scholarly journals A Large Scale Joint Analysis of Flowering Time Reveals Independent Temperate Adaptations in Maize

2016 ◽  
Author(s):  
Kelly Swarts ◽  
Eva Bauer ◽  
Jeffrey C. Glaubitz ◽  
Tiffany Ho ◽  
Lynn Johnson ◽  
...  
Keyword(s):  
Machine Learning ◽  
North American ◽  
Large Scale ◽  
Predictive Ability ◽  
Joint Analysis ◽  
Whole Genome ◽  
Learning Approaches ◽  
Ontology Term ◽  
Days To Flowering ◽  
The North

AbstractModulating days to flowering is a key mechanism in plants for adapting to new environments, and variation in days to flowering drives population structure by limiting mating. To elucidate the genetic architecture of flowering across maize, a quantitative trait, we mapped flowering in five global populations, a diversity panel (Ames) and four half-sib mapping designs, Chinese (CNNAM), US (USNAM), and European Dent (EUNAM-Dent) and Flint (EUNAM-Flint). Using whole-genome projected SNPs, we tested for joint association using GWAS, resampling GWAS and two regional approaches; Regional Heritability Mapping (RHM) (1, 2) and a novel method, Boosted Regional Heritability Mapping (BRHM). Direct overlap in significant regions detected between populations and flowering candidate genes was limited, but whole-genome cross-population predictive abilities were ≤0.78. Poor predictive ability correlated with increased population differentiation (r = 0.41), unless the parents were broadly sampled from across the North American temperate-tropical germplasm gradient; uncorrected GWAS results from populations with broadly sampled parents were well predicted by temperate-tropical FSTs in machine learning. Machine learning between GWAS results also suggested shared architecture between the American panels and, more distantly, the European panels, but not the Chinese panel. Machine learning approaches can reconcile non-linear relationships, but the combined predictive ability of all of the populations did not significantly enhance prediction of physiological candidates. While the North American-European temperate adaption is well studied, this study suggest independent temperate adaptation evolved in the Chinese panel, most likely in China after 1500, a finding supported by differential gene ontology term enrichment between populations.

scholarly journals Training machine learning models on climate model output yields skillful interpretable seasonal precipitation forecasts

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Peter B. Gibson ◽  
William E. Chapman ◽  
Alphan Altinok ◽  
Luca Delle Monache ◽  
Michael J. DeFlorio ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Climate Model ◽  
Learning Approaches ◽  
Model Ensemble ◽  
Learning Models ◽  
Seasonal Forecasts ◽  
The North ◽  
Climate Model Simulations ◽  
Machine Learning Models

AbstractA barrier to utilizing machine learning in seasonal forecasting applications is the limited sample size of observational data for model training. To circumvent this issue, here we explore the feasibility of training various machine learning approaches on a large climate model ensemble, providing a long training set with physically consistent model realizations. After training on thousands of seasons of climate model simulations, the machine learning models are tested for producing seasonal forecasts across the historical observational period (1980-2020). For forecasting large-scale spatial patterns of precipitation across the western United States, here we show that these machine learning-based models are capable of competing with or outperforming existing dynamical models from the North American Multi Model Ensemble. We further show that this approach need not be considered a ‘black box’ by utilizing machine learning interpretability methods to identify the relevant physical processes that lead to prediction skill.

Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery

2019 ◽  
Vol 19 (1) ◽  
pp. 4-16 ◽  
Author(s):  
Qihui Wu ◽  
Hanzhong Ke ◽  
Dongli Li ◽  
Qi Wang ◽  
Jiansong Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Large Scale ◽  
Recent Progress ◽  
High Specificity ◽  
Learning Approaches ◽  
Anticancer Peptides ◽  
The Past ◽  
Traditional Approaches ◽  
Large Scale Screening

Over the past decades, peptide as a therapeutic candidate has received increasing attention in drug discovery, especially for antimicrobial peptides (AMPs), anticancer peptides (ACPs) and antiinflammatory peptides (AIPs). It is considered that the peptides can regulate various complex diseases which are previously untouchable. In recent years, the critical problem of antimicrobial resistance drives the pharmaceutical industry to look for new therapeutic agents. Compared to organic small drugs, peptide- based therapy exhibits high specificity and minimal toxicity. Thus, peptides are widely recruited in the design and discovery of new potent drugs. Currently, large-scale screening of peptide activity with traditional approaches is costly, time-consuming and labor-intensive. Hence, in silico methods, mainly machine learning approaches, for their accuracy and effectiveness, have been introduced to predict the peptide activity. In this review, we document the recent progress in machine learning-based prediction of peptides which will be of great benefit to the discovery of potential active AMPs, ACPs and AIPs.

