Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics

Xu Wang Sandesh Kumar Shrestha Philomin Juliana Suchismita Mondal Francisco Pinto Govindan Velu Leonardo Abdiel Crespo Herrera JULIO HUERTA_ESPINO Ravi Singh Jesse Poland (2023, [Artículo])

New Crop Varieties Plant Breeding Programs Yield Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA LEARNING GRAIN YIELDS WHEAT BREEDING FOOD SECURITY

Multi-environment genomic prediction of plant traits using deep learners with dense architecture

Osval Antonio Montesinos-Lopez Jose Crossa (2018, [Artículo])

Shared Data Resources Deep Learning Genomic Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA ACCURACY GENOMICS NEURAL NETWORKS FORECASTING DATA MARKER-ASSISTED SELECTION

High-throughput plot-level quantitative phenotyping using convolutional neural networks on very high-resolution satellite images

Francisco Pinto Matthew Paul Reynolds Robert Furbank (2024, [Artículo])

Deep Learning Object-Based Image Analysis Optical Imagery CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA AGRICULTURE IMAGE ANALYSIS PLANT BREEDING REMOTE SENSING MACHINE LEARNING

Multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits

Osval Antonio Montesinos-Lopez Jose Crossa Francisco Javier Martin Vallejo (2018, [Artículo])

Deep Learning Genomic Prediction Bayesian Modeling Shared Data Resources CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA BAYESIAN THEORY RESOURCES DATA BREEDING PROGRAMMES

Statistical machine-learning methods for genomic prediction using the SKM library

Osval Antonio Montesinos-Lopez Brandon Alejandro Mosqueda González Jose Crossa (2023, [Artículo])

Sparse Kernel Methods R package Statistical Machine Learning Genomic Selection CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MARKER-ASSISTED SELECTION MACHINE LEARNING GENOMICS METHODS

Big data, small explanatory and predictive power: Lessons from random forest modeling of on-farm yield variability and implications for data-driven agronomy

Martin van Ittersum (2023, [Artículo])

Context: Collection and analysis of large volumes of on-farm production data are widely seen as key to understanding yield variability among farmers and improving resource-use efficiency. Objective: The aim of this study was to assess the performance of statistical and machine learning methods to explain and predict crop yield across thousands of farmers’ fields in contrasting farming systems worldwide. Methods: A large database of 10,940 field-year combinations from three countries in different stages of agricultural intensification was analyzed. Random effects models were used to partition crop yield variability and random forest models were used to explain and predict crop yield within a cross-validation scheme with data re-sampling over space and time. Results: Yield variability in relative terms was smallest for wheat and barley in the Netherlands and for wheat in Ethiopia, intermediate for rice in the Philippines, and greatest for maize in Ethiopia. Random forest models comprising a total of 87 variables explained a maximum of 65 % of cereal yield variability in the Netherlands and less than 45 % of cereal yield variability in Ethiopia and in the Philippines. Crop management related variables were important to explain and predict cereal yields in Ethiopia, while predictive (i.e., known before the growing season) climatic variables and explanatory (i.e., known during or after the growing season) climatic variables were most important to explain and predict cereal yield variability in the Philippines and in the Netherlands, respectively. Finally, model cross-validation for regions or years not seen during model training reduced the R2 considerably for most crop x country combinations, while for wheat in the Netherlands this was model dependent. Conclusion: Big data from farmers’ fields is useful to explain on-farm yield variability to some extent, but not to predict it across time and space. Significance: The results call for moderate expectations towards big data and machine learning in agronomic studies, particularly for smallholder farms in the tropics where model performance was poorest independently of the variables considered and the cross-validation scheme used.

Model Accuracy Model Precision Linear Mixed Models CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MACHINE LEARNING SUSTAINABLE INTENSIFICATION BIG DATA YIELDS MODELS AGRONOMY

Multimodal deep learning methods enhance genomic prediction of wheat breeding

Carolina Rivera-Amado Francisco Pinto Francisco Javier Pinera-Chavez David González-Diéguez Matthew Paul Reynolds Paulino Pérez-Rodríguez Huihui Li Osval Antonio Montesinos-Lopez Jose Crossa (2023, [Artículo])

Conventional Methods Genomic Prediction Accuracy Deep Learning Novel Methods CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT BREEDING MACHINE LEARNING METHODS MARKER-ASSISTED SELECTION

A line follower robot implementation using Lego's Mindstorms Kit and Q-Learning

VICTOR RICARDO CRUZ ALVAREZ ENRIQUE HIDALGO PEÑA HECTOR GABRIEL ACOSTA MESA (2012, [Artículo])

Un problema común al trabajar con robots móviles es que la fase de programación puede ser un proceso largo, costoso y difícil para los programadores. Los Algoritmos de Aprendizaje por Refuerzo ofrecen uno de los marcos de trabajo más generales en el ámbito de aprendizaje de máquina. Este trabajo presenta un enfoque usando el algoritmo de Q-Learning en un robot Lego para que aprenda "por sí mismo" a seguir una línea negra dibujada en una superficie blanca. El entorno de programación utilizado en este trabajo es Matlab.

