Dimeric allostery mechanism of the plant circadian clock photoreceptor ZEITLUPE

In Arabidopsis thaliana, the Light-Oxygen-Voltage (LOV) domain containing protein ZEITLUPE (ZTL) integrates light quality, intensity, and duration into regulation of the circadian clock. Recent structural and biochemical studies of ZTL indicate that the protein diverges from other members of the LOV superfamily in its allosteric mechanism, and that the divergent allosteric mechanism hinges upon conservation of two signaling residues G46 and V48 that alter dynamic motions of a Gln residue implicated in signal transduction in all LOV proteins. Here, we delineate the allosteric mechanism of ZTL via an integrated computational approach that employs atomistic simulations of wild type and allosteric variants of ZTL in the functional dark and light states, together with Markov state and supervised machine learning classification models. This approach has unveiled key factors of the ZTL allosteric mechanisms, and identified specific interactions and residues implicated in functional allosteric changes. The final results reveal atomic level insights into allosteric mechanisms of ZTL function that operate via a non-trivial combination of population-shift and dynamics-driven allosteric pathways.

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Systematic evaluation of supervised machine learning for sample origin prediction using metagenomic sequencing data

10.21203/rs.3.rs-15502/v1 ◽

2020 ◽

Author(s):

Chih-yu Chen ◽

Andrea D Tyler

Keyword(s):

Machine Learning ◽

Supervised Machine Learning ◽

Detection Sensitivity ◽

Prediction Errors ◽

Metagenomic Sequencing ◽

Learning Approaches ◽

Microbial Abundance ◽

Classification Models ◽

Machine Learning Classification ◽

Analytical Approaches

Abstract Background:The advent of metagenomic sequencing provides microbial abundance patterns that can be leveraged for sample origin prediction. Supervised machine learning classification approaches have been reported to predict sample origin accurately when the origin has been previously sampled. Using metagenomic datasets provided by the 2019 CAMDA challenge, we evaluated the influence of technical, analytical and machine learning approaches for result interpretation and source prediction of new origins.Results:Comparison between 16S rRNA amplicon and shotgun sequencing approaches as well as metagenomic analytical tools showed differences in measured microbial abundance of the same samples, especially for organisms present at low abundance. Shotgun sequence data analyzed using Kraken2 and Bracken taxonomic annotation, had higher detection sensitivity than did other methods. As classification models are limited to labeling previously trained origins, we proposed an alternative approach using Lasso-regularized multivariate regression to predict geographic coordinates for comparison. In both models, the prediction errors were much higher in Leave-1-city-out than in 10-fold cross validation, the former of which realistically forecasted the difficulty in accurately predicting samples from new origins than pre-trained origins. The challenge was further confirmed using mystery samples obtained from new origins. Overall, prediction performances between regression and classification models, as measured by mean squared error, were comparable on mystery samples. Due to higher prediction errors for samples from new origins, we provided an additional strategy based on prediction ambiguity to infer whether a sample is from a new origin for practical applications. Lastly, we showed increased prediction error when data from a different sequencing protocol were included as training data.Conclusions:Here we highlighted the capacity of predicting sample origin accurately with pre-trained origins and the challenge of predicting new origins through both regression and classification models. Overall, the work provided a summary evaluation of sequencing techniques, protocol, taxonomic analytical approaches, and machine learning approaches to inform future designs in metagenomic prediction of sample origin.

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Machine Learning Classification and Regression Approaches for Optical Network Traffic Prediction

Electronics ◽

10.3390/electronics10131578 ◽

2021 ◽

Vol 10 (13) ◽

pp. 1578

Author(s):

Daniel Szostak ◽

Adam Włodarczyk ◽

Krzysztof Walkowiak

Keyword(s):

