Practical Graph Isomorphism for Graphlet Data Mining in Protein Structures

2012 ◽  
pp. 345-360
Author(s):  
Carsten Henneges ◽  
Christoph Behle ◽  
Andreas Zell
Keyword(s):  
Data Mining ◽  
Protein Structures ◽  
Graph Isomorphism

Complex Biological Data Mining and Knowledge Discovery

Biotechnology ◽  
2019 ◽  
pp. 305-321
Author(s):  
Fatima Kabli
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Dna Sequences ◽  
Protein Structures ◽  
Extraction Process ◽  
Biological Data ◽  
Knowledge Discovery In Databases ◽  
Complex Data ◽  
Mining Methods ◽  
Scientific Challenge

The mass of data available on the Internet is rapidly increasing; the complexity of this data is discussed at the level of the multiplicity of information sources, formats, modals, and versions. Facing the complexity of biological data, such as the DNA sequences, protein sequences, and protein structures, the biologist cannot simply use the traditional techniques to analyze this type of data. The knowledge extraction process with data mining methods for the analysis and processing of biological complex data is considered a real scientific challenge in the search for systematically potential relationships without prior knowledge of the nature of these relationships. In this chapter, the authors discuss the Knowledge Discovery in Databases process (KDD) from the Biological Data. They specifically present a state of the art of the best known and most effective methods of data mining for analysis of the biological data and problems of bioinformatics related to data mining.

scholarly journals Data mining of metal ion environments present in protein structures

2008 ◽  
Vol 102 (9) ◽  
pp. 1765-1776 ◽  
Author(s):  
Heping Zheng ◽  
Maksymilian Chruszcz ◽  
Piotr Lasota ◽  
Lukasz Lebioda ◽  
Wladek Minor
Keyword(s):  
Data Mining ◽  
Metal Ion ◽  
Protein Structures

Complex Biological Data Mining and Knowledge Discovery

2018 ◽  
pp. 303-320
Author(s):  
Fatima Kabli
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Dna Sequences ◽  
Protein Structures ◽  
Extraction Process ◽  
Biological Data ◽  
Knowledge Discovery In Databases ◽  
Complex Data ◽  
Mining Methods ◽  
Scientific Challenge

The mass of data available on the Internet is rapidly increasing; the complexity of this data is discussed at the level of the multiplicity of information sources, formats, modals, and versions. Facing the complexity of biological data, such as the DNA sequences, protein sequences, and protein structures, the biologist cannot simply use the traditional techniques to analyze this type of data. The knowledge extraction process with data mining methods for the analysis and processing of biological complex data is considered a real scientific challenge in the search for systematically potential relationships without prior knowledge of the nature of these relationships. In this chapter, the authors discuss the Knowledge Discovery in Databases process (KDD) from the Biological Data. They specifically present a state of the art of the best known and most effective methods of data mining for analysis of the biological data and problems of bioinformatics related to data mining.

scholarly journals A C++ library for protein sub-structure search

2020 ◽  
Author(s):  
Jianfu Zhou ◽  
Gevorg Grigoryan
Keyword(s):  
Data Mining ◽  
Structural Biology ◽  
Protein Structures ◽  
Structural Motif ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Structure Search ◽  
Program Interface ◽  
Programmatic Access

AbstractSummaryMASTER is a previously published algorithm for protein sub-structure search. Given a database of protein structures and a query structural motif, composed of multiple disjoint segments, it finds all sub-structures from the database that align onto the query to within a pre-specified backbone root-mean-square deviation. Here, we present an improved version of the algorithm, MASTER v.2, in the form of an open-source C++ Application Program Interface library, thereby providing programmatic access to structure search functionality. An entirely reorganized approach to database representation now enables large structural databases to be stored in memory, further simplifying development of automated search-based methods. Given the increasingly important role of structure-based data mining, our improved implementation should find ample uses in structural biology applications.AvailabilityMASTER is available at https://grigoryanlab.org/master/[email protected]

