Jason H. Moore

Janet Clegg

Elena Marchiori

Marylyn Ritchie

Stephen Smith

Artificial Evolution Methods in the Biological and Biomedical Sciences

Algorithms that minimize putative synapomorphy in an alignment
cannot be directly implemented since trivial cases with concatenated
sequences would be selected because they would imply a minimum number
of events to be explained (e.g., a single insertion/deletion would be
required to explain divergence among two sequences). Therefore, indirect
measures to approach parsimony need to be implemented. In this
paper, we thoroughly present a Global Criterion for Sequence Alignment
(GLOCSA) that uses a scoring function to globally rate multiple
alignments aiming to produce matrices that minimize the number of putative
synapomorphies. We also present a Genetic Algorithm that uses
GLOCSA as the objective function to produce sequence alignments refining
alignments previously generated by additional existing alignment
tools (we recommend MUSCLE). We show that in the example cases our
GLOCSA-guided Genetic Algorithm (GGGA) does improve the GLOCSA
values, resulting in alignments that imply less putative synapomorphies.

Edgar D. Arenas-D&#237;az

Helga Ochoterena

Katya Rodr&#237;guez-V&#225;zquez

Multiple Sequence Alignment Using a Genetic Algorithm and GLOCSA

This paper presents a novel approach based on an improved Conserved Self Pattern Recognition Algorithm to analyze cytological characteristics of breast fine-needle aspirates (FNAs) for clinical breast cancer diagnosis. A novel detection strategy by coupling domain knowledge and randomized methods is proposed to resolve conflicts on anomaly detection between two types of detectors investigated in our earlier work on Conserved Self Pattern Recognition Algorithm (CSPRA). The improved CSPRA is applied to detect the malignant cases using clinical breast cancer data collected by Dr. Wolberg (1990), and the results are evaluated for performance measure (detection rate and false alarm rate). Results show that our approach has promising performance on breast cancer diagnosis and great potential in the area of clinical diagnosis. Effects of parameters setting in the CSPRA are discussed, and the experimental results are compared with the previous works.

Senhua Yu

Dipankar Dasgupta

Conserved Self Pattern Recognition Algorithm with Novel Detection Strategy Applied to Breast Cancer Diagnosis

Understanding mechanisms for the evolution of barriers to gene flow within interbreeding populations continues to be a topic of great interest among evolutionary theorists. In this work, simulated evolving diploid populations illustrate how mild underdominance (heterozygote disadvantage) can be easily introduced at multiple loci in interbreeding populations through simultaneous or sequential mutational events at individual loci, by means of directional selection and simple forms of epistasis (non-linear gene-gene interactions). It is then shown how multiscale interactions (within-locus, between-locus, and between-individual) can cause interbreeding populations with multiple underdominant loci to self-organize into clusters of compatible genotypes, in some circumstances resulting in the emergence of reproductively isolated species. If external barriers to gene flow are also present, these can have a stabilizing effect on cluster boundaries and help to maintain underdominant polymorphisms, even when homozygotes have differential fitness. It is concluded that multiscale interactions can potentially help to maintain underdominant polymorphisms and may contribute to speciation events.

Margaret J. Eppstein

Joshua L. Payne

Charles J. Goodnight

Underdominance, Multiscale Interactions, and Self-Organizing Barriers to Gene Flow

Discovering the models explaining the hidden relationship between genetic material and tumor pathologies is one of the most important open challenges in biology and medicine. Given the large amount of data made available by the DNA Microarray technique, Machine Learning is becoming a popular tool for this kind of investigations. In the last few years, we have been particularly involved in the study of Genetic Programming for mining large sets of biomedical data. In this paper, we present a comparison between four variants of Genetic Programming for the classification of two different oncologic datasets: the first one contains data from healthy colon tissues and colon tissues affected by cancer; the second one contains data from patients affected by two kinds of leukemia (acute myeloid leukemia and acute lymphoblastic leukemia). We report experimental results obtained using two different fitness criteria: the receiver operating characteristic and the percentage of correctly classified instances. These results, and their comparison with the ones obtained by three nonevolutionary Machine Learning methods (Support Vector Machines, MultiBoosting, and Random Forests) on the same data, seem to hint that Genetic Programming is a promising technique for this kind of classification.

