Deep learning methods improve genomic prediction of wheat breeding.

In the field of plant breeding, various machine learning models have been developed and studied to evaluate the genomic prediction (GP) accuracy of unseen phenotypes. Deep learning has shown promise. However, most studies on deep learning in plant breeding have been limited to small datasets, and only a few have explored its application in moderate-sized datasets. In this study, we aimed to address this limitation by utilizing a moderately large dataset. We examined the performance of a deep learning (DL) model and compared it with the widely used and powerful best linear unbiased prediction (GBLUP) model. The goal was to assess the GP accuracy in the context of a five-fold cross-validation strategy and when predicting complete environments using the DL model. The results revealed the DL model outperformed the GBLUP model in terms of GP accuracy for two out of the five included traits in the five-fold cross-validation strategy, with similar results in the other traits. This indicates the superiority of the DL model in predicting these specific traits. Furthermore, when predicting complete environments using the leave-one-environment-out (LOEO) approach, the DL model demonstrated competitive performance. It is worth noting that the DL model employed in this study extends a previously proposed multi-modal DL model, which had been primarily applied to image data but with small datasets. By utilizing a moderately large dataset, we were able to evaluate the performance and potential of the DL model in a context with more information and challenging scenario in plant breeding.

Assessing clinical progression from subjective cognitive decline to mild cognitive impairment with incomplete multi-modal neuroimages.

Accurately assessing clinical progression from subjective cognitive decline (SCD) to mild cognitive impairment (MCI) is crucial for early intervention of pathological cognitive decline. Multi-modal neuroimaging data such as T1-weighted magnetic resonance imaging (MRI) and positron emission tomography (PET), help provide objective and supplementary disease biomarkers for computer-aided diagnosis of MCI. However, there are few studies dedicated to SCD progression prediction since subjects usually lack one or more imaging modalities. Besides, one usually has a limited number (e.g., tens) of SCD subjects, negatively affecting model robustness. To this end, we propose a Joint neuroimage Synthesis and Representation Learning (JSRL) framework for SCD conversion prediction using incomplete multi-modal neuroimages. The JSRL contains two components: 1) a generative adversarial network to synthesize missing images and generate multi-modal features, and 2) a classification network to fuse multi-modal features for SCD conversion prediction. The two components are incorporated into a joint learning framework by sharing the same features, encouraging effective fusion of multi-modal features for accurate prediction. A transfer learning strategy is employed in the proposed framework by leveraging model trained on the Alzheimer's Disease Neuroimaging Initiative (ADNI) with MRI and fluorodeoxyglucose PET from 863 subjects to both the Chinese Longitudinal Aging Study (CLAS) with only MRI from 76 SCD subjects and the Australian Imaging, Biomarkers and Lifestyle (AIBL) with MRI from 235 subjects. Experimental results suggest that the proposed JSRL yields superior performance in SCD and MCI conversion prediction and cross-database neuroimage synthesis, compared with several state-of-the-art methods.

Dataset of thermal and visible aerial images for multi-modal and multi-spectral image registration and fusion.

This article presents a dataset of thermal and visible aerial images of the same flat scene at Melendez campus of Universidad del Valle, Cali, Colombia. The images were acquired using an UAV equipped with either a thermal or a visible camera. The dataset is useful for testing techniques for the improvement, registration and fusion of multi-modal and multi-spectral images. The dataset consists of 30 visible images and their metadata, 80 thermal images and their metadata, and a visible georeferenced orthoimage. The metadata related to every image contains the WGS84 coordinates for allocating the images. Also, the homography matrices between every image and the orthoimage are included in the dataset. The images and homographies are compatible with the well-known assessment protocol for detection and description proposed by Mikolajczyk and Schmid [1].

A multi-modal relative spatial access assessment approach to measure spatial accessibility to primary care providers.

Two-step floating catchment area (2SFCA) methods that account for multiple transportation modes provide more realistic accessibility representation than single-mode methods. However, the use of the impedance coefficient in an impedance function (e.g., Gaussian function) introduces uncertainty to 2SFCA results. This paper proposes an enhancement to the multi-modal 2SFCA methods through incorporating the concept of a spatial access ratio (SPAR) for spatial access measurement. SPAR is the ratio of a given place's access score to the mean of all access scores in the study area. An empirical study on spatial access to primary care physicians (PCPs) in the city of Albuquerque, NM, USA was conducted to evaluate the effectiveness of SPAR in addressing uncertainty introduced by the choice of the impedance coefficient in the classic Gaussian impedance function. We used ESRI StreetMap Premium and General Transit Specification Feed (GTFS) data to calculate the travel time to PCPs by car and bus. We first generated two spatial access scores-using different catchment sizes for car and bus, respectively-for each demanding population location: an accessibility score for car drivers and an accessibility score for bus riders. We then computed three corresponding spatial access ratios of the above scores for each population location. Sensitivity analysis results suggest that the spatial access scores vary significantly when using different impedance coefficients (p < 0.05); while SPAR remains stable (p = 1). Results from this paper suggest that a spatial access ratio can significantly reduce impedance coefficient-related uncertainties in multi-modal 2SFCA methods.