Machine Learning ◽

Optical Networks ◽

Network Traffic ◽

Optical Network ◽

Optimization Methods ◽

Supervised Machine Learning ◽

Traffic Prediction ◽

Machine Learning Classification ◽

Classification And Regression ◽

Network Technologies

Rapid growth of network traffic causes the need for the development of new network technologies. Artificial intelligence provides suitable tools to improve currently used network optimization methods. In this paper, we propose a procedure for network traffic prediction. Based on optical networks’ (and other network technologies) characteristics, we focus on the prediction of fixed bitrate levels called traffic levels. We develop and evaluate two approaches based on different supervised machine learning (ML) methods—classification and regression. We examine four different ML models with various selected features. The tested datasets are based on real traffic patterns provided by the Seattle Internet Exchange Point (SIX). Obtained results are analyzed using a new quality metric, which allows researchers to find the best forecasting algorithm in terms of network resources usage and operational costs. Our research shows that regression provides better results than classification in case of all analyzed datasets. Additionally, the final choice of the most appropriate ML algorithm and model should depend on the network operator expectations.

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Prediction of activity and selectivity profiles of human Carbonic Anhydrase inhibitors using machine learning classification models

Journal of Cheminformatics ◽

10.1186/s13321-021-00499-y ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Annachiara Tinivella ◽

Luca Pinzi ◽

Giulio Rastelli

Keyword(s):

Machine Learning ◽

Carbonic Anhydrase ◽

A Priori ◽

Selective Inhibition ◽

Great Promise ◽

Classification Models ◽

Machine Learning Classification ◽

Central Interest ◽

Human Carbonic Anhydrase ◽

In Silico Models

AbstractThe development of selective inhibitors of the clinically relevant human Carbonic Anhydrase (hCA) isoforms IX and XII has become a major topic in drug research, due to their deregulation in several types of cancer. Indeed, the selective inhibition of these two isoforms, especially with respect to the homeostatic isoform II, holds great promise to develop anticancer drugs with limited side effects. Therefore, the development of in silico models able to predict the activity and selectivity against the desired isoform(s) is of central interest. In this work, we have developed a series of machine learning classification models, trained on high confidence data extracted from ChEMBL, able to predict the activity and selectivity profiles of ligands for human Carbonic Anhydrase isoforms II, IX and XII. The training datasets were built with a procedure that made use of flexible bioactivity thresholds to obtain well-balanced active and inactive classes. We used multiple algorithms and sampling sizes to finally select activity models able to classify active or inactive molecules with excellent performances. Remarkably, the results herein reported turned out to be better than those obtained by models built with the classic approach of selecting an a priori activity threshold. The sequential application of such validated models enables virtual screening to be performed in a fast and more reliable way to predict the activity and selectivity profiles against the investigated isoforms.

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COMPLEMENTATION ANALYSIS OF LINKED CIRCADIAN CLOCK MUTANTS OF NEUROSPORA CRASSA

Genetics ◽

10.1093/genetics/82.1.9 ◽

1976 ◽

Vol 82 (1) ◽

pp. 9-17 ◽

Cited By ~ 3

Author(s):

Jerry F Feldman ◽

Marian N Hoyle

Keyword(s):

Linkage Group ◽

Circadian Clock ◽

Neurospora Crassa ◽

Period Length ◽

Complementation Analysis ◽

Wild Type ◽

The Right

ABSTRACT A fourth mutant of Neurospora crassa, designated frq-4, has been isolated in which the period length of the circadian conidiation rhythm is shortened to 19.3 ± 0.3 hours. This mutant is tightly linked to the three previously isolated frq mutants, and all four map to the right arm of linkage group VII about 10 map units from the centromere. Complementation tests suggest, but do not prove, that all four mutations are allelic, since each of the four mutants is co-dominant with the frq + allele—i.e., heterokaryons have period lengths intermediate between the mutant and wild-type—and since heterokaryons between pairs of mutants also have period lengths intermediate between those of the two mutants.