Leonardo Vanneschi

Francesco Archetti

Mauro Castelli

Ilaria Giordani

Classification of Oncologic Data with Genetic Programming

This paper presents Kuri, a software package developed to simulate the temporal and spatial dynamics of genetic variability in populations and multispecies communities of trees, as well as their interactions with environmental factors. A conceptual model using agents inspired on Echo models is used to define the environment, the hierarchical structures, and the low-level rules of the system. At the individual agent (tree) level a genetic algorithm is used to model the genotypic structure and the genetic processes, from a small set of simple rules, complex higher-order population, and environmental interactions emerge. The program was written in Delphi for the Windows environment, and was designed to be used for educational and research purposes.

Benn R. Alle

Lupe Furtado-Alle

Cedric Gondro

Jo&#227;o Carlos M. Magalh&#227;es

Kuri: A Simulator of Ecological Genetics for Tree Populations

The classification of cancers from gene expression profiles is a challenging research area in bioinformatics since the high dimensionality of microarray data results in irrelevant and redundant information that affects the performance of classification. This paper proposes using an evolutionary algorithm to select relevant gene subsets in order to further use them for the classification task. This is achieved by combining valuable results from different feature ranking methods into feature pools whose dimensionality is reduced by a wrapper approach involving  a genetic algorithm and SVM classifier. Specifically, the GA explores the space defined by each feature pool looking for solutions that balance the size of the feature subsets and their classification accuracy. Experiments demonstrate that the proposed method provide good results in comparison to different state of art methods for the classification of microarray data.

Nicoletta Dess&#236;

Barbara Pes

An Evolutionary Method for Combining Different Feature Selection Criteria in Microarray Data Classification

In biomedical signal analysis, artificial neural networks are often used for pattern classification because of their capability for nonlinear class separation and the possibility to efficiently implement them on a microcontroller.
Typically, the network topology is designed by hand, and a gradient-based search algorithm is used to find a set of
suitable parameters for the given classification task. In many cases, however, the choice of the network architecture
is a critical and difficult task. For example, hand-designed networks often require more computational resources than
necessary because they rely on input features that provide no information or are redundant. In the case of mobile
applications, where computational resources and energy are limited, this is especially detrimental. Neuroevolutionary
methods which allow for the automatic synthesis of network topology and parameters offer a solution to these
problems. In this paper, we use analog genetic encoding (AGE) for the evolutionary synthesis of a neural classifier
for a mobile sleep/wake discrimination system. The comparison with a hand-designed classifier trained with back propagation
shows that the evolved neural classifiers display similar performance to the hand-designed networks, but
using a greatly reduced set of inputs, thus reducing computation time and improving the energy efficiency of the
mobile system.

Peter D&#252;rr

Walter Karlen

J&#233;r&#233;mie Guignard

Claudio Mattiussi

Dario Floreano

Evolutionary Selection of Features for Neural Sleep/Wake Discrimination

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Published Special Issues

Journal of Artificial Evolution and Applications

Artificial Evolution Methods in the Biological and Biomedical Sciences

Artificial Evolution Methods in the Biological and Biomedical Sciences

Articles

Artificial Evolution Methods in the Biological and Biomedical Sciences

Multiple Sequence Alignment Using a Genetic Algorithm and GLOCSA

Conserved Self Pattern Recognition Algorithm with Novel Detection Strategy Applied to Breast Cancer Diagnosis

Underdominance, Multiscale Interactions, and Self-Organizing Barriers to Gene Flow

Classification of Oncologic Data with Genetic Programming

Kuri: A Simulator of Ecological Genetics for Tree Populations

An Evolutionary Method for Combining Different Feature Selection Criteria in Microarray Data Classification

Evolutionary Selection of Features for Neural Sleep/Wake Discrimination