Reducing the bottleneck of graph-based data mining by improving the efficiency of labeled graph isomorphism testing

2014 ◽  
Vol 91 ◽  
pp. 17-33 ◽  
Author(s):  
Shu-Ming Hsieh ◽  
Chiun-Chieh Hsu ◽  
Yen-Wu Ti ◽  
Chi-Jung Kuo
Keyword(s):  
Data Mining ◽  
Graph Isomorphism ◽  
Labeled Graph

Insights into the biochemical and functional characterization of sortase E transpeptidase of Corynebacterium glutamicum

2019 ◽  
Vol 476 (24) ◽  
pp. 3835-3847 ◽  
Author(s):  
Aliyath Susmitha ◽  
Kesavan Madhavan Nampoothiri ◽  
Harsha Bajaj
Keyword(s):  
Protein Engineering ◽  
Cell Attachment ◽  
Functional Characterization ◽  
Protein Structures ◽  
Structural Similarity ◽  
Surface Proteins ◽  
Sortase A ◽  
Peptide Cleavage ◽  
New Findings

Most Gram-positive bacteria contain a membrane-bound transpeptidase known as sortase which covalently incorporates the surface proteins on to the cell wall. The sortase-displayed protein structures are involved in cell attachment, nutrient uptake and aerial hyphae formation. Among the six classes of sortase (A–F), sortase A of S. aureus is the well-characterized housekeeping enzyme considered as an ideal drug target and a valuable biochemical reagent for protein engineering. Similar to SrtA, class E sortase in GC rich bacteria plays a housekeeping role which is not studied extensively. However, C. glutamicum ATCC 13032, an industrially important organism known for amino acid production, carries a single putative sortase (NCgl2838) gene but neither in vitro peptide cleavage activity nor biochemical characterizations have been investigated. Here, we identified that the gene is having a sortase activity and analyzed its structural similarity with Cd-SrtF. The purified enzyme showed a greater affinity toward LAXTG substrate with a calculated KM of 12 ± 1 µM, one of the highest affinities reported for this class of enzyme. Moreover, site-directed mutation studies were carried to ascertain the structure functional relationship of Cg-SrtE and all these are new findings which will enable us to perceive exciting protein engineering applications with this class of enzyme from a non-pathogenic microbe.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Heart Rate Variability, Emotions, and Music

2010 ◽  
Vol 24 (2) ◽  
pp. 112-119 ◽  
Author(s):  
F. Riganello ◽  
A. Candelieri ◽  
M. Quintieri ◽  
G. Dolce
Keyword(s):  
Data Mining ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Vegetative State ◽  
Low Frequency ◽  
Emotional Reactions ◽  
Heart Beat ◽  
Healthy Controls ◽  
Frequency Spectra ◽  
Emotional Value

The purpose of the study was to identify significant changes in heart rate variability (an emerging descriptor of emotional conditions; HRV) concomitant to complex auditory stimuli with emotional value (music). In healthy controls, traumatic brain injured (TBI) patients, and subjects in the vegetative state (VS) the heart beat was continuously recorded while the subjects were passively listening to each of four music samples of different authorship. The heart rate (parametric and nonparametric) frequency spectra were computed and the spectra descriptors were processed by data-mining procedures. Data-mining sorted the nu_lf (normalized parameter unit of the spectrum low frequency range) as the significant descriptor by which the healthy controls, TBI patients, and VS subjects’ HRV responses to music could be clustered in classes matching those defined by the controls and TBI patients’ subjective reports. These findings promote the potential for HRV to reflect complex emotional stimuli and suggest that residual emotional reactions continue to occur in VS. HRV descriptors and data-mining appear applicable in brain function research in the absence of consciousness.

Post, Mine, and Be Disturbed: Social Media Data Mining

PsycCRITIQUES ◽  
2016 ◽  
Vol 61 (51) ◽  
Author(s):  
Daniel Keyes
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Social Media Data ◽  
Media Data
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