

scholarly journals A Document Reuse Tool for Communities of Practice

2010 ◽  
pp. 13-25
Author(s):  
Aida Boukottaya ◽  
Bernadette Charlier ◽  
Micaël Paquier ◽  
Loïc Merz ◽  
Stéphane Sire ◽  
...  
Keyword(s):  
Communities Of Practice ◽  
Virtual Communities ◽  
Structure Transformation ◽  
Structure Representation ◽  
Information Reuse ◽  
Automatic Structure ◽  
Virtual Communities Of Practice ◽  
Matching Techniques

Virtual communities of practice are gaining importance as mean of sharing and exchanging knowledge. In such environments, information reuse is of major concern. In this paper, the authors outline the importance of structuring documents in order to facilitate the reuse of their content. They show how explicit structure representation facilitates the understanding of the original documents and helps considerably in automating the reuse process. The authors propose two main tools: the first performs automatic structure transformation using matching techniques and the second performs structure and instances evolution in a transparent and an automatic manner.

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