Machine learning identifies an immunological pattern associated with multiple juvenile idiopathic arthritis subtypes

2019 ◽  
Vol 78 (5) ◽  
pp. 617-628 ◽  
Author(s):  
Erika Van Nieuwenhove ◽  
Vasiliki Lagou ◽  
Lien Van Eyck ◽  
James Dooley ◽  
Ulrich Bodenhofer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Juvenile Idiopathic Arthritis ◽  
Large Scale ◽  
Inflammatory Diseases ◽  
Adaptive Immune System ◽  
Healthy Children ◽  
Learning Approaches ◽  
Data Set ◽  
Immune Signature ◽  
Systemic Jia

ObjectivesJuvenile idiopathic arthritis (JIA) is the most common class of childhood rheumatic diseases, with distinct disease subsets that may have diverging pathophysiological origins. Both adaptive and innate immune processes have been proposed as primary drivers, which may account for the observed clinical heterogeneity, but few high-depth studies have been performed.MethodsHere we profiled the adaptive immune system of 85 patients with JIA and 43 age-matched controls with indepth flow cytometry and machine learning approaches.ResultsImmune profiling identified immunological changes in patients with JIA. This immune signature was shared across a broad spectrum of childhood inflammatory diseases. The immune signature was identified in clinically distinct subsets of JIA, but was accentuated in patients with systemic JIA and those patients with active disease. Despite the extensive overlap in the immunological spectrum exhibited by healthy children and patients with JIA, machine learning analysis of the data set proved capable of discriminating patients with JIA from healthy controls with ~90% accuracy.ConclusionsThese results pave the way for large-scale immune phenotyping longitudinal studies of JIA. The ability to discriminate between patients with JIA and healthy individuals provides proof of principle for the use of machine learning to identify immune signatures that are predictive to treatment response group.

IMPROVING THE NORTH AMERICAN MULTI-MODEL ENSEMBLE (NMME) PRECIPITATION FORECASTS AT SEASONAL SCALE OVER THE HIMALAYAN REGION USING MACHINE LEARNING

2021 ◽  
Author(s):  
SOURABH SHRIVASTAVA ◽  
RAM AVTAR ◽  
PRASANTA KUMAR BAL
Keyword(s):  
Machine Learning ◽  
North American ◽  
Climate Models ◽  
Global Climate ◽  
Horizontal Resolution ◽  
Summer Monsoon Rainfall ◽  
Global Climate Models ◽  
Himalayan Region ◽  
Model Ensemble ◽  
The North

The coarse horizontal resolution global climate models (GCMs) have limitations in producing large biases over the mountainous region. Also, single model output or simple multi-model ensemble (SMME) outputs are associated with large biases. While predicting the rainfall extreme events, this study attempts to use an alternative modeling approach by using five different machine learning (ML) algorithms to improve the skill of North American Multi-Model Ensemble (NMME) GCMs during Indian summer monsoon rainfall from 1982 to 2009 by reducing the model biases. Random forest (RF), AdaBoost (Ada), gradient (Grad) boosting, bagging (Bag) and extra (Extra) trees regression models are used and the results from each models are compared against the observations. In simple MME (SMME), a wet bias of 20[Formula: see text]mm/day and an RMSE up to 15[Formula: see text]mm/day are found over the Himalayan region. However, all the ML models can bring down the mean bias up to [Formula: see text][Formula: see text]mm/day and RMSE up to 2[Formula: see text]mm/day. The interannual variability in ML outputs is closer to observation than the SMME. Also, a high correlation from 0.5 to 0.8 is found between in all ML models and then in SMME. Moreover, representation of RF and Grad is found to be best out of all five ML models that represent a high correlation over the Himalayan region. In conclusion, by taking full advantage of different models, the proposed ML-based multi-model ensemble method is shown to be accurate and effective.

scholarly journals Impacts of Large-Scale Circulation on Urban Ambient Concentrations of Gaseous Elemental Mercury in New York, USA

2017 ◽  
Author(s):  
Huiting Mao ◽  
Dolly Hall ◽  
Zhuyun Ye ◽  
Ying Zhou ◽  
Dirk Felton ◽  
...  
Keyword(s):  
New York ◽  
North American ◽  
Large Scale ◽  
Measurement Data ◽  
Elemental Mercury ◽  
Urban Site ◽  
Large Scale Circulation ◽  
Gaseous Elemental Mercury ◽  
The North ◽  
The Impact

