[id] => 268
[recorddate] => 2014:12:28:19:34:53
[lastupdate] => 2014:12:28:19:34:53
[type] => article
[place] => Sofia, Bulgaria
[subject] => chemistry - general; mathematics - probability theory; mathematics - statistics
[relatedworks] =>
• 2 (average):
  • Some applications of statistics in analytical chemistry, ?id=5
• 3 (low):
  • Shannon's entropy usage as statistic, ?id=289
[file] => ?f=268
[mime] => application/pdf
[size] => 463444
[pubname] => Biomath
[pubinfo] => Bulgarian Academy of Sciences
[pubkey] => 1314-684X, e1314-7218
[workinfo] => 2(1), a1309089 (11p), DOI: 10.11145/j.biomath.2013.09.089
[year] => 2013
[title] => Quantitative structure-activity relationships: linear regression modelling and validation strategies by example
[authors] => Sorana D. BOLBOACĂ, Lorentz JÄNTSCHI
[abstract] =>
Quantitative structure-activity relationships are mathematical models constructed based on the hypothesis that structure of chemical compounds is related to their biological activity. A linear regression model is often used to estimate and predict the nature of the relationships between a measured activity and some measure or calculated descriptors. Linear regression helps to answer main three questions: does the biological activity depend on structure information; if so, the nature of the relationship is linear; and if yes, how good is the model in prediction of the biological activity of new compounds. This manuscript presents the steps on linear regression analysis moving from theoretical knowledge to an example conducted on sets of endocrine disrupting chemicals.
[keywords] => robust regression; validation; diagnostic; predictive power; quantitative structure-activity relationships (QSARs)
[acknowledgment] => The authors are grateful to the organizers of the BIOMATH 2013 for the opportunity to present our results.