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Inferring species interaction networks from species abundance data: A comparative evaluation of various statistical and machine learning methods

Ali Faisal, Frank Dondelinger, Dirk Husmeier, Colin M. Beale
2010 Ecological Informatics  
Beale) the suitability of four statistical / machine learning methods for the identification of network structure on ecological data: Graphical Gaussian models (GGMs), L1-regularised linear regression  ...  The complexity of ecosystems is staggering, with hundreds or thousands of species interacting in a number of ways from competition and predation to facilitation and mutualism.  ...  We thank Jonathan Yearsley for providing the initial community simulation model and Marco Grzegorczyk for providing the Bayesian Network code and being available for questions.  ... 
doi:10.1016/j.ecoinf.2010.06.005 fatcat:bccq4vnfkffvhejli27z546jda

Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis

Chen Chen, Jie Hou, John J Tanner, Jianlin Cheng
2020 International Journal of Molecular Sciences  
We conclude with a discussion of how quantitative protein data can be used to reconstruct protein interactions and signaling networks.  ...  We first introduce bioinformatics software and tools designed for mass spectrometry-based protein identification and quantification, and then we review the different statistical and machine learning methods  ...  (e) Machine learning-based sample clustering. (f) Illustration of a network inferred from proteomics data. (g) Dimensionality reduction of proteomics expression profile. Int. J. Mol.  ... 
doi:10.3390/ijms21082873 pmid:32326049 pmcid:PMC7216093 fatcat:5zbrkah4xvec3mq3ypal7cxfbu

DAnIEL: A User-Friendly Web Server for Fungal ITS Amplicon Sequencing Data [article]

Daniel Loos, Lu Zhang, Christine Beemelmanns, Oliver Kurzai, Gianni Panagiotou
2021 bioRxiv   pre-print
analysis and machine learning with a manually curated relational database; (iii) comparing the user's uploaded datasets with publicly available from the Sequence Read Archive.  ...  Here we present a web server dedicated to the comprehensive analysis of the human mycobiome for (i) translating raw sequencing reads to data tables and high standard figures; (ii) integrating statistical  ...  Acknowledgments We thank the members of NRZMyk for providing us the data about clinical samples with fungal infections.  ... 
doi:10.1101/2021.04.12.437814 fatcat:ko3fzl64wngmhlbnbxdtnjhev4

Interpretable and accurate prediction models for metagenomics data [article]

Edi Prifti, Yann Chevaleyre, Blaise Hanczar, Eugeni Belda Cuesta, Karine Clement, Antoine Danchin, Jean-Daniel Zucker
2018 bioRxiv   pre-print
Yet, the current predictive models stemming from machine learning still behave as black boxes. Moreover, they seldom generalize well when learned on small datasets.  ...  Biomarker discovery using metagenomic data is becoming more prevalent for patient diagnosis, prognosis and risk evaluation.  ...  Experimental design The BTR models are tested on the 109 different datasets (see above) and compared with the methods from the state-of-the-art machine learning algorithms: support vector machine (SVM)  ... 
doi:10.1101/409144 fatcat:6nztkxatkzcfblzxlbr3ysveg4

Statistical computation methods for microbiome compositional data network inference [article]

Liang Chen, Qiuyan He, Hui Wan, Shun He, Minghua Deng
2021 arXiv   pre-print
In this paper, we provide a comprehensive review of emerging microbiome interaction network inference methods.  ...  A common and essential approach toward this objective involves the inference of microbiome interaction networks.  ...  The goal of microbiome compositional data network analysis is to utilize the observed relative abundance data of various microbes in order to make statistical inferences about the interaction network corresponding  ... 
arXiv:2109.01993v1 fatcat:lpg2kcil55d43ch2ottgbqspne

Hierarchical Bayesian models in ecology: Reconstructing species interaction networks from non-homogeneous species abundance data

Andrej Aderhold, Dirk Husmeier, Jack J. Lennon, Colin M. Beale, V. Anne Smith
2012 Ecological Informatics  
The elucidation of complex ecological systems with their interactions will benefit enormously from the development of new machine learning tools that aim to infer the structure of interaction networks  ...  We have evaluated the model on simulated data that combines a trophic niche model with a stochastic population model on a 2-dimensional lattice, and we have compared the performance of our model with L1  ...  Acknowledgment Part of the work was carried out while DH was employed at Biomathematics and Statistics Scotland (BioSS), and supported by the Scottish Governments Rural and Environment Science and Analytical  ... 
doi:10.1016/j.ecoinf.2012.05.002 fatcat:ircwfi67rnayndulkjm34zumiq

A new method for faster and more accurate inference of species associations from big community data [article]

Maximilian Pichler, Florian Hartig
2021 arXiv   pre-print
We implemented sjSDM in PyTorch, a modern machine learning framework that can make use of CPU and GPU calculations.  ...  and accuracy of the inferred species-species and species-environmental associations. 3.  ...  Tree-based inference of species interaction networks from abundance data. Methods in Ecology and Evolution, 11, 621-632.  ... 
arXiv:2003.05331v5 fatcat:2565sl4qlvdq5f2ainqwpzfdpu

A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science

James H. Faghmous, Vipin Kumar
2014 Big Data  
Despite the urgency, data science has had little impact on furthering our understanding of our planet in spite of the abundance of climate data.  ...  This is a stark contrast from other fields such as advertising or electronic commerce where big data has been a great success story.  ...  J.H.F. was also funded by an NSF Graduate Research Fellowship and a University of Minnesota Doctoral Dissertation Fellowship.  ... 
doi:10.1089/big.2014.0026 pmid:25276499 pmcid:PMC4174912 fatcat:jxldvpyj7bd4fnhyx5bsxiz3qq

