Evolutionary computation in medicine: an overview

Peña-Reyes, Carlos Andrés; Sipper, Moshe

doi:10.1016/s0933-3657(99)00047-0

Cited by 122 publications

(65 citation statements)

References 68 publications

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“…A method of feature selection that provides readily interpretable results in PLSR involves the method of Jack-knifing developed by Martens and colleagues, whereby the uncertainty variance in the regression coefficients, B, in the PLS model is estimated for a model with optimal complexity, while variables which do not possess regression coefficients that are statistically significant at a certain level of confidence are eliminated using a t-test [76]. Other methods that employ evolutionary computational techniques such as genetic algorithms are less readily interpretable, but very rapidly decompose the spectral dataset into a small number of variables that maximize the predictive capacity of the models [77], and can be used with any regression algorithm. These algorithms are particularly suited to this type of minimization problem since the search space (i.e.…”

Section: Quantitative Multivariate Analytical Methodologiesmentioning

confidence: 99%

“…These algorithms are particularly suited to this type of minimization problem since the search space (i.e. the number of combinations of variables that may be input to the regression models) is vast, and standard exhaustive search techniques are therefore unrealistic [77]. Genetic algorithms are one such option that have been successfully utilised in multivariate regression problems [73].…”

Section: Quantitative Multivariate Analytical Methodologiesmentioning

confidence: 99%

“…Individual chromosomes are selected and rejected by virtue of their 'fitness', i.e. the value of a multivariate selection criterion that must either be maximised or minimised depending on the evolution criteria [73,77].…”

Section: Quantitative Multivariate Analytical Methodologiesmentioning

confidence: 99%

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Spectroscopic and chemometric approaches to radiobiological analyses

Meade

Byrne²,

Lyng

2010

Mutation Research/Reviews in Mutation Research

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Vibrational spectroscopy is an attractive modality for the analysis of biological samples, providing a complete non-invasive acquisition of the biochemical fingerprint of the sample. It has been demonstrated that this data provides the means to assay multiple functional responses of a biological system at a spatial resolution as low as a micron within the sample. As the interaction of ionizing radiation with biological systems involves chemical reactions between the products of radiation induced damage and various structural and functional units within the cell, the vibrational spectroscopic modalities have received attention as potential measurement platforms for the in-situ examination of the chemistry of biological species in radiobiology. This presents challenges in relation to sample preparation and the construction of suitable analytical methodologies. In this work protocols for sample preparation and approaches to multivariate analysis of vibrational spectra in radiobiological analysis are detailed and the utility of the methodology in analyzing the evolution of biochemical responses to radiobiological damage are highlighted.

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Section: Quantitative Multivariate Analytical Methodologiesmentioning

confidence: 99%

Section: Quantitative Multivariate Analytical Methodologiesmentioning

confidence: 99%

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Spectroscopic and chemometric approaches to radiobiological analyses

Meade

Byrne²,

Lyng

2010

Mutation Research/Reviews in Mutation Research

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“…Finally, mutation rules apply random changes to individual parents to form children. GA is designed to simulate the evolutionary processes that occur in nature [32,33].…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

3D Image Analysis and Artificial Intelligence for Bone Disease Classification

et al. 2009

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International audienceIn order to prevent bone fractures due to disease and ageing of the population, and to detect problems while still in their early stages, 3D bone micro architecture needs to be investigated and characterized. Here, we have developed various image processing and simulation techniques to investigate bone micro architecture and its mechanical stiffness. We have evaluated morphological, topological and mechanical bone features using artificial intelligence methods. A clinical study is carried out on two populations of arthritic and osteoporotic bone samples. The performances of Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector Machines (SVM) and Genetic Algorithm (GA) in classifying the different samples have been compared. Results show that the best separation success (100 %) is achieved with Genetic Algorithm

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“…VOLUTIONARY algorithms (EAs) have proven to be efficient optimisation techniques in various domains [1], including medicine [2] and medical imagery [3], [4], [5]. However their use in tomographic reconstruction has been largely overlooked.…”

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confidence: 99%

PET reconstruction using a cooperative coevolution strategy in LOR space

Vidal

Louchet

Rocchisani

et al. 2009

2009 IEEE Nuclear Science Symposium Conference Record (NSS/MIC)

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Abstract-This paper presents preliminary results of a novel method that takes advantage of artificial evolution for positron emission tomography (PET) reconstruction. Fully 3D tomographic reconstruction in PET requires high computing power and leads to many challenges. To date, the use of such methods is still restricted due to the heavy computing power needed. Evolutionary algorithms have proven to be efficient optimisation techniques in various domains. However the use of evolutionary computation in tomographic reconstruction has been largely overlooked. We propose a computer-based algorithm for fully 3D reconstruction in PET based on artificial evolution and evaluate its relevance.

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Evolutionary computation in medicine: an overview

Cited by 122 publications

References 68 publications

Spectroscopic and chemometric approaches to radiobiological analyses

Spectroscopic and chemometric approaches to radiobiological analyses

3D Image Analysis and Artificial Intelligence for Bone Disease Classification

PET reconstruction using a cooperative coevolution strategy in LOR space

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