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Source allocation of per- and polyfluoroalkyl substances (PFAS) with supervised machine learning: Classification performance and the role of feature selection in an expanded dataset

Chemosphere ◽

10.1016/j.chemosphere.2021.130124 ◽

2021 ◽

Vol 275 ◽

pp. 130124

Author(s):

Tohren C.G. Kibbey ◽

Rafal Jabrzemski ◽

Denis M. O’Carroll

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Classification Performance ◽

Supervised Machine Learning ◽

Machine Learning Classification ◽

Polyfluoroalkyl Substances ◽

Source Allocation

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Evaluating disaster-related tweet credibility using content-based and user-based features

Information Discovery and Delivery ◽

10.1108/idd-04-2020-0044 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Nasser Assery ◽

Yuan (Dorothy) Xiaohong ◽

Qu Xiuli ◽

Roy Kaushik ◽

Sultan Almalki

Keyword(s):

Machine Learning ◽

Unsupervised Learning ◽

Supervised Learning ◽

Emergency Response ◽

Learning Model ◽

Performance Comparison ◽

Supervised Machine Learning ◽

Learning Methods ◽

Content Type ◽

Machine Learning Classification

Purpose This study aims to propose an unsupervised learning model to evaluate the credibility of disaster-related Twitter data and present a performance comparison with commonly used supervised machine learning models. Design/methodology/approach First historical tweets on two recent hurricane events are collected via Twitter API. Then a credibility scoring system is implemented in which the tweet features are analyzed to give a credibility score and credibility label to the tweet. After that, supervised machine learning classification is implemented using various classification algorithms and their performances are compared. Findings The proposed unsupervised learning model could enhance the emergency response by providing a fast way to determine the credibility of disaster-related tweets. Additionally, the comparison of the supervised classification models reveals that the Random Forest classifier performs significantly better than the SVM and Logistic Regression classifiers in classifying the credibility of disaster-related tweets. Originality/value In this paper, an unsupervised 10-point scoring model is proposed to evaluate the tweets’ credibility based on the user-based and content-based features. This technique could be used to evaluate the credibility of disaster-related tweets on future hurricanes and would have the potential to enhance emergency response during critical events. The comparative study of different supervised learning methods has revealed effective supervised learning methods for evaluating the credibility of Tweeter data.

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A Comparative Study on Machine Learning Classification Models for Activity Recognition

Journal of Information Technology & Software Engineering ◽

10.4172/2165-7866.1000209 ◽

2017 ◽

Vol 07 (04) ◽

Cited By ~ 7

Author(s):

Mohsen Nabian

Keyword(s):

Machine Learning ◽

Comparative Study ◽

Activity Recognition ◽

Classification Models ◽

Machine Learning Classification

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The plant circadian clock influences rhizosphere community structure and function

10.1101/158576 ◽

2017 ◽

Cited By ~ 1

Author(s):

Charley J. Hubbard ◽

Marcus T. Brock ◽

Linda T.A. van Diepen ◽

Loïs Maignien ◽

Brent E. Ewers ◽

...

Keyword(s):

Community Structure ◽

Microbial Community ◽

Circadian Clock ◽

Structure And Function ◽

Plant Performance ◽

Wild Type ◽

Night Time ◽

History Of ◽

And Function ◽

Rhizosphere Community

AbstractPlants alter chemical and physical properties of soil, and thereby influence rhizosphere microbial community structure. The structure of microbial communities may in turn affect plant performance. Yet, outside of simple systems with pairwise interacting partners, the plant genetic pathways that influence microbial community structure remain largely unknown, as are the performance feedbacks of microbial communities selected by the host plant genotype. We investigated the role of the plant circadian clock in shaping rhizosphere community structure and function. We performed 16S rRNA gene sequencing to characterize rhizosphere bacterial communities of Arabidopsis thaliana between day and night time points, and tested for differences in community structure between wild-type (Ws) vs. clock mutant (toc1-21, ztl-30) genotypes. We then characterized microbial community function, by growing wild-type plants in soils with an overstory history of Ws, toc1-21 or ztl-30 and measuring plant performance. We observed that rhizosphere community structure varied between day and night time points, and clock misfunction significantly altered rhizosphere communities. Finally, wild-type plants germinated earlier and were larger when inoculated with soils having an overstory history of wild-type in comparison to clock mutant genotypes. Our findings suggest the circadian clock of the plant host influences rhizosphere community structure and function.

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