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Author  |
Aracena, J.; Goles, E.; Moreira, A.; Salinas, L. |
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Title |
On the robustness of update schedules in Boolean networks |
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Year |
2009 |
Publication |
Biosystems |
Abbreviated Journal |
Biosystems |
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Volume |
97 |
Issue |
1 |
Pages |
1-8 |
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Keywords |
Boolean network; Update schedule; Robustness; Attractor; Dynamical cycle |
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Abstract |
Deterministic Boolean networks have been used as models of gene regulation and other biological networks. One key element in these models is the update schedule, which indicates the order in which states are to be updated. We study the robustness of the dynamical behavior of a Boolean network with respect to different update schedules (synchronous, block-sequential, sequential), which can provide modelers with a better understanding of the consequences of changes in this aspect of the model. For a given Boolean network, we define equivalence classes of update schedules with the same dynamical behavior, introducing a labeled graph which helps to understand the dependence of the dynamics with respect to the update, and to identify interactions whose timing may be crucial for the presence of a particular attractor of the system. Several other results on the robustness of update schedules and of dynamical cycles with respect to update schedules are presented. Finally, we prove that our equivalence classes generalize those found in sequential dynamical systems. (C) 2009 Elsevier Ireland Ltd. All rights reserved. |
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[Aracena, J.] Univ Concepcion, Dept Ingn Matemat, Concepcion, Chile, Email: jaracena@ing-mat.udec.cl |
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Elsevier Sci Ltd |
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English |
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0303-2647 |
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WOS:000267528900001 |
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Call Number |
UAI @ eduardo.moreno @ |
Serial |
29 |
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Author |
Arevalo-Ramirez, T.; Villacres, J.; Fuentes, A.; Reszka, P.; Cheein, F.A.A. |
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Title |
Moisture content estimation of Pinus radiata and Eucalyptus globulus from reconstructed leaf reflectance in the SWIR region |
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Year |
2020 |
Publication |
Biosystems Engineering |
Abbreviated Journal |
Biosyst. Eng. |
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Volume |
193 |
Issue |
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Pages |
187-205 |
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Keywords |
SWIR reconstruction; Machine learning; Spectral indices; Fuel moisture content; Equivalent water thickness |
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Abstract |
Valparaiso, a central-southern region in Chile, has one of the highest rates of wildfire occurrence in the country. The constant threat of fires is mainly due to its highly flammable forest plantation, composed of 97.5% Pinus radiata and Eucalyptus globulus. Fuel moisture content is one of the most relevant parameters for studying fire spreading and risk, and can be estimated from the reflectance of leaves in the short wave infra-red (SWIR) range, not easily available in most vision-based sensors. Therefore, this work addresses the problem of estimating the water content of leaves from the two previously mentioned species, without any knowledge of their spectrum in the SWIR band. To this end, and for validation purposes, the reflectance of 90 leaves per species, at five dehydration stages, were taken between 350 nm and 2500 nm (full spectrum). Then, two machine-learning regressors were trained with 70% of the data set to determine the unknown reflectance, in the range 1000 nm-2500 nm. Results were validated with the remaining 30% of the data, achieving a root mean square error less than 9% in the spectrum estimation, and an error of 10% in spectral indices related to water content estimation. (C) 2020 IAgrE. Published by Elsevier Ltd. All rights reserved. |
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Address |
[Arevalo-Ramirez, Tito; Villacres, Juan; Auat Cheein, Fernando A.] Univ Tecn Fedrico Santa Maria, Dept Ingn Elect, Valparaiso, Chile, Email: fernando.auat@usm.cl |
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Academic Press Inc Elsevier Science |
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English |
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1537-5110 |
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WOS:000526114500016 |
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UAI @ eduardo.moreno @ |
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1150 |
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Author |
Arevalo-Ramirez, TA.; Castillo, AHF.; Cabello, PSR.; Cheein, FAA. |
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Title |
Single bands leaf reflectance prediction based on fuel moisture content for forestry applications |
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Year |
2021 |
Publication |
Biosystems Engineering |
Abbreviated Journal |
Biosyst. Eng. |
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Volume |
202 |
Issue |
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Pages |
79-95 |
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Keywords |
Leaf water index; Machine learning; Remote sensing; Wildfire; Wildland fuels |
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Abstract |
Vegetation indices can be used to perform quantitative and qualitative assessment of vegetation cover. These indices exploit the reflectance features of leaves to predict their biophysical properties. In general, there are different vegetation indices capable of describing the same biophysical parameter. For instance, vegetation water content can be inferred from at least sixteen vegetation indices, where each one uses the reflectance of leaves in different spectral bands. Therefore, if the leaf moisture content, a vegetation index and the reflectance at the wavelengths to compute the vegetation index are known, then the reflectance in other spectral bands can be computed with a bounded error. The current work proposes a method to predict, by a machine learning regressor, the leaf reflectance (spectral signature) at specific spectral bands using the information of leaf moisture content and a single vegetation index of two tree species (Pinus radiata, and Eucalyptus globulus), which constitute 97.5% of the Valparai ' so forests in Chile. Results suggest that the most suitable vegetation index to predict the spectral signature is the Leaf Water Index, which using a Kernel Ridge Regressor achieved the best prediction results, with an RMSE lower than 0.022, and an average R2 greater than 0.95 for Pinus radiata and 0.81 for Eucalyptus globulus, respectively. (c) 2020 IAgrE. Published by Elsevier Ltd. All rights reserved. |
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1537-5110 |
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WOS:000613400300008 |
