scholarly journals Integrating genotype and weather variables for soybean yield prediction using deep learning

2018 ◽  
Author(s):  
Johnathon Shook ◽  
Linjiang Wu ◽  
Tryambak Gangopadhyay ◽  
Baskar Ganapathysubramanian ◽  
Soumik Sarkar ◽  
...  
Keyword(s):  
Complex Traits ◽  
Short Term Memory ◽  
Performance Testing ◽  
Learning Framework ◽  
Weather Events ◽  
Efficient Performance ◽  
Glycine Max L ◽  
Superior Genotypes ◽  
Genetic And Environmental Factors ◽  
Genotype Response

AbstractRealized performance of complex traits is dependent on both genetic and environmental factors, which can be difficult to dissect due to the requirement for multiple replications of many genotypes in diverse environmental conditions. To mediate these problems, we present a machine learning framework in soybean (Glycine max (L.) Merr.) to analyze historical performance records from Uniform Soybean Tests (UST) in North America, with an aim to dissect and predict genotype response in multiple envrionments leveraging pedigree and genomic relatedness measures along with weekly weather parameters. The ML framework of Long Short Term Memory - Recurrent Neural Networks works by isolating key weather events and genetic interactions which affect yield, seed oil, seed protein and maturity enabling prediction of genotypic responses in unseen environments. This approach presents an exciting avenue for genotype x environment studies and enables prediction based systems. Our approaches can be applied in plant breeding programs with multi-environment and multi-genotype data, to identify superior genotypes through selection for commercial release as well as for determining ideal locations for efficient performance testing.

scholarly journals Towards an Efficient Performance Testing Through Dynamic Workload Adaptation

2019 ◽  
pp. 215-233
Author(s):  
Osvaldo Huerta-Guevara ◽  
Vanessa Ayala-Rivera ◽  
Liam Murphy ◽  
A. Omar Portillo-Dominguez
Keyword(s):  
Performance Testing ◽  
Efficient Performance

Soya Bean (Glycine max L. Merr.) Genotype Response to Early-season Flooding: I. Root and Nodule Development

2007 ◽  
Vol 193 (3) ◽  
pp. 177-188 ◽  
Author(s):  
T. L. Henshaw ◽  
R. A. Gilbert ◽  
J. M. S. Scholberg ◽  
T. R. Sinclair
Keyword(s):  
Glycine Max ◽  
Soya Bean ◽  
Nodule Development ◽  
Early Season ◽  
Glycine Max L ◽  
Genotype Response

Biometrical modelling in genetics: are complex traits too complex?

2008 ◽  
Vol 17 (1) ◽  
pp. 75-96 ◽  
Author(s):  
Håkon K. Gjessing ◽  
Rolv Terje Lie
Keyword(s):  
Complex Traits ◽  
Nuclear Family ◽  
Population Based ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Birth Registry ◽  
Environmental Correlations ◽  
Biological Trait ◽  
Medical Birth Registry ◽  
Genetic And Environmental Factors

The field of traditional biometrical genetics uses mixed-effects models to quantify the influence of genetic and environmental factors on a biological trait, based essentially on estimating within-family trait correlations. Such analyses provide a useful preview of what may be discovered with the emerging full-scale genotyping strategies. However, biometrical analyses require unrealistically large sample sizes to obtain a reasonable precision, particularly for dichotomous traits. In addition, it may be very difficult to separate genetic and environmental effects because environmental correlations are poorly understood. We illustrate these and other difficulties using population-based cousins and nuclear family data for birth weight, collected from the Medical Birth Registry of Norway.

scholarly journals Descriptive Answer Script Grading System using CNN-BiLSTM Network

2021 ◽  
Vol 9 (5) ◽  
pp. 139-144
Author(s):  
Shirien K A ◽  
Neethu George ◽  
Surekha Mariam Varghese
Keyword(s):  
Short Term Memory ◽  
Student Evaluation ◽  
Semantic Representation ◽  
Similarity Score ◽  
Test Dataset ◽  
Learning Framework ◽  
Proposed Model ◽  
Public Dataset ◽  
Long Short Term Memory

Descriptive answer script assessment and rating program is an automated framework to evaluate the answer scripts correctly. There are several classification schemes in which a piece of text is evaluated on the basis of spelling, semantics and meaning. But, lots of these aren’t successful. Some of the models available to rate the response scripts include Simple Long Short Term Memory (LSTM), Deep LSTM. In addition to that Convolution Neural Network and Bi-directional LSTM is considered here to refine the result. The model uses convolutional neural networks and bidirectional LSTM networks to learn local information of words and capture long-term dependency information of contexts on the Tensorflow and Keras deep learning framework. The embedding semantic representation of texts can be used for computing semantic similarities between pieces of texts and to grade them based on the similarity score. The experiment used methods for data optimization, such as data normalization and dropout, and tested the model on an Automated Student Evaluation Short Response Scoring, a commonly used public dataset. By comparing with the existing systems, the proposed model has achieved the state-of-the-art performance and achieves better results in the accuracy of the test dataset.

