M. Hassanisadi

Works (1)

Updated: July 5th, 2023 15:57

2005 journal article

Prediction of electrophoretic mobilities of peptides in capillary zone electrophoresis by quantitative structure-mobility relationships using the offord model and artificial neural networks

ELECTROPHORESIS, 26(10), 1874–1885.

By: M. Jalali-Heravi n, Y. Shen n, M. Hassanisadi n & M. Khaledi n

author keywords: artificial neural networks; capillary zone electrophoresis; electrophoretic mobility; peptide separation and mapping; structure-mobility relationship
MeSH headings : Electrophoresis, Capillary / methods; Neural Networks, Computer; Peptides / chemistry; Peptides / isolation & purification; Regression Analysis; Reproducibility of Results; Structure-Activity Relationship
TL;DR: A 3–4–1 back propagation artificial neural networks (BP‐ANN) model resulted in a significant improvement in the predictive ability of the QSMR over the MLR treatment, especially for peptides of higher charges that contain basic amino acids arginine, histidine, and lysine. (via Semantic Scholar)
UN Sustainable Development Goals Color Wheel
UN Sustainable Development Goal Categories
Source: Web Of Science
Added: August 6, 2018

Citation Index includes data from a number of different sources. If you have questions about the sources of data in the Citation Index or need a set of data which is free to re-distribute, please contact us.

Certain data included herein are derived from the Web of Science© and InCites© (2025) of Clarivate Analytics. All rights reserved. You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.