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Graph Embedding Deep Learning Guides Microbial Biomarkers' Identification

Qiang Zhu, Xingpeng Jiang, Qing Zhu, Min Pan, Tingting He
<span title="2019-11-22">2019</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/r7trx2kj6je5jhtaoy3rztibgy" style="color: black;">Frontiers in Genetics</a> </i> &nbsp;
In addition, deep learning is considered as black box and hard to interpret. These factors make deep learning not widely used in microbiome-wide association studies.  ...  Secondly, we introduce a feature selection method based on graph embedding and validate the biological meaning of microbial markers. The code is available at https://github.com/MicroAVA/GEDFN.git.  ...  The previous work proposed a feature selection method based on Deep Forest (Zhu et al., 2018) ; however, there is less work on microbiome-wide association studies via Deep Neural Network and less research  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fgene.2019.01182">doi:10.3389/fgene.2019.01182</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31824573">pmid:31824573</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6883002/">pmcid:PMC6883002</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/z4l75rsobfhurihowp7ugl4mne">fatcat:z4l75rsobfhurihowp7ugl4mne</a> </span>
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Application of Deep Learning in Microbiome

Qiang Zhu, Ban Huo, Han Sun, Bojing Li, Xingpeng Jiang
<span title="">2020</span> <i title="Atlantis Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rls3zr3inrhyzksqgbjhfj7aha" style="color: black;">Journal of Artificial Intelligence for Medical Sciences</a> </i> &nbsp;
Based on these discussions, we recommend that before using deep learning to conduct microbiome-wide association studies, it is essential to consider prior knowledge such as phylogeny, which would improve  ...  In this survey, we introduce the application of machine learning in microbial data analysis and focus on microbial classification and feature selection tasks.  ...  To deal with feature selection via deep learning, Zhu et al. introduced an ensemble feature selection method based on Deep Forest to conduct MWAS [57] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2991/jaims.d.201028.001">doi:10.2991/jaims.d.201028.001</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hnopfambffdlrcgbi4x4ud6phi">fatcat:hnopfambffdlrcgbi4x4ud6phi</a> </span>
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Kernel principal components based cascade forest towards disease identification with human microbiota

Jiayu Zhou, Yanqing Ye, Jiang Jiang
<span title="2021-12-23">2021</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/bnylrk2y7bfnrn7u2f2vjkx7ta" style="color: black;">BMC Medical Informatics and Decision Making</a> </i> &nbsp;
However, the abundance matrix of microbiome data is so sparse, an interpretable deep model is crucial to further represent and mine the data for expansion, such as the deep forest model.  ...  Methods In this work, we propose the kernel principal components based cascade forest method, so-called KPCCF, to classify the disease states of patients by using taxonomic profiles of the microbiome at  ...  Deep Neural Networks (DNNs) have been widely exploited recently for meta-genomic association studies [24, 25] , metagenomic classification [26, 27] , and disease diagnose [28, 29] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12911-021-01705-5">doi:10.1186/s12911-021-01705-5</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34949186">pmid:34949186</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8697468/">pmcid:PMC8697468</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kqrbdiwe6ffm5hukvuszl4msh4">fatcat:kqrbdiwe6ffm5hukvuszl4msh4</a> </span>
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Towards multi-label classification: Next step of machine learning for microbiome research

Shunyao Wu, Yuzhu Chen, Zhiruo Li, Jian Li, Fengyang Zhao, Xiaoquan Su
<span title="2021-04-28">2021</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/cmflnsn2l5gylcviv447te3mku" style="color: black;">Computational and Structural Biotechnology Journal</a> </i> &nbsp;
Machine learning (ML) has been widely used in microbiome research for biomarker selection and disease prediction.  ...  multi-label classification in microbiome-based studies.  ...  Table 1 1 Characteristics of machine learning methods widely used for microbiome-based disease detection.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.csbj.2021.04.054">doi:10.1016/j.csbj.2021.04.054</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34093989">pmid:34093989</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8131981/">pmcid:PMC8131981</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7z5esebtpjgudospdbbmmpicvu">fatcat:7z5esebtpjgudospdbbmmpicvu</a> </span>
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Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