INGENIERÍA Y TECNOLOGÍA Algoritmos de aprendizaje reforzado Q-learning (Algoritmo de aprendizaje reforzado) Lego Mindstorms (Robótica) Matlab Reinforcement learning algorithms Q-Learning (Reinforcement learning algorithm) Lego Mindstorms (Robotics) Matlab

Predicción de actividad antimicrobiana usando modelos de escala evolutiva a través de un flujo de trabajo en la plataforma KNIME

Prediction of antimicrobial activity using models of evolutionary scale through a workflow in the KNIME platform

Karla Lorena MartÍnez Mauricio (2023, [Tesis de maestría])

Dentro de las estrategias para combatir la resistencia antimicrobiana, se está llevando a cabo investigación para la creación de nuevos fármacos basados en péptidos antimicrobianos. En los últimos años, se han realizado esfuerzos para incorporar herramientas computacionales que ayuden a acelerar la identificación de péptidos con actividad antimicrobiana. Una de estas herramientas son los modelos QSAR basados en aprendizaje tradicional, que permiten predecir la actividad antimicrobiana en péptidos a partir de información basada en su secuencia. Un componente clave en este proceso es el tipo de características moleculares a utilizar. Recientemente, ha surgido una familia de modelos pre-entrenados llamados ESM-2, los cuales generan incrustaciones (características) que fueron aprendidas a partir de 65 millones de secuencias que abarcan diversidad evolutiva. En este trabajo de tesis, se analiza la contribución de las incrustaciones ESM-2 de diferentes dimensiones de forma individual y en conjunto en el desarrollo de modelos QSAR basados en aprendizaje tradicional para la clasificación de péptidos antimicrobianos, así como sus tipos funcionales, como antibacteriano, antifúngico y antiviral. A partir de este estudio se concluye que aumentar la capacidad de los modelos ESM-2 no implica una mejora en el rendimiento de los modelos para predecir péptidos antimicrobianos. Los modelos ESM-2 t30 y ESM-2 t33 son los más apropiados para extraer características y mejorar la exactitud en las predicciones de péptidos antimicrobianos. Además, fusionar características de diferentes incrustaciones ESM-2 es una estrategia efectiva para construir mejores modelos QSAR que el uso exclusivo de características derivadas de un modelo ESM-2 específico. Se construyeron modelos más simples con un rendimiento comparable o superior a los modelos basados en aprendizaje profundo reportados en la literatura. Para llevar a cabo este estudio se implementó un flujo de trabajo en KNIME que genera de forma automática hasta 1980 modelos de clasificación binaria basados en aprendizaje tradicional. Incorpora diversas técnicas de selección de características, algoritmos de clasificación, métricas de desempeño y una fase de limpieza de datos. Este flujo de trabajo se encuentra disponible en https://github.com/cicese-biocom/classification-QSAR-bioKom.

Molecular features play an important role in different bio-chem-informatics tasks, such as the Quantitative Structure-Activity Relationships (QSAR) modeling. Several pre-trained models have been recently created to be used in downstream tasks either by fine-tuning a specific model or by extracting features to feed traditional classifiers. In this sense, a new family of Evolutionary Scale Modeling models (termed as ESM-2 models) has been recently introduced, demonstrating outstanding results in structure protein prediction benchmarks. Herein, we are devoted to assessing the usefulness of different-dimensional embeddings derived from ESM-2 models in the prediction of antimicrobial peptides, given the great deal of attention received because of their potential to become a plausible option to mainly fight multi-drug resistant bacteria. To this end, we created a KNIME workflow to guarantee using the same modeling methodology, and consequently, carrying out fair comparisons. As a result, it can be drawn that the 640- and 1,280- dimensional embeddings are the most appropriate to be used in modeling because statistically better results were achieved from them. We also combined features from different embeddings, and we can draw that the fusion of features of different embeddings contributes to getting better models than only using a specific model ESM-2. Comparisons regarding state-of-the-art deep learning models confirm that when performing methodologically principled studies in the prediction of AMPs, non-DL based models yield comparable-to-superior results to DL-based models. The implemented KNIME workflow is availablefreely at https://github.com/cicese-biocom/classification-QSAR-bioKom. We consider that this workflow can be valuable to prevent unfair comparisons regarding new computational methods, as well as to propose new non-DL based models.

péptidos antimicrobianos, QSAR, aprendizaje automático ESM-2, KNIME antimicrobial peptides, QSAR, machine learning, ESM-2, KNIME INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS TECNOLOGÍA DE LOS ORDENADORES DISEÑO CON AYUDA DE ORDENADOR DISEÑO CON AYUDA DE ORDENADOR

Distance learning for farmers: Experience during the pandemic

Andrea Gardeazabal (2023, [Documento de trabajo])

In response to the COVID-19 pandemic's disruption of farmer training—a crucial component for enhancing the resilience and livelihoods of smallholder farmers—CIMMYT innovated educational solutions to sustain capacity building in agri-food systems. Addressing the challenges of limited mobile device access, poor internet connectivity, and digital illiteracy, CIMMYT implemented two pilot projects in Mexico. These projects facilitated distance learning for adult farmers in rural areas, employing both internet-based and non-internet methods. The non-internet approach utilized traditional media like print, while the internet-based approach leveraged WhatsApp for educational content delivery. Building on these experiences, CIMMYT expanded its offerings by creating micro -courses delivered through WhatsApp, hosted on the Co-LAB's new Learning Network platform, specifically targeting farmers. This paper delves into the various strategies, methods, and techniques adopted, documenting the learning outcomes, results, and key conclusions drawn from these innovative training initiatives.

Distance Learning Digital Inclusion Innovative Training CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA DISTANCE EDUCATION CAPACITY DEVELOPMENT METHODS COMMUNICATION TECHNOLOGY