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Highly Robust Classification: A Regularized Approach for Omics Data

Jan Kalina, Jaroslav Hlinka
<span title="">2016</span> <i title="SCITEPRESS - Science and and Technology Publications"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/lxwop3qtazexrkwj5xfe7k3uqm" style="color: black;">Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies</a> </i> &nbsp;
For this purpose, we propose a regularized version of the minimum weighted covariance determinant estimator, which is one of highly robust estimators of multivariate location and scatter.  ...  We use principles of robust statistics to propose classification methods suitable for data with the number of variables exceeding the number of observations.  ...  The authors are thankful to Anna Schlenker for the data analyzed in Section 3.4.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5220/0005623500170026">doi:10.5220/0005623500170026</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/biostec/KalinaH16.html">dblp:conf/biostec/KalinaH16</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gxdvbpqzfrdgtf2y6df4th6bea">fatcat:gxdvbpqzfrdgtf2y6df4th6bea</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190225181026/http://pdfs.semanticscholar.org/62e1/b0e3a883615f98e6722897fbc0c4e5ffc97f.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/62/e1/62e1b0e3a883615f98e6722897fbc0c4e5ffc97f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5220/0005623500170026"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

A dropout-regularized classifier development approach optimized for precision medicine test discovery from omics data

Joanna Roder, Carlos Oliveira, Lelia Net, Maxim Tsypin, Benjamin Linstid, Heinrich Roder
<span title="2019-06-13">2019</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/n5zrklrhlzhtdorf4rk4rmeo3i" style="color: black;">BMC Bioinformatics</a> </i> &nbsp;
The dropout-regularized combination method also generates an effective classifier in a classification task with a known confounding variable.  ...  It is based on a hierarchy of classification and information abstraction and combines boosting, bagging, and strong dropout regularization.  ...  CO and LN acquired the data and participated in its analysis and interpretation. MT participated in the interpretation of the data. BL was involved in acquisition of the data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12859-019-2922-2">doi:10.1186/s12859-019-2922-2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/42b3p4ps4ba4zpjoszw3mkocwa">fatcat:42b3p4ps4ba4zpjoszw3mkocwa</a> </span>
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Using prior knowledge from cellular pathways and molecular networks for diagnostic specimen classification

Enrico Glaab
<span title="2015-07-02">2015</span> <i title="Oxford University Press (OUP)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/op7ztx4fhvairowgqifu7dnvsi" style="color: black;">Briefings in Bioinformatics</a> </i> &nbsp;
Classical statistical learning approaches for specimen classification using omics data, however, often cannot provide diagnostic models with sufficient accuracy and robustness for heterogeneous diseases  ...  In recent years, new approaches for building multivariate biomarker models on omics data have been proposed, which exploit prior biological knowledge from molecular networks and cellular pathways to address  ...  , although noise in the data may make the estimates highly uncertain.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/bib/bbv044">doi:10.1093/bib/bbv044</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/26141830">pmid:26141830</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4870394/">pmcid:PMC4870394</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rdvyk7eyurc2tk7lp6otlyq6ne">fatcat:rdvyk7eyurc2tk7lp6otlyq6ne</a> </span>
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MORONET: Multi-omics Integration via Graph Convolutional Networks for Biomedical Data Classification [article]

Tongxin Wang, Wei Shao, Zhi Huang, Haixu Tang, Zhengming Ding, Kun Huang
<span title="2020-07-03">2020</span> <i title="Cold Spring Harbor Laboratory"> bioRxiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We demonstrate that MORONET outperforms other state-of-the-art supervised multi-omics integrative analysis approaches from a wide range of biomedical classification applications using mRNA expression data  ...  We present a novel multi-omics integrative method named Multi-Omics gRaph cOnvolutional NETworks (MORONET) for biomedical classification.  ...  Block_plsda and block_splsda represent the state-of-the-art approaches for supervised multi-omics integration and classification.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/2020.07.02.184705">doi:10.1101/2020.07.02.184705</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lemnficphncsxdkayxyruligbm">fatcat:lemnficphncsxdkayxyruligbm</a> </span>
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Extending Classification Algorithms to Case-Control Studies