scholarly journals Automated Individual Cattle Identification Using Video Data: A Unified Deep Learning Architecture Approach

2021 ◽  
Vol 2 ◽  
Author(s):  
Yongliang Qiao ◽  
Cameron Clark ◽  
Sabrina Lomax ◽  
He Kong ◽  
Daobilige Su ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Attention Mechanism ◽  
Video Data ◽  
Identification Accuracy ◽  
Sequence Length ◽  
Livestock Farming ◽  
Precision Livestock Farming ◽  
Learning Framework ◽  
Spatio Temporal

Individual cattle identification is a prerequisite and foundation for precision livestock farming. Existing methods for cattle identification require radio frequency or visual ear tags, all of which are prone to loss or damage. Here, we propose and implement a new unified deep learning approach to cattle identification using video analysis. The proposed deep learning framework is composed of a Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) with a self-attention mechanism. More specifically, the Inception-V3 CNN was used to extract features from a cattle video dataset taken in a feedlot with rear-view. Extracted features were then fed to a BiLSTM layer to capture spatio-temporal information. Then, self-attention was employed to provide a different focus on the features captured by BiLSTM for the final step of cattle identification. We used a total of 363 rear-view videos from 50 cattle at three different times with an interval of 1 month between data collection periods. The proposed method achieved 93.3% identification accuracy using a 30-frame video length, which outperformed current state-of-the-art methods (Inception-V3, MLP, SimpleRNN, LSTM, and BiLSTM). Furthermore, two different attention schemes, namely, additive and multiplicative attention mechanisms were compared. Our results show that the additive attention mechanism achieved 93.3% accuracy and 91.0% recall, greater than multiplicative attention mechanism with 90.7% accuracy and 87.0% recall. Video length also impacted accuracy, with video sequence length up to 30-frames enhancing identification performance. Overall, our approach can capture key spatio-temporal features to improve cattle identification accuracy, enabling automated cattle identification for precision livestock farming.

scholarly journals Causal relationships between genetically determined metabolites and human intelligence: A Mendelian randomization study

2020 ◽  
Author(s):  
Jian Yang ◽  
Binbin Zhao ◽  
Li Qian ◽  
Fengjie Gao ◽  
Yanjuan Fan ◽  
...  
Keyword(s):  
Complex Traits ◽  
Mendelian Randomization ◽  
Sensitivity Analyses ◽  
Human Intelligence ◽  
Causal Relationships ◽  
Biological Mechanisms ◽  
Inverse Variance ◽  
Weighted Median ◽  
Genetic And Environmental Factors ◽  
Genetically Determined

Abstract Intelligence predicts important life and health outcomes, but the biological mechanisms underlying differences in intelligence are not yet understood. The use of genetically determined metabotypes (GDMs) to understand the role of genetic and environmental factors, and their interactions, in human complex traits has been recently proposed. However, this strategy has not been applied to human intelligence. Here we implemented a two-sample Mendelian randomization (MR) analysis using GDMs to assess the causal relationships between genetically determined metabolites and human intelligence. The standard inverse-variance weighted (IVW) method was used for the primary MR analysis and three additional MR methods (MR-Egger, weighted median, and MR-PRESSO) were used for sensitivity analyses. Using 25 genetic variants as instrumental variables (IVs), our study found that 5-oxoproline was associated with better performance in human intelligence tests (P IVW = 9 · 25×10 -5 ). The causal relationship was robust when sensitivity analyses were applied (P MR-Egger = 0 · 0001, P Weighted median = 6 · 29×10 -6 , P MR-PRESSO = 0 · 0007), and no evidence of horizontal pleiotropy was observed. Similarly, also dihomo-linoleate (20:2n6) and p-acetamidophenylglucuronide showed robust association with intelligence. Our study provides novel insight by integrating genomics and metabolomics to estimate causal effects of genetically determined metabolites on human intelligence, which help to understanding of the biological mechanisms related to human intelligence.

scholarly journals Contracts for Difference: A Reinforcement Learning Approach

2020 ◽  
Vol 13 (4) ◽  
pp. 78
Author(s):  
Nico Zengeler ◽  
Uwe Handmann
Keyword(s):  
Reinforcement Learning ◽  
Short Term Memory ◽  
Learning Agents ◽  
Learning Framework ◽  
Learning Agent ◽  
Markov Decision ◽  
Economic Trends ◽  
Model Size ◽  
Contracts For Difference ◽  
Partially Observable

We present a deep reinforcement learning framework for an automatic trading of contracts for difference (CfD) on indices at a high frequency. Our contribution proves that reinforcement learning agents with recurrent long short-term memory (LSTM) networks can learn from recent market history and outperform the market. Usually, these approaches depend on a low latency. In a real-world example, we show that an increased model size may compensate for a higher latency. As the noisy nature of economic trends complicates predictions, especially in speculative assets, our approach does not predict courses but instead uses a reinforcement learning agent to learn an overall lucrative trading policy. Therefore, we simulate a virtual market environment, based on historical trading data. Our environment provides a partially observable Markov decision process (POMDP) to reinforcement learners and allows the training of various strategies.

