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Biblioteca (s) : |
INIA Tacuarembó. |
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Fecha : |
21/02/2014 |
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Actualizado : |
27/06/2019 |
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Tipo de producción científica : |
Documentos |
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Autor : |
METHOL, R.; BALMELLI, G.; RESQUÍN, F. |
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Afiliación : |
RICARDO METHOL, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GUSTAVO DANIEL BALMELLI HERNANDEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JOSE FERNANDO RESQUIN PEREZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Evaluación al tercer año de distintos esquemas de raleo en Eucalyptus grandis. |
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Fecha de publicación : |
2005 |
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Fuente / Imprenta : |
ln: INIA (INSTITUTO NACIONAL DE INVESTIGACIÓN AGROPECUARIA); PROGRAMA NACIONAL PRODUCCIÓN FORESTAL. Jornada Forestal, 2., Tacuarembó, agosto, 2005. Visita a ensayos de silvicultura y mejoramiento de pinos y eucaliptos. Tacuarembó (Uruguay): INIA, 2005. |
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Páginas : |
p. 15-17 |
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Serie : |
(INIA Serie Actividades de Difusión ; 416) |
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Idioma : |
Español |
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Contenido : |
El objetivo de este ensayo es evaluar distintos esquemas de raleo y poblaciones dejadas a turno final en el crecimiento individual (DAP) y total (volumen/ha) de rodales de E. grandis. A su vez, dicha información permitirá determinar los esquemas de raleo de mejor performance económica. |
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Palabras claves : |
FOREST AND FORESTRY. |
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Thesagro : |
CORTA SELECTIVA; EUCALYPTUS GRANDIS; URUGUAY. |
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Asunto categoría : |
K10 Producción forestal |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/12400/1/Sad-416P15-17.pdf
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Marc : |
LEADER 01116naa a2200217 a 4500
001 1025604
005 2019-06-27
008 2005 bl uuuu u00u1 u #d
100 1 $aMETHOL, R.
245 $aEvaluación al tercer año de distintos esquemas de raleo en Eucalyptus grandis.
260 $c2005
300 $ap. 15-17
490 $a(INIA Serie Actividades de Difusión ; 416)
520 $aEl objetivo de este ensayo es evaluar distintos esquemas de raleo y poblaciones dejadas a turno final en el crecimiento individual (DAP) y total (volumen/ha) de rodales de E. grandis. A su vez, dicha información permitirá determinar los esquemas de raleo de mejor performance económica.
650 $aCORTA SELECTIVA
650 $aEUCALYPTUS GRANDIS
650 $aURUGUAY
653 $aFOREST AND FORESTRY
700 1 $aBALMELLI, G.
700 1 $aRESQUÍN, F.
773 $tln: INIA (INSTITUTO NACIONAL DE INVESTIGACIÓN AGROPECUARIA); PROGRAMA NACIONAL PRODUCCIÓN FORESTAL. Jornada Forestal, 2., Tacuarembó, agosto, 2005. Visita a ensayos de silvicultura y mejoramiento de pinos y eucaliptos. Tacuarembó (Uruguay): INIA, 2005.