Bryan Stanfill, Sarah Reehl, Lisa Bramer, Ernesto S Nakayasu, Stephen S Rich, Thomas O Metz, Marian Rewers, Bobbie-Jo Webb-Robertson, TEDDY Study Group
<span title="2019-07-15">2019</span> <i title="SAGE Publications"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/somvh6lnaje6fejxliemkhv63u" style="color: black;">Biomedical Engineering and Computational Biology</a> </i> &nbsp;
Classification is a common technique applied to 'omics data to build predictive models and identify potential markers of biomedical outcomes.  ...  We propose a data preprocessing step which generalizes and makes any linear or nonlinear classification algorithm, even those typically not appropriate for matched design data, available to be used to  ...  Acknowledgements The authors would like to thank PNNL scientist Jennifer Kyle for her help with filtering the lipidomic data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1177/1179597219858954">doi:10.1177/1179597219858954</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31320812">pmid:31320812</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6630079/">pmcid:PMC6630079</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/w6alhazcm5cdllmtxvcuaciyee">fatcat:w6alhazcm5cdllmtxvcuaciyee</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200507062024/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC6630079&amp;blobtype=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/12/d3/12d32758b0cc47264086a3f7e6ad85aace7f1aaf.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1177/1179597219858954"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> sagepub.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6630079" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Primal-dual for classification with rejection (PD-CR): a novel method for classification and feature selection—an application in metabolomics studies

David Chardin, Olivier Humbert, Caroline Bailleux, Fanny Burel-Vandenbos, Valerie Rigau, Thierry Pourcher, Michel Barlaud
<span title="2021-12-15">2021</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/n5zrklrhlzhtdorf4rk4rmeo3i" style="color: black;">BMC Bioinformatics</a> </i> &nbsp;
Background Supervised classification methods have been used for many years for feature selection in metabolomics and other omics studies.  ...  PD-CR projects data onto a low dimension space and performs classification by minimizing an appropriate quadratic cost.  ...  for validation, and formal analysis.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12859-021-04478-w">doi:10.1186/s12859-021-04478-w</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34911437">pmid:34911437</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8672607/">pmcid:PMC8672607</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qyymgmlwknfqdhk3nev64of6ru">fatcat:qyymgmlwknfqdhk3nev64of6ru</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220131234132/https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-021-04478-w.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/a1/d0/a1d01e2bda368f80d51b8af703c94d3f823b32a7.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12859-021-04478-w"> <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> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8672607" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

A Cascade Deep Forest Model for Breast Cancer Subtype Classification Using Multi-Omics Data

Ala'a El-Nabawy, Nahla A. Belal, Nashwa El-Bendary
<span title="2021-07-04">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ye33srllvnanjouxn4tmrfgjsq" style="color: black;">Mathematics</a> </i> &nbsp;
Many methods have been used for breast cancer subtype classification.  ...  In this work, a cascade Deep Forest is employed to classify breast cancer subtypes, IntClust and Pam50, using multi-omics datasets and different configurations.  ...  [4] used integrated omics data for cancer subtype classification using a deep neural forest model, HI-DFNForest, proposing an improved performance.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/math9131574">doi:10.3390/math9131574</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4b5perv7ifaqpcbnpqyqau4sga">fatcat:4b5perv7ifaqpcbnpqyqau4sga</a> </span>
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Sparse Proteomics Analysis – a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data

Tim O. F. Conrad, Martin Genzel, Nada Cvetkovic, Niklas Wulkow, Alexander Leichtle, Jan Vybiral, Gitta Kutyniok, Christof Schütte
<span title="2017-03-09">2017</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/n5zrklrhlzhtdorf4rk4rmeo3i" style="color: black;">BMC Bioinformatics</a> </i> &nbsp;
High-throughput proteomics techniques, such as mass spectrometry (MS)-based approaches, produce very high-dimensional data-sets.  ...  In a clinical setting one is often interested in how mass spectra differ between patients of different classes, for example spectra from healthy patients vs. spectra from patients having a particular disease  ...  Acknowledgements The authors are thankful to Irena Bojarovska for fruitful discussions and help conducting the experiments.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12859-017-1565-4">doi:10.1186/s12859-017-1565-4</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/28274197">pmid:28274197</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5343371/">pmcid:PMC5343371</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5tgx6kjdtfhcrn5nvtciukxfvu">fatcat:5tgx6kjdtfhcrn5nvtciukxfvu</a> </span>
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netDx: interpretable patient classification using integrated patient similarity networks

