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hal.structure.identifierAgroécologie [Dijon]
dc.contributor.authorBARROSO-BERGADA, Didac
hal.structure.identifierUniversity of Surrey [UNIS]
dc.contributor.authorTAMADDONI-NEZHAD, Alizera
hal.structure.identifierImperial College London
dc.contributor.authorMUGGLETON, Stephen H.
hal.structure.identifierBiodiversité, Gènes & Communautés [BioGeCo]
dc.contributor.authorVACHER, Corinne
hal.structure.identifierSyngenta Crop Protect LLC, Greensboro, NC 27409 USA.
dc.contributor.authorGALIC, Nika
hal.structure.identifierAgroécologie [Dijon]
dc.contributor.authorBOHAN, David
dc.date.conference2021-10-25
dc.identifier.isbn0302-9743 978-3-030-97454-1; 978-3-030-97453-4
dc.description.abstractEnInteraction between species in microbial communities plays an important role in the functioning of all ecosystems, from cropland soils to human gut microbiota. Many statistical approaches have been proposed to infer these interactions from microbial abundance information. However, these statistical approaches have no general mechanisms for incorporating existing ecological knowledge in the inference process. We propose an Abductive/Inductive Logic Programming (A/ILP) framework to infer microbial interactions from microbial abundance data, by including logical descriptions of different types of interaction as background knowledge in the learning. This framework also includes a new mechanism for estimating the probability of each interaction based on the frequency and compression of hypotheses computed during the abduction process. This is then used to identify real interactions using a bootstrapping, re-sampling procedure. We evaluate our proposed framework on simulated data previously used to benchmark statistical interaction inference tools. Our approach has comparable accuracy to SparCC, which is one of the state-of-the-art statistical interaction inference algorithms, but with the the advantage of including ecological background knowledge. Our proposed framework opens up the opportunity of inferring ecological interaction information from diverse ecosystems that currently cannot be studied using other methods.
dc.language.isoen
dc.publisherSpringer International Publishing Ag
dc.subject.enAbductive/Inductive Logic Programming (A/ILP)
dc.subject.eninteraction network
dc.subject.eninference
dc.subject.enmachine learning of ecological networks
dc.subject.enhypothesis frequency
dc.subject.enestimation (HFE)
dc.subject.encomputer science
dc.title.enMachine learning of microbial interactions using abductive ILP and hypothesis frequency/compression estimation
dc.typeCommunication dans un congrès
dc.subject.halSciences du Vivant [q-bio]
bordeaux.volume13191
bordeaux.conference.title30th International Conference on Inductive Logic Programming (ILP) held as part of the 1st International Joint Conference on Learning and Reasoning (IJCLR)
bordeaux.countryFR
bordeaux.conference.cityVirtuel
bordeaux.peerReviewednon
hal.identifierhal-03692876
hal.version1
hal.invitednon
hal.proceedingsnon
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03692876v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.volume=13191&rft.au=BARROSO-BERGADA,%20Didac&TAMADDONI-NEZHAD,%20Alizera&MUGGLETON,%20Stephen%20H.&VACHER,%20Corinne&GALIC,%20Nika&rft.isbn=0302-9743%20978-3-030-97454-1;%20978-3-030-97453-4&rft.genre=unknown


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