A new approach to the automatic identification of organism evolution using neural networks

Kasperski, Andrzej; Kasperska, Renata

doi:10.1016/j.biosystems.2016.03.005

Cited by 7 publications

(7 citation statements)

References 45 publications

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“…ANN, according to several authors [3][4][5][6][7][8][9][11][12][13][14][15], is a promising tool in analyzing and predicting lots of complex issues that exist in animal studies. As indicated by Fernández et al [10], its advantage over traditional analytical methods is due to its accuracy of estimation as well as its ability to generalize even when less significant data is entered.…”

Section: Discussionmentioning

confidence: 99%

“…Artificial neural networks have found their use in several fields of animal husbandry. The designing of an appropriate model that takes account of measurable and immeasurable features offers possibilities for, amongst others, forecasting the occurrence of lameness in horses [3], assessing animal behaviors while estimating their levels of welfare [9], analyzing the factors impacting on milk yield in cows [4] and goats [10], susceptibility to mastitis in cattle [6,11] as well as the risk of occurrence of complications in parturition [12], predicting the occurrence of African horse sickness [13], in genomic selection in cattle [14], and including research in the field of evolution [15]. Data analysis with the use of artificial neural networks would pave the way for precise classification and adjustments to the expected pattern, clustering, modelling, and forecasting.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting the Utility Value of Hucul Horses by Means of Artificial Intelligence

Topczewska

Kwater

2020

Sustainability

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The paper suggests the application of artificial neural networks (ANN) for the analysis of variables that significantly impact on the results of Hucul horses that participate at the National Breeding and Utility Championships for Hucul horses. The study exploits the results obtained during 2009–2015. The research material collected enabled the creation of a set of input data (for the artificial neural network), out of which independent learning and testing sets were isolated. The neural classification system in form of a multi-layered artificial neural network suggested in this paper was implemented in the programming environment Matlab, the 8.1.0.604 version. Each horse was described using features in three models. Experimental simulations were carried out separately for each model, conducting the learning and testing simulation process 10 times. In accepting the division of the evaluated group of horses into 10 classes for the analysis of the issue both the expert and network designated the classes, not without reservations due to imprecision of demarcations. The increase in class numbers would result in increased accuracy of selection (allocation to varied classes) of individuals. The average for 10 network responses which was 77% suggest an identical or a very similar horse class when compared with the expert’s value. Preliminary results of the application of artificial neural networks in predicting the utility value of Hucul horses, relying on a specific set of features seem promising.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting the Utility Value of Hucul Horses by Means of Artificial Intelligence

Topczewska

Kwater

2020

Sustainability

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“…The amino acid sequences used to teach the three and four-layer neural networks and then to recognize the evolution of organisms were converted to their binary form by changing each character in the sequences to a five-positional binary number (i.e., 'A' has been converted to "00001", 'B' has been converted to "00010", 'C' has been converted to "00011", and so on). This way of conversion gave good results when cytochrome b sequences were used to recognize evolution [11]. Since the number of amino acids in the cytochrome c sequences is about 105, the lengths of each sequence used to teach and then to recognize the evolution of organisms have been aligned to 105.…”

Section: Implementation and Teaching The Artificial Neural Networkmentioning

confidence: 99%

“…This article can be considered as a continuation of previously published articles in which unified cell bioenergetics (UCB) and new attempts to establish methods that can be used to examine the evolution of organisms (including the evolution of transformed cells) have been presented [8,[11][12][13][14][15]. These new methods include the use of artificial neural network (ANN) and a semihomologous approach to recognize evolution.…”

Section: Introductionmentioning

confidence: 99%

“…These new methods include the use of artificial neural network (ANN) and a semihomologous approach to recognize evolution. In the previously published articles, the neural network has been trained using cytochrome b sequences that have been used as phylogenetic probes that represent whole organisms [8,11]. It has been presented that the taught neural network is able to recognize the evolution of unknown (i.e., unseen before) organisms.…”

Section: Introductionmentioning

confidence: 99%

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Genome Attractors as Places of Evolution and Oases of Life

Kasperski

2021

Processes

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So far, much effort has been made to understand evolution and life phenomena. However, the more we know, the more new puzzles appear. This article introduces some new approaches to understanding what drives evolution. Organism evolution has been examined using artificial neural networks and a semihomologous approach based on the sequences of cytochrome c. To realize this task, three and four-layer neural networks have been designed and then taught. It has been shown that the four-layer neural network more clearly recognizes evolutionary similarities, usually indicating greater (comparing to the three-layer network) similarities to the organisms that were used to train the neural networks. It has been noted that unified cell bioenergetics allows describing the manner in which the main engine that drives evolution works. Reasons for some diseases have been also interpreted to present considerations in a broader and more holistic view. The presented results point out that the evolution of organisms can be considered as a discontinuous process taking place mainly in genome attractors that define and stabilize organisms.

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The Digital World of Cytogenetic and Cytogenomic Web Resources

Kasperski,

Heng

2024

Methods in Molecular Biology

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A new approach to the automatic identification of organism evolution using neural networks

Cited by 7 publications

References 45 publications

Forecasting the Utility Value of Hucul Horses by Means of Artificial Intelligence

Forecasting the Utility Value of Hucul Horses by Means of Artificial Intelligence

Genome Attractors as Places of Evolution and Oases of Life

The Digital World of Cytogenetic and Cytogenomic Web Resources

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