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Registro original : |
INIA Tacuarembó (TBO) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
17/11/2023 |
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Actualizado : |
17/11/2023 |
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Tipo de producción científica : |
Poster |
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Autor : |
FARIELLO, M.I.; ARBOLEYA, L.; BELZARENA, D.; DE LOS SANTOS, L.; ELENTER, J.; ETCHEBARNE, G.; HOUNIE, I.; CIAPPESONI, G.; NAVAJAS, E.; LECUMBERRY, F. |
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Afiliación : |
MARIA INÉS FARIELLO, Facultad de Ingeniería, Universidad de la República, Uruguay; Institut Pasteur de Montevideo, Uruguay; LUCÍA ARBOLEYA, Facultad de Ingeniería, Universidad de la República, Uruguay; DIEGO BELZARENA, Facultad de Ingeniería, Universidad de la República, Uruguay; LEONARDO DE LOS SANTOS, Facultad de Ingeniería, Universidad de la República, Uruguay; JUAN ELENTER, Facultad de Ingeniería, Universidad de la República, Uruguay; GUILLERMO ETCHEBARNE, Facultad de Ingeniería, Universidad de la República, Uruguay; IGNACIO HOUNIE, Facultad de Ingeniería, Universidad de la República, Uruguay; CARLOS GABRIEL CIAPPESONI SCARONE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ELLY ANA NAVAJAS VALENTINI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FEDERICO LECUMBERRY, Facultad de Ingeniería, Universidad de la República, Uruguay; Institut Pasteur de Montevideo, Uruguay. |
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Título : |
Something old, something new, something borrowed : Evaluation of different neural network architectures for genomic prediction. [poster] |
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Fecha de publicación : |
2023 |
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Fuente / Imprenta : |
En: Plant & Animal Genome Conference : PAG 30, San Diego, California, USA, 13-18 january 2023. |
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Descripción física : |
Editorial: Plant and Animal Genome Conference (PAG). |
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Idioma : |
Inglés |
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Notas : |
Este trabajo fue parcialmente financiado por la Universidad de la República y el proyecto ANII FDA 1_2018_1_154364. -- LICENCIA: Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0). |
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Contenido : |
ABSTRACT.- Genome enabled prediction of complex traits aims to predict a measurable characteristic of an organism using their genetic information. In the present work we address diverse traits and organisms including Yeast growth, Wheat yield, Jersey bull fertility and various Holstein cattle milk-related traits. We benchmark several popular Machine Learning models: Bayesian and penalized linear regressions, kernel methods, and Decision Tree ensembles. Through exhaustive hyperparameter tuning we outperform state-of-the-art results in most datasets. We also evaluate two codification techniques for input data and perform ablation studies to assess robustness to genetic markers - i.e input features- elimination. We also explore different Deep Learning architectures for this task. We propose and evaluate Convolutional Neural Network (CNN) architectures, showing that using residual connections improves performance but that in some cases Fully Connected Networks outperform CNNs. We link this to the fact that absolute positions are relevant in genomes, and thus, CNN's translational equivariance may not be an adequate inductive bias for tackling this problem. We evaluate Graph Neural Network (GNN) architectures by formulating trait prediction as a node regression problem on a population graph, where each node represents an individual, and edges association between their genetic information. We evaluate the transferability of these graphical models and find that the extent to which they exploit neighborhood information is limited. By combining CNN and GNN architectures, we could outperform all other models for predicting milk yield in Holstein cattle.The methods that are based on neural networks can be computationally demanding when used on high density chips or sequence data, even more when fully connected layers are used. To overcome this problem, we propose to obtain a new representation of the input vector by using the intermediate representation (code) of an Autoencoder (AE). Currently we are evaluating the performance benchmarks. Another common issue when using these databases is the missing data or the combination of chips of different SNP's numbers. Again, we propose to use AE for imputing the missing values. One of the main focuses of this work was to explore the feasibility of employing modern deep learning architectures in Genomic Prediction. In this regard, it was possible to train highly over-parameterized architectures and still obtain good generalization. For some datasets and traits, these models outperform all others. However, this did not hold for all the models, traits and datasets studied. Besides, whether the gains in performance outweigh the increase in model size and thus its training and inference computational cost, and lack of interpretability, calls for further discussion. MenosABSTRACT.- Genome enabled prediction of complex traits aims to predict a measurable characteristic of an organism using their genetic information. In the present work we address diverse traits and organisms including Yeast growth, Wheat yield, Jersey bull fertility and various Holstein cattle milk-related traits. We benchmark several popular Machine Learning models: Bayesian and penalized linear regressions, kernel methods, and Decision Tree ensembles. Through exhaustive hyperparameter tuning we outperform state-of-the-art results in most datasets. We also evaluate two codification techniques for input data and perform ablation studies to assess robustness to genetic markers - i.e input features- elimination. We also explore different Deep Learning architectures for this task. We propose and evaluate Convolutional Neural Network (CNN) architectures, showing that using residual connections improves performance but that in some cases Fully Connected Networks outperform CNNs. We link this to the fact that absolute positions are relevant in genomes, and thus, CNN's translational equivariance may not be an adequate inductive bias for tackling this problem. We evaluate Graph Neural Network (GNN) architectures by formulating trait prediction as a node regression problem on a population graph, where each node represents an individual, and edges association between their genetic information. We evaluate the transferability of these graphical models and find that the extent to which t... Presentar Todo |
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Palabras claves : |
Deep learning; Predicción genómica; Signal processing. |
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Asunto categoría : |
L10 Genética y mejoramiento animal |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/17417/1/Fariello-PAG-2023-FABDEEHCNL23.pdf
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Marc : |
LEADER 03979nam a2200277 a 4500
001 1064372
005 2023-11-17
008 2023 bl uuuu u00u1 u #d
100 1 $aFARIELLO, M.I.