Big data in IBD: big progress for clinical practice

Nasim Sadat Seyed Tabib, Matthew Madgwick, Padhmanand Sudhakar, Bram Verstockt, Tamas Korcsmaros, Séverine Vermeire
2020 Gut  
We also outline the challenges and opportunities presented by machine learning and big data in clinical IBD research.  ...  In this narrative review, we discuss how big data integration and machine learning have been applied to translational IBD research.  ...  Several studies have reported an increase in the abundance of certain species from the Proteobacteria phylum, such as Escherichia coli, and a decline in anti-inflammatory butyrate-producing bacteria species  ... 
doi:10.1136/gutjnl-2019-320065 pmid:32111636 pmcid:PMC7398484 fatcat:64i6abikczhvbjwabgvp6cxyke

BIG DATA ANALYTICS AND PRECISION ANIMAL AGRICULTURE SYMPOSIUM: Machine learning and data mining advance predictive big data analysis in precision animal agriculture1

Gota Morota, Ricardo V Ventura, Fabyano F Silva, Masanori Koyama, Samodha C Fernando
2018 Journal of Animal Science  
To address such knowledge gaps, this article outlines a framework for machine learning and data mining and offers a glimpse into how they can be applied to solve pressing problems in animal sciences.  ...  The emerging fields of machine learning and data mining are expected to be instrumental in helping meet the daunting challenges facing global agriculture.  ...  MACHINE LEARNING FRAMEWORK Machine learning, also known as statistical learning, is a subfield of artificial intelligence dedicated to the study of algorithms for prediction and inference.  ... 
doi:10.1093/jas/sky014 pmid:29385611 fatcat:xrnekxfbqbea7gq5zejupxhdry

Dynamic interaction network inference from longitudinal microbiome data

Jose Lugo-Martinez, Daniel Ruiz-Perez, Giri Narasimhan, Ziv Bar-Joseph
2019 Microbiome  
However, analysis of such data is challenging and very few methods have been developed to reconstruct dynamic models from time series microbiome data.  ...  In many of these studies, researchers collect longitudinal data with the goal of understanding not only just the composition of the microbiome but also the interactions between the different taxa.  ...  Availability of data and materials All code and longitudinal microbiome data sets can be downloaded from https://github.com/jlugomar/longitudinal_microbiome_analysis_public.  ... 
doi:10.1186/s40168-019-0660-3 pmid:30940197 pmcid:PMC6446388 fatcat:43cjc6x7dneanphq5ytqlzdiyq

Data Integration in Functional Analysis of MicroRNAs

Hasan Ogul, Mahinur S. Akkaya
2011 Current Bioinformatics  
The discovery of microRNAs (miRNAs), about a decade ago, has completely changed our understanding of the complexity of gene regulatory networks.  ...  To elucidate the individual or co-operative effects of miRNAs, it is required to place them in the overall network of gene regulation and link them to other pathways and systems-level processes.  ...  Recent research suggests that incorporation of various machine learning methods by using the same factors as classifier's feature space has improved overall target prediction accuracy [6, 17, 53, 99,  ... 
doi:10.2174/157489311798072945 fatcat:cfojul2hszaq7oy2crc44fx56a

Dynamic interaction network inference from longitudinal microbiome data [article]

Jose Lugo-Martinez, Daniel Ruiz-Perez, Giri Narasimhan, Ziv Bar-Joseph
2018 bioRxiv   pre-print
However, analysis of such data is challenging and very few methods have been developed to reconstruct dynamic models from time series microbiome data.  ...  The aligned profiles are then used to learn a dynamic Bayesian network which represents causal relationships between taxa and clinical variables.  ...  we compared our DBNs to three other methods suggested for modeling interactions among taxa: (a) McGeachie et al. [10] developed a different DBN model where network learning is estimated from the BDeu scoring  ... 
doi:10.1101/430462 fatcat:sqdn46yw7vaxtacvg6uhpq7fjm

Modeling time-series data from microbial communities

Benjamin J Ridenhour, Sarah L Brooker, Janet E Williams, James T Van Leuven, Aaron W Miller, M Denise Dearing, Christopher H Remien
2017 The ISME Journal  
the breadth of the data to infer ecological interactions from these longitudinal data.  ...  We test the method on gut microbiome data from white-throated woodrats (Neotoma albigula) that were fed varying amounts of the plant secondary compound oxalate over a period of 22 days to estimate interactions  ...  Acknowledgements We would like to thank Jodie Nicotra for help with comments and edits on the manuscript as well as members of CMCI, IBEST, and BCB at the University of Idaho for helpful discussions helped  ... 
doi:10.1038/ismej.2017.107 pmid:28786973 fatcat:qxiwj7s7d5bntj3lu46mkycodu

Reverse-engineering biological networks from large data sets [article]

Joseph L. Natale, David Hofmann, Damian G. Hernández, Ilya Nemenman
2017 arXiv   pre-print
Much of contemporary systems biology owes its success to the abstraction of a network, the idea that diverse kinds of molecular, cellular, and organismal species and interactions can be modeled as relational  ...  Since the advent of high-throughput data acquisition technologies in fields such as genomics, metabolomics, and neuroscience, the automated inference and reconstruction of such interaction networks directly  ...  Why is the task of learning networks from data considered so important?  ... 
arXiv:1705.06370v2 fatcat:owto6kvzizbebggfagrrf7itsi
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