Single and multiple time–point artificial neural networks models for predicting the survival of gastric cancer patients
The extensive availability of recent computational models and data mining techniques for data analysis calls for researchers and practitioners in the medical field to opt for the most suitable strategies to confront clinical prediction problems. In many clinical research work, the main outcome...
Main Author: | Dezfouli, Hamid Nilsaz |
---|---|
Format: | Thesis |
Language: | English |
Published: |
2016
|
Subjects: | |
Online Access: | http://psasir.upm.edu.my/id/eprint/67515/1/IPM%202016%2015%20IR.pdf |
Similar Items
-
Protein expression and gene analyses of HER2, NM23, and K-RAS in gastric cancer and Helicobacter pylori-associated gastritis
by: Samsudin, Nurulhafizah
Published: (2012) -
Protein expression of TGF-β, SMAD2 and RUNX3 in normal stomach, chronic gastritis and gastric adenocarcinoma
by: Ismail, Ahmad Zharif
Published: (2016) -
Analysis of HSP27, APC and β-catenin expressions in gastric cancer, chronic atrophic gastritis and Helicobacter pylori-associated chronic gastritis
by: Tay, Tan Chow
Published: (2013) -
Therapeutic potentials of bone marrow derived mesenchymal stem cells in averting organ damage due to rifampicin induced toxicity in animal model
by: Lawal, Danjuma
Published: (2018) -
Food and feeding habits of Omobranchus sp. (Blenniidae: Omobranchini) larvae in the seagrass-mangrove ecosystem of Johor Strait, Malaysia
by: Ara, Roushon, et al.
Published: (2016)