scholarly journals A New Video-Based Crash Detection Method: Balancing Speed and Accuracy Using a Feature Fusion Deep Learning Framework

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Zhenbo Lu ◽  
Wei Zhou ◽  
Shixiang Zhang ◽  
Chen Wang
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Feature Fusion ◽  
Urban Traffic ◽  
Detection Accuracy ◽  
Traffic Crash ◽  
Learning Framework ◽  
Proposed Model ◽  
Speed And Accuracy ◽  
Detection Speed

Quick and accurate crash detection is important for saving lives and improved traffic incident management. In this paper, a feature fusion-based deep learning framework was developed for video-based urban traffic crash detection task, aiming at achieving a balance between detection speed and accuracy with limited computing resource. In this framework, a residual neural network (ResNet) combined with attention modules was proposed to extract crash-related appearance features from urban traffic videos (i.e., a crash appearance feature extractor), which were further fed to a spatiotemporal feature fusion model, Conv-LSTM (Convolutional Long Short-Term Memory), to simultaneously capture appearance (static) and motion (dynamic) crash features. The proposed model was trained by a set of video clips covering 330 crash and 342 noncrash events. In general, the proposed model achieved an accuracy of 87.78% on the testing dataset and an acceptable detection speed (FPS > 30 with GTX 1060). Thanks to the attention module, the proposed model can capture the localized appearance features (e.g., vehicle damage and pedestrian fallen-off) of crashes better than conventional convolutional neural networks. The Conv-LSTM module outperformed conventional LSTM in terms of capturing motion features of crashes, such as the roadway congestion and pedestrians gathering after crashes. Compared to traditional motion-based crash detection model, the proposed model achieved higher detection accuracy. Moreover, it could detect crashes much faster than other feature fusion-based models (e.g., C3D). The results show that the proposed model is a promising video-based urban traffic crash detection algorithm that could be used in practice in the future.

scholarly journals Multiomic blood correlates of genetic risk identify presymptomatic disease alterations

2020 ◽  
Vol 117 (35) ◽  
pp. 21813-21820
Author(s):  
Michael Wainberg ◽  
Andrew T. Magis ◽  
John C. Earls ◽  
Jennifer C. Lovejoy ◽  
Nasa Sinnott-Armstrong ◽  
...  
Keyword(s):  
Biological Networks ◽  
Genetic Risk ◽  
Complex Traits ◽  
Clinical Laboratory ◽  
Clinical Symptoms ◽  
Association Studies ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
High Genetic Risk ◽  
Genetic And Environmental Factors

Transitions from health to disease are characterized by dysregulation of biological networks under the influence of genetic and environmental factors, often over the course of years to decades before clinical symptoms appear. Understanding these dynamics has important implications for preventive medicine. However, progress has been hindered both by the difficulty of identifying individuals who will eventually go on to develop a particular disease and by the inaccessibility of most disease-relevant tissues in living individuals. Here we developed an alternative approach using polygenic risk scores (PRSs) based on genome-wide association studies (GWAS) for 54 diseases and complex traits coupled with multiomic profiling and found that these PRSs were associated with 766 detectable alterations in proteomic, metabolomic, and standard clinical laboratory measurements (clinical labs) from blood plasma across several thousand mostly healthy individuals. We recapitulated a variety of known relationships (e.g., glutamatergic neurotransmission and inflammation with depression, IL-33 with asthma) and found associations directly suggesting therapeutic strategies (e.g., Ω-6 supplementation and IL-13 inhibition for amyotrophic lateral sclerosis) and influences on longevity (leukemia inhibitory factor, ceramides). Analytes altered in high-genetic-risk individuals showed concordant changes in disease cases, supporting the notion that PRS-associated analytes represent presymptomatic disease alterations. Our results provide insights into the molecular pathophysiology of a range of traits and suggest avenues for the prevention of health-to-disease transitions.

scholarly journals On Identifying Hashtags in Disaster Twitter Data

2020 ◽  
Vol 34 (01) ◽  
pp. 498-506 ◽  
Author(s):  
Jishnu Ray Chowdhury ◽  
Cornelia Caragea ◽  
Doina Caragea
Keyword(s):  
Disaster Response ◽  
Short Term Memory ◽  
Automatic Identification ◽  
Short Term ◽  
Term Memory ◽  
Learning Framework ◽  
Twitter Data ◽  
Long Short Term Memory ◽  
Search For Information ◽  
Unique Dataset

Tweet hashtags have the potential to improve the search for information during disaster events. However, there is a large number of disaster-related tweets that do not have any user-provided hashtags. Moreover, only a small number of tweets that contain actionable hashtags are useful for disaster response. To facilitate progress on automatic identification (or extraction) of disaster hashtags for Twitter data, we construct a unique dataset of disaster-related tweets annotated with hashtags useful for filtering actionable information. Using this dataset, we further investigate Long Short-Term Memory-based models within a Multi-Task Learning framework. The best performing model achieves an F1-score as high as $92.22%$. The dataset, code, and other resources are available on Github.1

Sign in / Sign up

Export Citation Format

Share Document