245 $aSomething old, something new, something borrowed$bEvaluation of different neural network architectures for genomic prediction. [poster]$h[electronic resource]
260 $aEn: Plant & Animal Genome Conference : PAG 30, San Diego, California, USA, 13-18 january 2023.$c2023
300 $cEditorial: Plant and Animal Genome Conference (PAG).
500 $aEste trabajo fue parcialmente financiado por la Universidad de la República y el proyecto ANII FDA 1_2018_1_154364. -- LICENCIA: Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0).
520 $aABSTRACT.- Genome enabled prediction of complex traits aims to predict a measurable characteristic of an organism using their genetic information. In the present work we address diverse traits and organisms including Yeast growth, Wheat yield, Jersey bull fertility and various Holstein cattle milk-related traits. We benchmark several popular Machine Learning models: Bayesian and penalized linear regressions, kernel methods, and Decision Tree ensembles. Through exhaustive hyperparameter tuning we outperform state-of-the-art results in most datasets. We also evaluate two codification techniques for input data and perform ablation studies to assess robustness to genetic markers - i.e input features- elimination. We also explore different Deep Learning architectures for this task. We propose and evaluate Convolutional Neural Network (CNN) architectures, showing that using residual connections improves performance but that in some cases Fully Connected Networks outperform CNNs. We link this to the fact that absolute positions are relevant in genomes, and thus, CNN's translational equivariance may not be an adequate inductive bias for tackling this problem. We evaluate Graph Neural Network (GNN) architectures by formulating trait prediction as a node regression problem on a population graph, where each node represents an individual, and edges association between their genetic information. We evaluate the transferability of these graphical models and find that the extent to which they exploit neighborhood information is limited. By combining CNN and GNN architectures, we could outperform all other models for predicting milk yield in Holstein cattle.The methods that are based on neural networks can be computationally demanding when used on high density chips or sequence data, even more when fully connected layers are used. To overcome this problem, we propose to obtain a new representation of the input vector by using the intermediate representation (code) of an Autoencoder (AE). Currently we are evaluating the performance benchmarks. Another common issue when using these databases is the missing data or the combination of chips of different SNP's numbers. Again, we propose to use AE for imputing the missing values. One of the main focuses of this work was to explore the feasibility of employing modern deep learning architectures in Genomic Prediction. In this regard, it was possible to train highly over-parameterized architectures and still obtain good generalization. For some datasets and traits, these models outperform all others. However, this did not hold for all the models, traits and datasets studied. Besides, whether the gains in performance outweigh the increase in model size and thus its training and inference computational cost, and lack of interpretability, calls for further discussion.
653 $aDeep learning
653 $aPredicción genómica
653 $aSignal processing
700 1 $aARBOLEYA, L.
700 1 $aBELZARENA, D.
700 1 $aDE LOS SANTOS, L.
700 1 $aELENTER, J.
700 1 $aETCHEBARNE, G.
700 1 $aHOUNIE, I.
700 1 $aCIAPPESONI, G.
700 1 $aNAVAJAS, E.
700 1 $aLECUMBERRY, F.
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