Graph-Based Structural Data Mining in Cognitive Pattern Interpretation

2006 ◽  
pp. 349-350 ◽  
Author(s):  
Lidia Ogiela ◽  
Ryszard Tadeusiewicz ◽  
Marek R. Ogiela
Keyword(s):  
Data Mining ◽  
Structural Data ◽  
Cognitive Pattern

scholarly journals Diaminoimidazopyrimidines: Access via the Groebke-Blackburn-Bienaymé Reaction and Structural Data Mining

2020 ◽  
Author(s):  
Markella Konstantinidou ◽  
Zlata Boiarska ◽  
Roberto Butera ◽  
Constantinos G. Neochoritis ◽  
Katarzyna Kurpiewska ◽  
...  
Keyword(s):  
Data Mining ◽  
Structural Data

Novel function discovery through sequence and structural data mining

2016 ◽  
Vol 38 ◽  
pp. 53-61 ◽  
Author(s):  
Briallen Lobb ◽  
Andrew C Doxey
Keyword(s):  
Data Mining ◽  
Structural Data ◽  
Function Discovery

Comparison of sequence and structure-based datasets for nonredundant structural data mining

2005 ◽  
Vol 60 (4) ◽  
pp. 577-583 ◽  
Author(s):  
Carmen K. Chu ◽  
Lina L. Feng ◽  
Merridee A. Wouters
Keyword(s):  
Data Mining ◽  
Structural Data

scholarly journals Text and Structural Data Mining of Influenza Mentions in Web and Social Media

2010 ◽  
Vol 7 (2) ◽  
pp. 596-615 ◽  
Author(s):  
Courtney Corley ◽  
Diane Cook ◽  
Armin Mikler ◽  
Karan Singh
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Structural Data

scholarly journals Data Mining Approach of Accident Occurrences Identification with Effective Methodology and Implementation

2018 ◽  
Vol 8 (5) ◽  
pp. 4033 ◽  
Author(s):  
Meenu Gupta ◽  
Vijender Kumar Solanki ◽  
Vijay Kumar Singh ◽  
Vicente García-Díaz
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Structural Data ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Classification Algorithms ◽  
Research Approach ◽  
Word Count ◽  
Data Mining Approach ◽  
Frequency Calculation

Data mining is used in various domains of research to identify a new cause for tan effect in the society over the globe. This article includes the same reason for using the data mining to identify the Accident Occurrences in different regions and to identify the most valid reason for happening accidents over the globe. Data Mining and Advanced Machine Learning algorithms are used in this research approach and this article discusses about hyperline, classifications, pre-processing of the data, training the machine with the sample datasets which are collected from different regions in which we have structural and semi-structural data. We will dive into deep of machine learning and data mining classification algorithms to find or predict something novel about the accident occurrences over the globe. We majorly concentrate on two classification algorithms to minify the research and task and they are very basic and important classification algorithms. SVM (Support vector machine), CNB Classifier. This discussion will be quite interesting with WEKA tool for CNB classifier, Bag of Words Identification, Word Count and Frequency Calculation.

VeriNA3d: an R package for nucleic acids data mining

2019 ◽  
Vol 35 (24) ◽  
pp. 5334-5336 ◽  
Author(s):  
Diego Gallego ◽  
Leonardo Darré ◽  
Pablo D Dans ◽  
Modesto Orozco
Keyword(s):  
Data Mining ◽  
Nucleic Acids ◽  
High Throughput ◽  
Structural Data ◽  
R Package ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Rna Structures ◽  
High Throughput Analysis ◽  
Single Structure

Abstract Summary veriNA3d is an R package for the analysis of nucleic acids structural data, with an emphasis in complex RNA structures. In addition to single-structure analyses, veriNA3d also implements functions to handle whole datasets of mmCIF/PDB structures that could be retrieved from public/local repositories. Our package aims to fill a gap in the data mining of nucleic acids structures to produce flexible and high throughput analysis of structural databases. Availability and implementation http://mmb.irbbarcelona.org/gitlab/dgallego/veriNA3d. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Three-Dimensional Protein Structural Data Mining Based on the Glycine Filter Reduced Representation

2005 ◽  
Vol 4 (2) ◽  
pp. 33-42
Author(s):  
Hiroaki KATO ◽  
Shin-ichi CHIKAMATSU ◽  
Yoshimasa TAKAHASHI ◽  
Hidetsugu ABE
Keyword(s):  
Data Mining ◽  
Three Dimensional ◽  
Structural Data ◽  
Reduced Representation
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