Neural networks and other machine learning methods in cancer research

Authors:
Alfredo Vellido;Paulo J. G. Lisboa
Affiliations:
Department of Computing Languages and Systems. Technical University of Catalonia, Barcelona, Spain;School of Computing and Mathematical Sciences. Liverpool John Moores University, Liverpool, UK
Venue:
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Year:
2007

Citing 20
Cited 3

Bioinformatics: the machine learning approach

Bioinformatics: the machine learning approach
Gene Selection for Cancer Classification using Support Vector Machines

Machine Learning
A review of evidence of health benefit from artificial neural networks in medical intervention

Neural Networks
GenSo-FDSS: a neural-fuzzy decision support system for pediatric ALL cancer subtype identification using gene expression data

Artificial Intelligence in Medicine
2005 Special Issue: Ovarian cancer diagnosis by hippocampus and neocortex-inspired learning memory structures

Neural Networks - 2005 Special issue: IJCNN 2005
The use of artificial neural networks in decision support in cancer: A systematic review

Neural Networks
e-Science and artificial neural networks in cancer management: Research Articles

Concurrency and Computation: Practice & Experience - Selected Papers from the 2004 U.K. e-Science All Hands Meeting (AHM 2004)
Cell-nuclear data reduction and prognostic model selection in bladder tumor recurrence

Artificial Intelligence in Medicine
Missing data imputation through GTM as a mixture of t-distributions

Neural Networks
Fuzzy rough sets hybrid scheme for breast cancer detection

Image and Vision Computing
A new method to help diagnose cancers for small sample size

Expert Systems with Applications: An International Journal
Double-blind evaluation and benchmarking of survival models in a multi-centre study

Computers in Biology and Medicine
Artificial neural network for the joint modelling of discrete cause-specific hazards

Artificial Intelligence in Medicine
Evaluation of radiological features for breast tumour classification in clinical screening with machine learning methods

Artificial Intelligence in Medicine
Impact of missing data in evaluating artificial neural networks trained on complete data

Computers in Biology and Medicine
Neural networks for computer-aided diagnosis: detection of lung nodules in chest radiograms

IEEE Transactions on Information Technology in Biomedicine
A Bayesian neural network approach for modelling censored data with an application to prognosis after surgery for breast cancer

Artificial Intelligence in Medicine
Evolving connectionist systems for knowledge discovery from gene expression data of cancer tissue

Artificial Intelligence in Medicine
A multiple classifier system for early melanoma diagnosis

Artificial Intelligence in Medicine
Evolutionary computing for knowledge discovery in medical diagnosis

Artificial Intelligence in Medicine

Rule-Based Assistance to Brain Tumour Diagnosis Using LR-FIR

KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Feature Selection with Single-Layer Perceptrons for a Multicentre 1H-MRS Brain Tumour Database

IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Comparative diagnostic accuracy of linear and nonlinear feature extraction methods in a neuro-oncology problem

MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

Evidence-based medicine has grown in stature over the last three decades and is now regarded a key development of modern medicine. The evidence base can be heterogeneous, involving both qualitative knowledge and measured quantitative data. Machine Learning (ML) methods have also begun to establish themselves as an alternative and promising approach to computer-based data analysis in oncology, as this field moves gradually away from being the preserve of traditional statistical analysis. In this paper, we describe the main areas of cancer research in which ML methods are currently being applied, and briefly discuss some of the advantages and disadvantages of their application.