Literature DB >> 19065803

Overview of artificial neural networks.

Jinming Zou1, Yi Han, Sung-Sau So.   

Abstract

The artificial neural network (ANN), or simply neural network, is a machine learning method evolved from the idea of simulating the human brain. The data explosion in modem drug discovery research requires sophisticated analysis methods to uncover the hidden causal relationships between single or multiple responses and a large set of properties. The ANN is one of many versatile tools to meet the demand in drug discovery modeling. Compared to a traditional regression approach, the ANN is capable of modeling complex nonlinear relationships. The ANN also has excellent fault tolerance and is fast and highly scalable with parallel processing. This chapter introduces the background of ANN development and outlines the basic concepts crucially important for understanding more sophisticated ANN. Several commonly used learning methods and network setups are discussed briefly at the end of the chapter.

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Year:  2008        PMID: 19065803     DOI: 10.1007/978-1-60327-101-1_2

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  30 in total

1.  Applying principles from complex systems to studying the efficacy of CAM therapies.

Authors:  Andrew C Ahn; Richard L Nahin; Carlo Calabrese; Susan Folkman; Elizabeth Kimbrough; Jacob Shoham; Aviad Haramati
Journal:  J Altern Complement Med       Date:  2010-09       Impact factor: 2.579

2.  Discovery of Influenza A virus neuraminidase inhibitors using support vector machine and Naïve Bayesian models.

Authors:  Wenwen Lian; Jiansong Fang; Chao Li; Xiaocong Pang; Ai-Lin Liu; Guan-Hua Du
Journal:  Mol Divers       Date:  2015-12-21       Impact factor: 2.943

3.  Improved accuracy of anticoagulant dose prediction using a pharmacogenetic and artificial neural network-based method.

Authors:  Hussain A Isma'eel; George E Sakr; Robert H Habib; Mohamad Musbah Almedawar; Nathalie K Zgheib; Imad H Elhajj
Journal:  Eur J Clin Pharmacol       Date:  2013-12-03       Impact factor: 2.953

Review 4.  Arterial Stiffness and Coronary Ischemia: New Aspects and Paradigms.

Authors:  Alexandre Vallée; Alexandre Cinaud; Athanase Protogerou; Yi Zhang; Jirar Topouchian; Michel E Safar; Jacques Blacher
Journal:  Curr Hypertens Rep       Date:  2020-01-10       Impact factor: 5.369

5.  Transcript signature predicts tissue NK cell content and defines renal cell carcinoma subgroups independent of TNM staging.

Authors:  Judith Eckl; Alexander Buchner; Petra U Prinz; Rainer Riesenberg; Sabine I Siegert; Robert Kammerer; Peter J Nelson; Elfriede Noessner
Journal:  J Mol Med (Berl)       Date:  2011-08-26       Impact factor: 4.599

6.  Machine learning predicts risk of cerebrospinal fluid shunt failure in children: a study from the hydrocephalus clinical research network.

Authors:  Andrew T Hale; Jay Riva-Cambrin; John C Wellons; Eric M Jackson; John R W Kestle; Robert P Naftel; Todd C Hankinson; Chevis N Shannon
Journal:  Childs Nerv Syst       Date:  2021-01-30       Impact factor: 1.475

Review 7.  Computational approaches for translational clinical research in disease progression.

Authors:  Mary F McGuire; Madurai Sriram Iyengar; David W Mercer
Journal:  J Investig Med       Date:  2011-08       Impact factor: 2.895

8.  Artificial neural network modeling using clinical and knowledge independent variables predicts salt intake reduction behavior.

Authors:  Hussain A Isma'eel; George E Sakr; Mohamad M Almedawar; Jihan Fathallah; Torkom Garabedian; Savo Bou Zein Eddine; Lara Nasreddine; Imad H Elhajj
Journal:  Cardiovasc Diagn Ther       Date:  2015-06

Review 9.  Risk estimation and risk prediction using machine-learning methods.

Authors:  Jochen Kruppa; Andreas Ziegler; Inke R König
Journal:  Hum Genet       Date:  2012-07-03       Impact factor: 4.132

10.  Machine Learning to Develop and Internally Validate a Predictive Model for Post-operative Delirium in a Prospective, Observational Clinical Cohort Study of Older Surgical Patients.

Authors:  Annie M Racine; Douglas Tommet; Madeline L D'Aquila; Tamara G Fong; Yun Gou; Patricia A Tabloski; Eran D Metzger; Tammy T Hshieh; Eva M Schmitt; Sarinnapha M Vasunilashorn; Lisa Kunze; Kamen Vlassakov; Ayesha Abdeen; Jeffrey Lange; Brandon Earp; Bradford C Dickerson; Edward R Marcantonio; Jon Steingrimsson; Thomas G Travison; Sharon K Inouye; Richard N Jones
Journal:  J Gen Intern Med       Date:  2020-10-19       Impact factor: 5.128

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