Laura Judith Marcos-Zambrano, Kanita Karaduzovic-Hadziabdic, Tatjana Loncar Turukalo, Piotr Przymus, Vladimir Trajkovik, Oliver Aasmets, Magali Berland, Aleksandra Gruca, Jasminka Hasic, Karel Hron, Thomas Klammsteiner, Mikhail Kolev (+17 others)
<span title="2021-02-19">2021</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/67anf6qgandy5adz4rakospiuq" style="color: black;">Frontiers in Microbiology</a> </i> &nbsp;
The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine  ...  This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics.  ...  ACKNOWLEDGMENTS The authors are grateful to all COST Action CA18131 "Statistical and machine learning techniques in human microbiome studies" members for their contribution in discussion about evaluation  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fmicb.2021.634511">doi:10.3389/fmicb.2021.634511</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33737920">pmid:33737920</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7962872/">pmcid:PMC7962872</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wbun4lkwwjen5ccdy4zb7mnz3q">fatcat:wbun4lkwwjen5ccdy4zb7mnz3q</a> </span>
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Application of Deep Learning in Plant–Microbiota Association Analysis

Zhiyu Deng, Jinming Zhang, Junya Li, Xiujun Zhang
<span title="2021-10-08">2021</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/r7trx2kj6je5jhtaoy3rztibgy" style="color: black;">Frontiers in Genetics</a> </i> &nbsp;
The ability of feature representation and pattern recognition is the advantage of deep learning methods in modeling and interpretation for association analysis.  ...  Here, we review the analytic strategies in the microbiome data analysis and describe the applications of deep learning models for plant–microbiome correlation studies.  ...  ACKNOWLEDGMENTS We thank all the members of Plant Bioinformatics Group at Wuhan Botanical Garden, CAS for the discussions.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fgene.2021.697090">doi:10.3389/fgene.2021.697090</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34691142">pmid:34691142</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8531731/">pmcid:PMC8531731</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/poo5bdc2ynetzafqoydz6psc2e">fatcat:poo5bdc2ynetzafqoydz6psc2e</a> </span>
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A Review and Tutorial of Machine Learning Methods for Microbiome Host Trait Prediction

Yi-Hui Zhou, Paul Gallins
<span title="2019-06-25">2019</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/r7trx2kj6je5jhtaoy3rztibgy" style="color: black;">Frontiers in Genetics</a> </i> &nbsp;
One aspect specific to microbiome prediction is the use of taxonomy-informed feature selection.  ...  Methods are described at an introductory level, and R/Python code for the analyses is provided.  ...  Chris Smith for the IT support in Bioinformatics Research Center.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fgene.2019.00579">doi:10.3389/fgene.2019.00579</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31293616">pmid:31293616</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6603228/">pmcid:PMC6603228</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yjjvaz3dqngxlm7wq6zm2jwnv4">fatcat:yjjvaz3dqngxlm7wq6zm2jwnv4</a> </span>
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Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring

Ryan B. Ghannam, Stephen M. Techtmann
<span title="2021-01-27">2021</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/cmflnsn2l5gylcviv447te3mku" style="color: black;">Computational and Structural Biotechnology Journal</a> </i> &nbsp;
In order to leverage machine learning into more translational research related to the microbiome and strengthen our ability to extract meaningful biological information, it is important for models to be  ...  Machine learning has appeal as a powerful tool that can provide deep insights into microbial communities and identify patterns in microbial community data.  ...  For these reasons, and on the basis of computational tractability, embedded methods are an ideal practical feature selection method for optimizing microbial-based ML models.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.csbj.2021.01.028">doi:10.1016/j.csbj.2021.01.028</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33680353">pmid:33680353</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7892807/">pmcid:PMC7892807</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xfmch3r3krekvhy5rmcc4bjys4">fatcat:xfmch3r3krekvhy5rmcc4bjys4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210716114333/https://digitalcommons.mtu.edu/cgi/viewcontent.cgi?article=33983&amp;context=michigantech-p" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c8/cc/c8cc7b5eaa91af9ee4d7b34bd5c81f38d1e9e1e1.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.csbj.2021.01.028"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> elsevier.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7892807" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Multiscale network-based approaches in bioinformatics and biomedicine

Huiru (Jane) Zheng, Xiaohua (Tony) Hu
<span title="2020-01-08">2020</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/3jmevtg2ajcdnpgyntimblv7iu" style="color: black;">Methods</a> </i> &nbsp;
[8] applied deep random forest to investigate the microbiome-wide associations and proposed an ensemble feature selection method for the identification of microbial biomarkers.  ...  Results demonstrate that the proposed method is capable of selectively integrating multiple inter-relational and intra-relational data sources for a better prediction of the lncRNAsdisease association.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.ymeth.2020.01.002">doi:10.1016/j.ymeth.2020.01.002</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31926262">pmid:31926262</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ympaq52sazaijbfby2tipwpmq4">fatcat:ympaq52sazaijbfby2tipwpmq4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210427205729/https://pure.ulster.ac.uk/ws/files/88339999/1_s2.0_S1046202320300037_main_3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f4/0e/f40ec51be47dc0db537a895fff8c879422587272.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.ymeth.2020.01.002"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Gut microbiota and artificial intelligence approaches: A scoping review

