Predicting habitat suitability with machine learning models: The potential area of Pinus sylvestris L. in the Iberian Peninsula

Garzón, Marta Benito; Blažek, Radim; Neteler, Markus; Dios, Rut Sánchez de; Ollero, Helios Sáinz; Furlanello, Cesare

doi:10.1016/j.ecolmodel.2006.03.015

Cited by 163 publications

(86 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Garzón et al [27], Evans and Cushman [25], Cutler et al [23] and Hernandez et al [35] predict the presence of a species from climatic and topographic variables and Peters et al [60] show that RF performs well in the prediction of vegetation types from environmental variables. Perdiguero-Alonso et al…”

Section: Rf Applications In Bioinformatics: Some Examplesmentioning

confidence: 99%

Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics

Boulesteix

Janitza

Kruppa

et al. 2012

WIREs Data Min & Knowl

632

455

View full text Add to dashboard Cite

The Random Forest (RF) algorithm by Leo Breiman has become a standard data analysis tool in bioinformatics. It has shown excellent performance in settings where the number of variables is much larger than the number of observations, can cope with complex interaction structures as well as highly correlated variables and returns measures of variable importance. This paper synthesizes ten years of RF development with emphasis on applications to bioinformatics and computational biology. Special attention is given to practical aspects such as the selection of parameters, available RF implementations, and important pitfalls and biases of RF and its variable importance measures (VIMs). The paper surveys recent developments of the methodology relevant to bioinformatics as well as some representative examples of RF applications in this context and possible directions for future research.

show abstract

Section: Rf Applications In Bioinformatics: Some Examplesmentioning

confidence: 99%

Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics

Boulesteix

Janitza

Kruppa

et al. 2012

WIREs Data Min & Knowl

632

455

View full text Add to dashboard Cite

show abstract

“…It's high predictive power has been supported by previous comparative studies with other machine learning (ML) methods [19][20][21]. The final classification is obtained by combining the classification results from the individual decision trees.…”

Section: Selection Of the Pathways Pairsmentioning

confidence: 81%

Revealing pathway cross-talk related to diabetes mellitus by Monte Carlo Cross-Validation analysis

Cai

Shi

2017

Open Life Sciences

View full text Add to dashboard Cite

Abstract:Objective: To explore potential functional biomarkers in diabetes mellitus (DM) by utilizing gene pathway cross-talk. Methods: Firstly, potential disrupted pathways that were enriched by differentially expressed genes (DEGs) were identified based on biological pathways downloaded from the Ingenuity Pathways Analysis (IPA) database. In addition, we quantified the pathway crosstalk for each pair of pathways based on Discriminating Score (DS). Random forest (RF) classification was then employed to find the top 10 pairs of pathways with a high area under the curve (AUC) value between DM samples versus normal samples based on 10-fold cross-validation. Finally, a Monte Carlo Cross-Validation was applied to demonstrate the identified pairs of pathways by a mutual information analysis. Results: A total of 247 DEGs in normal and disease samples were identified. Based on the F-test, 50 disrupted pathways were obtained with false discovery rate (FDR) < 0.01. Simultaneously, after calculating the DS, the top 10 pairs of pathways were selected based on a higher AUC value as measured by RF classification. From the Monte Carlo Cross-Validation, we considered the top 10 pairs of pathways with higher AUC values ranked for all 50 bootstraps as the most frequently detected ones. Conclusion: The pairs of pathways identified in our study might be key regulators in DM.

show abstract

“…Overall accuracy larger than 90% is commonly found in modeling work using large database from satellite images (GARZÓN et al, 2006;WANG et al, 2016). The high performance of Croton floribundus is related to its concentrate area of occurrence.…”

Section: Algorithms Assessmentmentioning

confidence: 99%

“…The advantages are the ability to work with correlated predictors, nonlinear relationships and noisy data. These characteristics are essential to improve the performance and reduce errors in ecological modeling (GARZÓN et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Modeling Ecological Niche of Tree Species in Brazilian Tropical Area

Carvalho

Gomide

Santos

et al. 2017

CERNE

View full text Add to dashboard Cite

ABSTRACT:Modeling of the ecological niche of vegetal species is useful for understanding the species-environment relationship, for prediction of responses to climate changes and for correct reforestation programs and establishment of plantation's recommendation. The objective of this work was to establish a model for the distribution of four tree species (Casearia sylvestris, Copaifera langsdorffii, Croton floribundus and Tapirira guianensis), widely used in reforestation projects in the state of Minas Gerais, Brazil. In addition, we analyzed the relationship between environmental characteristics and the occurrence of species and tested the performance of Random Forest and Artificial Neural Networks as modeling methods. These methods were evaluated by their overall accuracy, sensitivity, specificity, Kappa, true skill statistic and the area under the receiver operating curve. The results showed the species Casearia sylvestris, Copaifera langsdorffii and Tapirira guianensis widely occurring in the state of Minas Gerais, including a broad range of environmental variables. Croton floribundus had restricted occurrence in the southern state, showing narrow environmental variation. The resulting algorithms demonstrated greater performance when modeling restricted geographic and environmental species, as well as species occurring with high prevalence in data. The algorithm Random Forest performed better for distribution modeling of all species, although the results varied for each metric and species. The maps generated had acceptable metrics and are supported by and ecological information obtained from other sources, constituting a useful tool to understand the ecology and biogeography of the target species. MODELAGEM DO NICHO ECOLÓGICOS DE ESPÉCIES ARBÓREAS EM UMA ÁREA TROPICAL BRASILEIRARESUMO: A modelagem de nicho ecológico de uma espécie é útil para a compreensão da relação espécie-ambiente, para a previsão do comportamento frente às alterações climáticas e para a indicação correta em reflorestamentos e estabelecimento de plantações. O objetivo foi modelar a distribuição de quatro espécies arbóreas amplamente utilizadas em projetos de reflorestamento no estado de Minas Gerais (Casearia sylvestris, Copaifera langsdorffii, Croton floribundus e Tapirira guianensis). Como complemento, o objetivo foi analisar a relação entre as características ambientais e a ocorrência de espécies e testar o desempenho das técnicas random forest e redes neurais artificiais como métodos de modelagem. Estes métodos foram avaliados pelas métricas de acurácia global, sensibilidade, especificidade, kappa, true skill statistic e área sob a curva. Verificou-se que as espécies Casearia sylvestris, Copaifera langsdorffii e Tapirira guianensis apresentaram ampla área de ocorrência no estado Minas Gerais, cobrindo ampla gama de variáveis ambientais. Já Croton floribundus demonstrou ocorrência restrita do sul do estado, mostrando estreita variação ambiental. Os resultados dos algoritmos demonstraram maior desempenho na modelagem de espécies geograf...

show abstract

Predicting habitat suitability with machine learning models: The potential area of Pinus sylvestris L. in the Iberian Peninsula

Cited by 163 publications

References 58 publications

Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics

Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics

Revealing pathway cross-talk related to diabetes mellitus by Monte Carlo Cross-Validation analysis

Modeling Ecological Niche of Tree Species in Brazilian Tropical Area

Contact Info

Product

Resources

About