Works (3)

Updated: July 5th, 2023 15:57

2019 journal article

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen


By: M. Menden*, D. Wang*, M. Mason*, B. Szalai*, K. Bulusu*, Y. Guan*, T. Yu*, J. Kang* ...

MeSH headings : ADAM17 Protein / antagonists & inhibitors; Antineoplastic Combined Chemotherapy Protocols / pharmacology; Antineoplastic Combined Chemotherapy Protocols / therapeutic use; Benchmarking; Biomarkers, Tumor / genetics; Cell Line, Tumor; Computational Biology / methods; Computational Biology / standards; Datasets as Topic; Drug Antagonism; Drug Resistance, Neoplasm / drug effects; Drug Resistance, Neoplasm / genetics; Drug Synergism; Genomics / methods; Humans; Molecular Targeted Therapy / methods; Mutation; Neoplasms / drug therapy; Neoplasms / genetics; Pharmacogenetics / methods; Pharmacogenetics / standards; Phosphatidylinositol 3-Kinases / genetics; Phosphoinositide-3 Kinase Inhibitors; Treatment Outcome
TL;DR: A large drug combination screen across cancer cell lines is provided to benchmark crowdsourced methods and to computationally predict drug synergies, and genomic rationale for synergy predictions are identified. (via Semantic Scholar)
Source: Web Of Science
Added: July 1, 2019

2005 journal article

Artificial neural network modeling of peptide mobility and peptide mapping in capillary zone electrophoresis


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

author keywords: peptide mapping; capillary zone electrophoresis; electrophoretic mobility; artificial neural networks
MeSH headings : Amino Acid Sequence; Cytochromes c / chemistry; Electrophoresis, Capillary / methods; Glucagon / chemistry; Molecular Sequence Data; Multivariate Analysis; Neural Networks, Computer; Oligopeptides / isolation & purification; Peptide Fragments / chemistry; Peptide Mapping / methods; Regression Analysis
TL;DR: The present model exhibits better robustness than the MLR models in predicting CZE mobilities of a diverse data set at different experimental conditions and indicates the non-linear characteristics of the electrophoretic mobility of peptides. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Source: Web Of Science
Added: August 6, 2018

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 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© (2024) 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.