Ernesto Iadanza, Rachele Fabbri, Džana Bašić-ČiČak, Amedeo Amedei, Jasminka Hasic Telalovic
<span title="2020-10-26">2020</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2xi6tzpfxvckxneqmnggi3o5km" style="color: black;">Health and Technology</a> </i> &nbsp;
The papers included in the review describe the use of ML or DL methods applied to the study of human gut microbiota. In total, 1109 papers were considered in this study.  ...  The most applied ML algorithm was Random Forest and it also exhibited the best performances.  ...  Random Forest Random Forest (RF) is an ensemble method introduced by Breiman in 2001 [30] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s12553-020-00486-7">doi:10.1007/s12553-020-00486-7</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/elpael2iybfx3hk7htzmut276a">fatcat:elpael2iybfx3hk7htzmut276a</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210429014438/https://link.springer.com/content/pdf/10.1007/s12553-020-00486-7.pdf?error=cookies_not_supported&amp;code=8b08905b-1cd6-4349-9068-0774f598bb2c" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/38/b8/38b897405d152ff7737a1430287dcb070190cd92.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s12553-020-00486-7"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> springer.com </button> </a>

Microbiome Sample Comparison and Search: From Pair-Wise Calculations to Model-Based Matching

Yuguo Zha, Hui Chong, Kang Ning
<span title="2021-04-07">2021</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/67anf6qgandy5adz4rakospiuq" style="color: black;">Frontiers in Microbiology</a> </i> &nbsp;
In this review, we systematically compared distance-based, unsupervised, and supervised methods for microbiome sample comparison and search.  ...  Thirdly, we provided several applications for microbiome sample comparisons and searches, and provided suggestions on the choice of methods.  ...  AUTHOR CONTRIBUTIONS KN conceived and proposed the idea, and designed the study. YZ, HC, and KN contributed to editing and proof-reading the manuscript.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fmicb.2021.642439">doi:10.3389/fmicb.2021.642439</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33897651">pmid:33897651</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8059704/">pmcid:PMC8059704</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ow6lfl6zgvdvvja2zwmbcslaha">fatcat:ow6lfl6zgvdvvja2zwmbcslaha</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210420212727/https://fjfsdata01prod.blob.core.windows.net/articles/files/642439/pubmed-zip/.versions/1/.package-entries/fmicb-12-642439/fmicb-12-642439.pdf?sv=2018-03-28&amp;sr=b&amp;sig=KAGdX%2FIgRmVjR6FziymSZtwFujPvttcWZcqHnrh5ywg%3D&amp;se=2021-04-20T21%3A27%3A55Z&amp;sp=r&amp;rscd=attachment%3B%20filename%2A%3DUTF-8%27%27fmicb-12-642439.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/25/5f/255fb9a61e0491412c6bbe5b805f71257a7b4a6f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fmicb.2021.642439"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> frontiersin.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8059704" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Big data in IBD: big progress for clinical practice

Nasim Sadat Seyed Tabib, Matthew Madgwick, Padhmanand Sudhakar, Bram Verstockt, Tamas Korcsmaros, Séverine Vermeire
<span title="2020-02-28">2020</span> <i title="BMJ"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/hvnkj5gfubdarprnm5x7rslxou" style="color: black;">Gut</a> </i> &nbsp;
Approaches such as machine learning may enable patient stratification, prediction of disease progression and therapy responses for fine-tuning treatment options with positive impacts on cost, health and  ...  IBD is a complex multifactorial inflammatory disease of the gut driven by extrinsic and intrinsic factors, including host genetics, the immune system, environmental factors and the gut microbiome.  ...  feature selection method (the penalised logistic regression model).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1136/gutjnl-2019-320065">doi:10.1136/gutjnl-2019-320065</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/32111636">pmid:32111636</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7398484/">pmcid:PMC7398484</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/64i6abikczhvbjwabgvp6cxyke">fatcat:64i6abikczhvbjwabgvp6cxyke</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210718003026/https://lirias2repo.kuleuven.be/bitstream/handle/123456789/656174/Big%20data%20in%20IBD,%20big%20progress%20for%20clinical%20practice.pdf;jsessionid=3EC3A9C20A60F3EEE02C230AF78F1733?sequence=2" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ed/85/ed852f676b0a9cf1cffb235774c19612e884b7b2.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1136/gutjnl-2019-320065"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> bmj.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7398484" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