Abstract. The impact of large-scale circulation on urban gaseous elemental mercury (GEM) was investigated through analysis of 2008–2015 measurement data from an urban site in New York City (NYC), New York, USA. Distinct annual cycles were observed in 2009–2010 with mixing ratios in warm seasons (i.e. spring–summer) 10–20 ppqv (~ 10 %–25 %) higher than in cool seasons (i.e. fall–winter). This annual cycle was disrupted in 2011 by an anomalously strong influence of the North American trough in that warm season and was reproduced in 2014 with annual amplitude enhanced up to ~ 70 ppqv associated with a particularly strong Bermuda High. North American trough axis index (TAI) and intensity index (TII) were used to characterize the effect of the North American trough on NYC GEM especially in winter and summer. The intensity and position of the Bermuda High had a significant impact on GEM in warm seasons supported by a strong correlation (r reaching 0.96, p 

scholarly journals Typology and nuance: relativization

2020 ◽  
pp. 104-130
Author(s):  
Marianne Mithun
Keyword(s):  
North American ◽  
Large Scale ◽  
Relative Clauses ◽  
Spontaneous Speech ◽  
The Other ◽  
Stages Of Development ◽  
Linguistic Typology ◽  
The North

Much of linguistic typology is inherently categorical. In large-scale typological surveys, grammatical constructions, distinctions, and even variables are typically classified as present, absent, or embodying one of a set of specified options. This work is valuable for a multitude of purposes, and in many cases such categorization is sufficient. In others, we can advance our understanding further if we take a more nuanced approach, considering the extent to which a particular construction, distinction, or variable is installed in the grammar. An important tool for this approach is the examination of unscripted speech in context, complete with prosody. This point is illustrated here with Mohawk, an Iroquoian language indigenous to the North American Northeast. As will be seen, the two types of construction which might be identified as relative clauses are emergent, one less integrated into the grammar than the other. Examination of spontaneous speech indicates that the earliest stages of development are prosodic, as speakers shape their messages according to their communicative purposes at each moment.

Machine Learning Based Taxonomy and Analysis of English Learners' Translation Errors

2019 ◽  
Vol 9 (3) ◽  
pp. 68-83
Author(s):  
Ying Qin
Keyword(s):  
Machine Learning ◽  
English Learners ◽  
Large Scale ◽  
Learning Approaches ◽  
Efl Learners ◽  
Translation Error ◽  
Chinese Learners ◽  
Error Taxonomy ◽  
Skill Improvement ◽  
Translation Errors

This study extracts the comments from a large scale of Chinese EFL learners' translation corpus to study the taxonomy of translation errors. Two unsupervised machine learning approaches are used to obtain the computational evidences of translation error taxonomy. After manually revision, ten types of English to Chinese (E2C) and eight types Chinese to English (C2E) translation errors are finally confirmed. There probably exists three categories of top-level errors according to the hierarchical clustering results. In addition, three supervised learning methods are applied to automatically recognize the types of errors, among which the highest performance reaches F1 = 0.85 on E2C and F1 = 0.90 on C2E translation. Further comparison to the intuitive or theoretical studies on translation taxonomy shows some phenomenon accompanied by language skill improvement of Chinese learners. Analysis on translation problems based on machine learning provides the objective insight and understanding on the students' translations.

scholarly journals A Machine Learning View on Momentum and Reversal Trading

Algorithms ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 170 ◽  
Author(s):  
Zhixi Li ◽  
Vincent Tam
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Stock Market ◽  
Short Term Memory ◽  
Predictive Ability ◽  
Trading Strategies ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Approaches ◽  
Learning Techniques

Momentum and reversal effects are important phenomena in stock markets. In academia, relevant studies have been conducted for years. Researchers have attempted to analyze these phenomena using statistical methods and to give some plausible explanations. However, those explanations are sometimes unconvincing. Furthermore, it is very difficult to transfer the findings of these studies to real-world investment trading strategies due to the lack of predictive ability. This paper represents the first attempt to adopt machine learning techniques for investigating the momentum and reversal effects occurring in any stock market. In the study, various machine learning techniques, including the Decision Tree (DT), Support Vector Machine (SVM), Multilayer Perceptron Neural Network (MLP), and Long Short-Term Memory Neural Network (LSTM) were explored and compared carefully. Several models built on these machine learning approaches were used to predict the momentum or reversal effect on the stock market of mainland China, thus allowing investors to build corresponding trading strategies. The experimental results demonstrated that these machine learning approaches, especially the SVM, are beneficial for capturing the relevant momentum and reversal effects, and possibly building profitable trading strategies. Moreover, we propose the corresponding trading strategies in terms of market states to acquire the best investment returns.

Improving the North American multi-model ensemble (NMME) precipitation forecasts at local areas using wavelet and machine learning

2019 ◽  
Vol 53 (1-2) ◽  
pp. 601-615 ◽  
Author(s):  
Lei Xu ◽  
Nengcheng Chen ◽  
Xiang Zhang ◽  
Zeqiang Chen ◽  
Chuli Hu ◽  
...  
Keyword(s):  
Machine Learning ◽  
North American ◽  
Model Ensemble ◽  
The North ◽  
Local Areas
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