Computational geometry: an introduction
Computational geometry: an introduction
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Data quality and systems theory
Communications of the ACM
The impact of poor data quality on the typical enterprise
Communications of the ACM
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Properties of support vector machines
Neural Computation
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Data mining: concepts and techniques
Data mining: concepts and techniques
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Machine Learning
Machine Learning
Experiments with Noise Filtering in a Medical Domain
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Feature Selection for Clustering
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
An introduction to variable and feature selection
The Journal of Machine Learning Research
Benchmarking Attribute Selection Techniques for Discrete Class Data Mining
IEEE Transactions on Knowledge and Data Engineering
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Identifying and eliminating mislabeled training instances
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Data mining techniques for cancer detection using serum proteomic profiling
Artificial Intelligence in Medicine
WISB '06 Proceedings of the 2006 workshop on Intelligent systems for bioinformatics - Volume 73
Guilt-by-association feature selection: Identifying biomarkers from proteomic profiles
Journal of Biomedical Informatics
Expert Systems with Applications: An International Journal
Cancer classification using kernelized fuzzy C-means
FS'08 Proceedings of the 9th WSEAS International Conference on Fuzzy Systems
Classification of Proteomic Signals by Block Kriging Error Matching
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
Classification of Mass Spectrometry Based Protein Markers by Kriging Error Matching
MDA '08 Proceedings of the 3rd international conference on Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry
Fuzzy rule base classifier fusion for protein mass spectra based ovarian cancer diagnosis
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
A hybrid random subspace classifier fusion approach for protein mass spectra classification
EvoBIO'08 Proceedings of the 6th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Computer Methods and Programs in Biomedicine
Commentary: Clinical decision support: Converging toward an integrated architecture
Journal of Biomedical Informatics
Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data
Information Sciences: an International Journal
An Inference Engine for Estimating Outside States of Clinical Test Items
ACM Transactions on Management Information Systems (TMIS)
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Currently, the best way to reduce the mortality of cancer is to detect and treat it in the earliest stages. Technological advances in genomics and proteomics have opened a new realm of methods for early detection that show potential to overcome the drawbacks of current strategies. In particular, pattern analysis of mass spectra of blood samples has attracted attention as an approach to early detection of cancer. Mass spectrometry provides rapid and precise measurements of the sizes and relative abundances of the proteins present in a complex biological/chemical mixture. This article presents a review of the development of clinical decision support systems using mass spectrometry from a machine learning perspective. The literature is reviewed in an explicit machine learning framework, the components of which are preprocessing, feature extraction, feature selection, classifier training, and evaluation.