ArticleG3 (Bethesda, Md.)2020
A Multivariate Poisson Deep Learning Model for Genomic Prediction of Count Data.
Article in G3 (Bethesda, Md.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Artificial Intelligence-Assisted Breeding for Plant Disease Resistance.International journal of molecular sciences · 2025Review
- Advances in multi-trait genomic prediction approaches: classification, comparative analysis, and perspectives.Briefings in bioinformatics · 2025Review
- Invited commentary: deep learning-methods to amplify epidemiologic data collection and analyses.American journal of epidemiology · 2025Article
- Application of deep learning with bivariate models for genomic prediction of sow lifetime productivity-related traits.Animal bioscience · 2024Article
- Sparse multi-trait genomic prediction under balanced incomplete block design.The plant genome · 2023Article
- A novel method for genomic-enabled prediction of cultivars in new environments.Frontiers in plant science · 2023Article
- A Review of Integrative Omic Approaches for Understanding Rice Salt Response Mechanisms.Plants (Basel, Switzerland) · 2022Review
- Bayesian multitrait kernel methods improve multienvironment genome-based prediction.G3 (Bethesda, Md.) · 2022Article
- Effects of common full-sib families on accuracy of genomic prediction for tagging weight in striped catfishFrontiers in genetics · 2022Article
- Classification and Regression Models for Genomic Selection of Skewed Phenotypes: A Case for Disease Resistance in Winter Wheat (Frontiers in genetics · 2022Article
- Application of a Poisson deep neural network model for the prediction of count data in genome-based prediction.The plant genome · 2021Article
- New neural network classification method for individuals ancestry prediction from SNPs data.BioData mining · 2021Article
- A zero altered Poisson random forest model for genomic-enabled prediction.G3 (Bethesda, Md.) · 2021Article
- Prediction of count phenotypes using high-resolution images and genomic data.G3 (Bethesda, Md.) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The paradigm called genomic selection (GS) is a revolutionary way of developing new plants and animals. This is a predictive methodology, since it uses learning methods to perform its task. Unfortunately, there is no universal model that can be used for all types of predictions; for this reason, specific methodologies are required for each type of output (response variables). Since there is a lack of efficient methodologies for multivariate count data outcomes, in this paper, a multivariate Poisson deep neural network (MPDN) model is proposed for the genomic prediction of various count outcomes simultaneously. The MPDN model uses the minus log-likelihood of a Poisson distribution as a loss function, in hidden layers for capturing nonlinear patterns using the rectified linear unit (RELU) activation function and, in the output layer, the exponential activation function was used for producing outputs on the same scale of counts. The proposed MPDN model was compared to conventional generalized Poisson regression models and univariate Poisson deep learning models in two experimental data sets of count data. We found that the proposed MPDL outperformed univariate Poisson deep neural network models, but did not outperform, in terms of prediction, the univariate generalized Poisson regression models. All deep learning models were implemented in Tensorflow as back-end and Keras as front-end, which allows implementing these models on moderate and large data sets, which is a significant advantage over previous GS models for multivariate count data.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.