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UAI @ alexi.delcanto @ |
Serial |
1346 |
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Author |
Chetty, M.; Hallinan, J.; Ruz, G.A.; Wipat, A. |
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Title |
Computational intelligence and machine learning in bioinformatics and computational biology |
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2022 |
Publication |
Biosystems |
Abbreviated Journal |
Biosystems |
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222 |
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104792 |
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Bioinformatics and computational biology are major beneficiaries of the current innovations in artificial intelligence and machine learning. While Bioinformatics applies principles of computer science and technique to help understand the vast, diverse, and complex life sciences data and thus make it more useful, in contrast, Computational Biology applies computational approaches to address theoretical and experimental questions in biology. This Special Issue on Computational Intelligence and Machine Learning in Bioinformatics and Computational Biology comprises of extended versions of the key papers from the 18th IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB, 2021) which is a major event in the field of computational intelligence and its applications to problems in bioinformatics, computational biology, and biomedical engineering. The conference, run annually, provides a global forum for academic and industrial scientists from computer science, biology, chemistry, medicine, mathematics, statistics, and engineering, to discuss and present their latest research findings from theory to applications. |
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0303-2647 |
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UAI @ alexi.delcanto @ |
Serial |
1689 |
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Author |
Ruz, G.A.; Ashlock, D.; Allmendinger, R.; Fogel, G.B. |
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Editorial: 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB 2020) |
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2022 |
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Biosystems |
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Biosystems |
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218 |
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104698 |
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0303-2647 |
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WOS:000823127700002 |
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UAI @ alexi.delcanto @ |
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1612 |
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Author |
Ruz, G.A.; Goles, E. |
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Title |
Gene regulatory networks with binary weights |
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Year |
2023 |
Publication |
Biosystems |
Abbreviated Journal |
Biosystems |
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Volume |
227 |
Issue |
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Pages |
104902 |
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Abstract |
An evolutionary computation framework to learn binary threshold networks is presented. Inspired by the recent trend of binary neural networks, where weights and activation thresholds are represented using 1 and -1 such that they can be stored in 1-bit instead of full precision, we explore this approach for gene regulatory network modeling. We test our method by inferring binary threshold networks of two regulatory network models: Quorum sensing systems in bacterium Paraburkholderia phytofirmans PsJN and the fission yeast cell-cycle. We considered differential evolution and particle swarm optimization for the simulations. Results for weights having only 1 and -1 values, and different activation thresholds are presented. Full binary threshold networks were found with minimum error (2 bits), whereas when the binary restriction is relaxed for the activation thresholds, networks with 0 bit error were found. |
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Data Observatrory |
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0303-2647 |
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WOS:000986957200001 |
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UAI @ alexi.delcanto @ |
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1783 |
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Author |
Ruz, G.A.; Goles, E.; Montalva, M.; Fogel, G.B. |
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Title |
Dynamical and topological robustness of the mammalian cell cycle network: A reverse engineering approach |
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2014 |
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Biosystems |
Abbreviated Journal |
Biosystems |
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115 |
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23-32 |
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Keywords |
Gene regulatory networks; Boolean networks; Threshold networks; Update robustness; Topology robustness; Bees algorithm |
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Abstract |
A common gene regulatory network model is the threshold Boolean network, used for example to model the Arabidopsis thaliana floral morphogenesis network or the fission yeast cell cycle network. In this paper, we analyze a logical model of the mammalian cell cycle network and its threshold Boolean network equivalent. Firstly, the robustness of the network was explored with respect to update perturbations, in particular, what happened to the attractors for all the deterministic updating schemes. Results on the number of different limit cycles, limit cycle lengths, basin of attraction size, for all the deterministic updating schemes were obtained through mathematical and computational tools. Secondly, we analyzed the topology robustness of the network, by reconstructing synthetic networks that contained exactly the same attractors as the original model by means of a swarm intelligence approach. Our results indicate that networks may not be very robust given the great variety of limit cycles that a network can obtain depending on the updating scheme. In addition, we identified an omnipresent network with interactions that match with the original model as well as the discovery of new interactions. The techniques presented in this paper are general, and can be used to analyze other logical or threshold Boolean network models of gene regulatory networks. (C) 2013 Elsevier Ireland Ltd. All rights reserved. |
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Address |
[Ruz, Gonzalo A.; Goles, Eric; Montalva, Marco] Univ Adolfo Ibanez, Fac Ingn & Ciencias, Santiago, Chile, Email: gonzalo.ruz@uai.cl |
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Elsevier Sci Ltd |
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English |
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0303-2647 |
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WOS:000330500100004 |
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UAI @ eduardo.moreno @ |
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350 |
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