Shraddha Pai, Shirley Hui, Ruth Isserlin, Muhammad A Shah, Hussam Kaka, Gary D Bader
<span title="2019-03-14">2019</span> <i title="EMBO"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/oayrvwhnr5a2jm4pbva2dvz3ui" style="color: black;">Molecular Systems Biology</a> </i> &nbsp;
In a cancer survival benchmark dataset integrating up to six data types in four cancer types, netDx significantly outperforms most other machine-learning approaches across most cancer types.  ...  as a patient classifier and as a tool for discovery of biological features characteristic of disease.  ...  SP and MAS analyzed the data. SP wrote the netDx software package with contributions from MAS. SH, HK, and RI developed initial versions of netDx. SP and GDB wrote the paper. GDB supervised the work.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.15252/msb.20188497">doi:10.15252/msb.20188497</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/30872331">pmid:30872331</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6423721/">pmcid:PMC6423721</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wzsw735kafgofmpgznbnhrist4">fatcat:wzsw735kafgofmpgznbnhrist4</a> </span>
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JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data [article]

Jiadong Ji, Di He, Yang Feng, Yong He, Fuzhong Xue, Lei Xie
<span title="2017-01-09">2017</span> <i title="Cold Spring Harbor Laboratory"> bioRxiv </i> &nbsp; <span class="release-stage" >pre-print</span>
JDINAC provides a general framework for feature selection and classification using high-dimensional sparse omics data.  ...  At the same time, JDINAC uses the network biomarkers to build a classification model.  ...  Acknowledgements The authors would like to acknowledge TCGA for providing the BRCA data. We would also like to thank the patients for access to their study data. Conflict of Interest: none declared.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/099234">doi:10.1101/099234</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xkhl33s5vrewhnqhvmnvikvkiq">fatcat:xkhl33s5vrewhnqhvmnvikvkiq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190429171809/https://www.biorxiv.org/content/biorxiv/early/2017/01/09/099234.full.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/8a/ff/8affd6fd440f5af519c07b93f592c69b01777c10.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/099234"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> biorxiv.org </button> </a>

Cancer classification based on chromatin accessibility profiles with deep adversarial learning model

Hai Yang, Qiang Wei, Dongdong Li, Zhe Wang, Anna R. Panchenko
<span title="2020-11-09">2020</span> <i title="Public Library of Science (PLoS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ch57atmlprauhhbqdf7x4ytejm" style="color: black;">PLoS Computational Biology</a> </i> &nbsp;
Since ATAC-seq data plays a crucial role in the study of the effects of non-coding regions on the molecular classification of cancers, we explore the clustering solution obtained by ClusterATAC on the  ...  Still, the methods they used always do not directly support the high-dimensional omics data across the whole genome (Such as ATAC-seq profiles).  ...  Of all the approaches, only iCluster used multi-omics data instead of ATAC-seq data for the clustering, making it the farthest from other methods.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pcbi.1008405">doi:10.1371/journal.pcbi.1008405</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33166290">pmid:33166290</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/w767ufm33zaefdkwovryciwzvi">fatcat:w767ufm33zaefdkwovryciwzvi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201110112003/https://journals.plos.org/ploscompbiol/article/file?id=10.1371%2Fjournal.pcbi.1008405&amp;type=printable" 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/da/56/da5680a877c1a7e1cb0a74ca8f26edd0639538a0.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pcbi.1008405"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> plos.org </button> </a>

scPretrain: Multi-task self-supervised learning for cell type classification [article]