ResistoXplorer: a web-based tool for visual, statistical and exploratory data analysis of resistome data

Achal Dhariwal, Roger Junges, Tsute Chen, Fernanda C Petersen
<span title="2021-01-06">2021</span> <i title="Oxford University Press (OUP)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dhm4lwu4tvbf5dklpg7zonzosa" style="color: black;">NAR Genomics and Bioinformatics</a> </i> &nbsp;
The study of resistomes using whole metagenomic sequencing enables high-throughput identification of resistance genes in complex microbial communities, such as the human microbiome.  ...  enables users to intuitively explore the associations between antimicrobial resistance genes and the microbial hosts using network visual analytics to gain biological insights.  ...  In particular, the Random Forest algorithm uses an ensemble of classification trees (forest), with final class prediction based on the majority vote of the ensemble.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/nargab/lqab018">doi:10.1093/nargab/lqab018</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33796850">pmid:33796850</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7991225/">pmcid:PMC7991225</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hxcqeewwqjhfng6ya4jkdbl6lu">fatcat:hxcqeewwqjhfng6ya4jkdbl6lu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210715072921/https://www.duo.uio.no/bitstream/handle/10852/85829/lqab018.pdf?sequence=1" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/9c/4f/9c4f5f0e988840800b4a3c4facff8bc29a5e576a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/nargab/lqab018"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> oup.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7991225" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Variability of Classification Results in Data with High Dimensionality and Small Sample Size

Jana Busa, Inese Polaka
<span title="2021-12-07">2021</span> <i title="Riga Technical University"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dlwvk7ko2bbfdgs6cbzmo3xu7q" style="color: black;">Information Technology and Management Science</a> </i> &nbsp;
The study focuses on the analysis of biological data containing information on the number of genome sequences of intestinal microbiome bacteria before and after antibiotic use.  ...  In the experiments, the authors examined how classification results were affected by feature selection and increased size of the data set.  ...  Feature Selection based on perceptron cannot.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.7250/itms-2021-0007">doi:10.7250/itms-2021-0007</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bt3uwajgoree7liy5qhiwzoyie">fatcat:bt3uwajgoree7liy5qhiwzoyie</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211216095356/https://itms-journals.rtu.lv/article/download/itms-2021-0007/pdf_1" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/8a/22/8a22397000f0dbbf2d77c009093509845a6a8408.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.7250/itms-2021-0007"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Could Artificial Intelligence/Machine Learning and Inclusion of Diet-Gut Microbiome Interactions Improve Disease Risk Prediction? Case Study: Coronary Artery Disease

Baiba Vilne, Juris Ķibilds, Inese Siksna, Ilva Lazda, Olga Valciņa, Angelika Krūmiņa
<span title="2022-04-11">2022</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/67anf6qgandy5adz4rakospiuq" style="color: black;">Frontiers in Microbiology</a> </i> &nbsp;
Finally, we provide an outlook for putting it all together for improved CAD risk predictions.  ...  Diet is one of the modifiable factors for improving lifestyle and disease prevention.  ...  FUNDING This research was funded by the Latvian Council of Science within the project Gut microbiome composition and diversity among health and lifestyle induced dietary regimen, project No. lzp-2018/2  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fmicb.2022.627892">doi:10.3389/fmicb.2022.627892</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/35479632">pmid:35479632</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC9036178/">pmcid:PMC9036178</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hq5mrzee25f35awo52blfuzecm">fatcat:hq5mrzee25f35awo52blfuzecm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220519165929/https://fjfsdata01prod.blob.core.windows.net/articles/files/627892/pubmed-zip/.versions/1/.package-entries/fmicb-13-627892/fmicb-13-627892.pdf?sv=2018-03-28&amp;sr=b&amp;sig=gP0%2BE2K%2BrFKog7EeHHji43xOXfVphCK%2Ft8VW6ZG95bc%3D&amp;se=2022-05-19T16%3A59%3A58Z&amp;sp=r&amp;rscd=attachment%3B%20filename%2A%3DUTF-8%27%27fmicb-13-627892.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/31/c7/31c725d75ec8884c73a35f53770b608a08211ae0.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fmicb.2022.627892"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> frontiersin.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9036178" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>
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