Metodología multi-modal en relaciones cuantitativas estructura-actividad / Multi-Modal approach in quantitative structure-activity relationships studies

Los estudios QSAR definidos en la literatura están basados en enfoques uni-modales, dejando de analizar conjuntos de datos que contienen distintas informaciones químicas. En esta investigación se propone aplicar por primera vez y analizar el comportamiento del enfoque multi-modal en el desarrollo de estudios QSAR. Para este fin se utilizó una base de compuestos con actividad hepatotóxica, a partir de la cual se construyeron cuatro modalidades considerando distintos descriptores moleculares basados en diversas teorías y enfoques. Se desarrollaron varios modelos usando los enfoques uni-modales y multi-modales utilizando algoritmos de clasificación reportados en la literatura e implementados en el lenguaje R. Los parámetros de cada uno de los algoritmos se optimizaron con el procedimiento, parameter tuningwithrepeated grid-search cross-validation, mientras la validación de dichos modelos se realizó mediante validación cruzada de 10 pliegues con 10 repeticiones. Estadísticamente se comprobó que el enfoque multimodal mejora el desempeño de los modelos predictivos comparado con algunos de los modelos derivados de los conjuntos de datos con modalidades individuales(AU)

The QSAR studies defined in the literature are based on uni-modal approaches and do not consider datasets with different chemical information. Thus, this research has as objective to apply and analyze the behavior of multi-modal approaches when QSAR studies are carried out. To this end, a compound dataset with hepatotoxicity activity was employed and four modalities were built considering molecular descriptors based on different mathematical theories. Also, several predictive models were developed taking into account both uni-modal and multi-modal approaches by using classification algorithms reported in the literature and implemented in R language. The parameters of these algorithms with the procedure, parameter tuning with repeated grid-search cross-validation, were optimized, while the strategy 10-fold cross-validation with 10 repetitions was used to corroborate the predictive accuracy of the models. As result of this study it can be stated that the behavior of the models based on multi-modal approach present significant differences with to those models developed from uni-modal approaches(AU)

Metodología multi-modal en relaciones cuantitativas estructura-actividad / Multi-Modal approach in quantitative structure-activity relationships studies

Los estudios QSAR definidos en la literatura están basados en enfoques uni-modales, dejando de analizar conjuntos de datos que contienen distintas informaciones químicas. En esta investigación se propone aplicar por primera vez y analizar el comportamiento del enfoque multi-modal en el desarrollo de estudios QSAR. Para este fin se utilizó una base de compuestos con actividad hepatotóxica, a partir de la cual se construyeron cuatro modalidades considerando distintos descriptores moleculares basados en diversas teorías y enfoques. Se desarrollaron varios modelos usando los enfoques uni-modales y multi-modales utilizando algoritmos de clasificación reportados en la literatura e implementados en el lenguaje R. Los parámetros de cada uno de los algoritmos se optimizaron con el procedimiento peatedgrid-searchcross-validation, mientras la validación de dichos modelos se realizó mediante validación cruzada de 10 pliegues con 10 repeticiones. Estadísticamente se comprobó que el enfoque multimodal mejora el desempeño de los modelos predictivos comparado con algunos de los modelos derivados de los conjuntos de datos con modalidades individuales(AU)

The QSAR studies defined in the literature are based on uni-modal approaches and do not consider datasets with different chemical information. Thus, this research has as objective to apply and analyze the behavior of multi-modal approaches when QSAR studies are carried out. To this end, a compound dataset with hepatotoxicity activity was employed and four modalities were built considering molecular descriptors based on different mathematical theories. Also, several predictive models were developed taking into account both uni-modal and multi-modal approaches by using classification algorithms reported in the literature and implemented in R language. The parameters of these algorithms with the procedure parameter tuning with repeated grid-search cross-validation were optimized, while the strategy 10-fold cross-validation with 10 repetitions was used to corroborate the predictive accuracy of the models. As result of this study it can be stated that the behavior of the models based on multi-modal approach present significant differences with to those models developed from uni-modal approaches(AU)