Ruiyi Zhang, Yunan Luo, Jianzhu Ma, Ming Zhang, Sheng Wang
<span title="2020-11-20">2020</span> <i title="Cold Spring Harbor Laboratory"> bioRxiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we proposed scPretrain, a multi-task self-supervised learning approach that jointly considers annotated and unannotated cells for cell type classification. scPretrain consists of a pre-training  ...  Cell type classification, which aims at characterizing and labeling groups of cells according to their gene expression, is one of the most important steps for single-cell analysis.  ...  In this paper, we proposed a novel multi-task self-supervised learning approach scPretrain for cell type classification.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/2020.11.18.386102">doi:10.1101/2020.11.18.386102</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/i7bkdsbeg5fv7maxahbddfvaei">fatcat:i7bkdsbeg5fv7maxahbddfvaei</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210429120840/https://www.biorxiv.org/content/biorxiv/early/2020/11/20/2020.11.18.386102.full.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/66/13/66133d9b62b87a7493fddce00aba115c12f270ff.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/2020.11.18.386102"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> biorxiv.org </button> </a>

Gene co-expression analysis for functional classification and gene–disease predictions

Sipko van Dam, Urmo Võsa, Adriaan van der Graaf, Lude Franke, João Pedro de Magalhães
<span title="2017-01-10">2017</span> <i title="Oxford University Press (OUP)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/op7ztx4fhvairowgqifu7dnvsi" style="color: black;">Briefings in Bioinformatics</a> </i> &nbsp;
Acknowledgements We thank the members of the Integrative Genomics of Ageing Group for useful suggestions and discussions.  ...  Finally, we thank the Institute of Integrative Biology at the University of Liverpool for supporting the PhD studies of Sipko van Dam.  ...  Several computational methods and publicly available data sets are available for multi-omics data integration.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/bib/bbw139">doi:10.1093/bib/bbw139</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/28077403">pmid:28077403</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6054162/">pmcid:PMC6054162</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/meqto5bjubd33egrudekf52y7e">fatcat:meqto5bjubd33egrudekf52y7e</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190427002633/https://pure.rug.nl/ws/files/62205197/Gene_co_expression_analysis_for_functional.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/6a/bf/6abf59a071541c0f0feb633400ce3afba142fbf5.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/bib/bbw139"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> oup.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6054162" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Classification and severity progression measure of COVID-19 patients using pairs of multi-omic factors

Teng Chen, Paweł Polak, Stanislav Uryasev
<span title="2022-05-16">2022</span> <i title="Taylor &amp; Francis"> figshare.com </i> &nbsp;
In a result, we achieve 96–98% AUC on the validation data.  ...  Our findings can help medical experts to identify small groups of biomarkers that after nonlinear transformation can be used to construct a cost-effective test for patient screening and prediction of severity  ...  The columns are: Pairs of Biomarkers = names of the two biomarkers in a given pair; Omics = P for proteomic biomarker, M for metabolomic biomarker, L for lipidomic biomarker, T for trascriptomic biomarker  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.6084/m9.figshare.19710628.v2">doi:10.6084/m9.figshare.19710628.v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/h24cvlbranfk5hts2dyc4523dm">fatcat:h24cvlbranfk5hts2dyc4523dm</a> </span>
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A Harmonized Atlas of Spinal Cord Cell Types and Their Computational Classification [article]

Daniel E. Russ, Ryan B. Patterson Cross, Li Li, Stephanie C. Koch, Kaya J.E. Matson, Ariel Levine
<span title="2020-09-04">2020</span> <i title="Cold Spring Harbor Laboratory"> bioRxiv </i> &nbsp; <span class="release-stage" >pre-print</span>
computational classification of spinal cord cell types based on transcriptomic data.  ...  This work provides a comprehensive resource with unprecedented resolution of spinal cord cell types and charts a path forward for how to utilize transcriptomic data to expand our knowledge of spinal cord  ...  These results establish a two-tiered model based on label transfer and a neural network as an effective approach for the computational classification of single cell sequencing data, even in the context  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/2020.09.03.241760">doi:10.1101/2020.09.03.241760</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/q7ponewf7vgphnndiqn4ucizna">fatcat:q7ponewf7vgphnndiqn4ucizna</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201209200727/https://www.biorxiv.org/content/biorxiv/early/2020/09/04/2020.09.03.241760.full.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/06/3d/063d3a14beb83e37f7ce757137b4dab64f23e97d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/2020.09.03.241760"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> biorxiv.org </button> </a>
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