Chemometrics and Intelligent Laboratory Systems



【Chemometrics and Intelligent Laboratory Systems】介绍
英文名称:Chemometrics and Intelligent Laboratory Systems
中文名称:,
期刊主页:http://www.elsevier.com/locate/chemolab
出版地:U.S

Chemometrics and Intelligent Laboratory Systems publishes original research papers, short communications, reviews, tutorials and software descriptions reporting on novel developments in techniques for chemistry and related disciplines that are characterised by the application of statistical and computer methods.
Chemometrics is the chemical discipline that uses mathematical and statistical methods to design or select optimal procedures and experiments, and to provide maximum chemical information by analysing chemical data.
The journal deals with the following topics:
1) Development of new statistical, mathematical and chemometrical methods for Chemistry and related fields (Environmental Chemistry, Biochemistry, Toxicology, System Biology etc.)
2) Applications of chemometrics to all branches of Chemistry and related fields (typical domains of interest are: process control, experimental design, data mining, signal processing, supervised modelling, decision making, robust statistics, mixture analysis, multivariate calibration etc.)
3) Development of new software
4) Well characterized data sets to test performance for the new methods and software.

Welcome to the online submission and editorial system for Chemometrics and Intelligent Laboratory Systems.
http://ees.elsevier.com/chemolab/

实验室在此刊物上发表的文章
  1. Investigation of the proteins folding rates and their properties of amino acid networks

  2. Chemometrics and Intelligent Laboratory Systems,(2010) 123–129

    Yaping Fang  , DaiChuan Ma  , Menglong Li*  , Zhining Wen  , YuanBo DIAO 

    Abstract:  

    The mechanism of protein folding is an important problem in molecular biology. It is usually thought that protein folding is a complex system process related to the entire molecule. In this article, we have investigated 78 structures of folding proteins in native state, from complex networks perspective, to understand the role of topological parameters in proteins folding kinetics. The 31 parameters were calculated based on the amino acid networks of the folding proteins. The relationship between those parameters and protein folding rates has been systematically analyzed. Our results show that the significant parameters between two-state and multi-state folding proteins correlate well with the folding rates of proteins. It is also found that classifying the proteins into different classes can improve the correlation coefficient from 0.926 to 0.983 between the parameters and folding rates of two- and multistate proteins, respectively. Genetic Algorithms–Multiple Linear Regression (GA–MLR) was adopted to select the best subset parameters from the whole 31 parameters to construct the MLR model to avoid overfitting. Ourmethods showa correlation coefficient of 0.921 for the all folding proteins based on the classification of the folding proteins. The results indicate that the general topological parameters of the amino acids networks of the folding proteins can effectively represent the structural and functional properties, such as the rates of folding.