SIMON: Open-Source Knowledge Discovery Platform.
Adriana Tomic,Adriana Tomic,Ivan Tomic,Levi Waldron,Ludwig Geistlinger,Max Kuhn,Rachel L. Spreng,Lindsay C. Dahora,Kelly E. Seaton,Georgia D. Tomaras,Jennifer Hill,Niharika A. Duggal,Ross D. Pollock,Norman R. Lazarus,Stephen D. R. Harridge,Janet M. Lord,Janet M. Lord,Purvesh Khatri,Andrew J. Pollard,Mark M. Davis,Mark M. Davis +20 more
- 08 Jan 2021
- Vol. 2, Iss: 1, pp 100178-100178
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TL;DR: SIMON as mentioned in this paper is a modular open-source software to facilitate the application of 180+ state-of-the-art machine learning algorithms to high-dimensional biomedical data, with an easy-to-use graphical user interface, standardized pipelines, and automated approach for machine learning and other statistical analysis methods, SIMON helps to identify optimal algorithms and provides a resource that empowers non-technical and technical researchers to identify crucial patterns in biomedical data.
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Abstract: Data analysis and knowledge discovery has become more and more important in biology and medicine with the increasing complexity of biological datasets, but the necessarily sophisticated programming skills and in-depth understanding of algorithms needed pose barriers to most biologists and clinicians to perform such research. We have developed a modular open-source software, SIMON, to facilitate the application of 180+ state-of-the-art machine-learning algorithms to high-dimensional biomedical data. With an easy-to-use graphical user interface, standardized pipelines, and automated approach for machine learning and other statistical analysis methods, SIMON helps to identify optimal algorithms and provides a resource that empowers non-technical and technical researchers to identify crucial patterns in biomedical data.
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A blood atlas of COVID-19 defines hallmarks of disease severity and specificity
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TL;DR: The immuneML (immuneml.uioio.no) project as discussed by the authors is an open-source software ecosystem that is based on fully specified and shareable workflows.
ProPythia: A Python package for protein classification based on machine and deep learning
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TL;DR: ProPythia as discussed by the authors is a generic and modular Python package that allows to easily deploy ML and DL approaches for a plethora of problems in protein sequence analysis and classification, including read and alter sequences, calculate protein features, preprocess datasets, execute feature selection and dimensionality reduction, perform clustering and manifold analysis, as well as to train and optimize ML/DL models and use them to make predictions.
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A blood atlas of COVID-19 defines hallmarks of disease severity and specificity
David Ahern,Z Ai,Mark A. Ainsworth,Charlotte L. Allan,A Allcock,A Ansari,Carolina V. Arancibia-Cárcamo,Dominik Aschenbrenner,Moustafa Attar,J K Baillie,E Barnes,Rachael Bashford-Rogers,A. Bashyal,Sally Beer,Georgina Berridge,A Beveridge,S Bibi,Tihana Bicanic,L Blackwell,Paul Bowness,Andrew Brent,Anthony Brown,John Broxholme,David Buck,Katie L. Burnham,Helen M. Byrne,S Camara,I Candido Ferreira,Philip D. Charles,W Chen,Chen Y-L.,Amanda Y. Chong,Elizabeth A. Clutterbuck,Mark Coles,Christopher P. Conlon,Richard J. Cornall,Adam P. Cribbs,F Curion,Emma E. Davenport,N Davidson,Simon J. Davis,Calliope A. Dendrou,J Dequaire,L Dib,J Docker,Christina Dold,Tao Dong,Damien J. Downes,A Drakesmith,Susanna Dunachie,David A. Duncan,C Eijsbouts,R Esnouf,A Espinosa,R Etherington,Benjamin P. Fairfax,Rory Fairhead,H Fang,S Fassih,S Felle,M Fernandez Mendoza,Ricardo C. Ferreira,Roman Fischer,T H Foord,Aden Forrow,John Frater,Anastasia Fries,V Gallardo Sanchez,L Garner,C Geeves,D Georgiou,Laura Godfrey,Tanya Golubchik,M Gomez Vazquez,Angie Green,H Harper,Heather A. Harrington,Raphael Heilig,Svenja Hester,Jennifer Hill,Charles J. Hinds,C Hird,Ho L-P.,Renee S. Hoekzema,B Hollis,Jim R. Hughes,P Hutton,M. Jackson,Ashwin Kumar Jainarayanan,A James-Bott,Kathrin Jansen,Katie Jeffery,E Y Jones,Luke Jostins,G Kerr,David Y. Kim,Paul Klenerman,Julian C. Knight,V Kumar,P. Kumar Sharma,P Kurupati,Andrew J Kwok,Andy C. H. Lee,A Linder,T Lockett,Lorne Lonie,Maria Lopopolo,Martyna Lukoseviciute,J Luo,S Marinou,Brian D. Marsden,Jose Vicente Martinez,Paul M. Matthews,M Mazurczyk,Simon J. McGowan,Stuart McKechnie,Adam J. Mead,Alexander J. Mentzer,Y Mi,Claudia Monaco,R Montadon,Giorgio Napolitani,Isar Nassiri,Alex Novak,D O'Brien,Daniel H. O'Connor,Denise O'Donnell,Graham S. Ogg,L E Overend,I Park,Ian D. Pavord,Y Peng,F Penkava,M Pereira Pinho,E Perez,Andrew J. Pollard,Fiona Powrie,B Psaila,T P Quan,Emmanouela Repapi,S Revale,L Silva-Reyes,Richard J-B.,Charlotte Rich-Griffin,Thomas G Ritter,Christine S. Rollier,Matthew J. Rowland,Fabian Ruehle,Mariolina Salio,Stephen N. Sansom,A Santos Delgado,Tatjana Sauka-Spengler,Ron Schwessinger,G Scozzafava,Gavin R. Screaton,Anna Seigal,Malcom G Semple,Martin J. Sergeant,C Simoglou Karali,David Sims,Donal T. Skelly,Hubert Sławiński,A Sobrinodiaz,N Sousos,Lizzie Stafford,Lisa Stockdale,M Strickland,O Sumray,B Sun,Chelsea A Taylor,Stephen S. Taylor,Aimee R. Taylor,Supat Thongjuea,H Thraves,John A. Todd,Adriana Tomic,O Tong,Amy Trebes,Dominik Trzupek,F A Tucci,Lance Turtle,Irina A. Udalova,Holm H. Uhlig,E van Grinsven,Iolanda Vendrell,M Verheul,A Voda,G Wang,Linwei Wang,D Wang,Peter J. Watkinson,Robert A. Watson,Michael Weinberger,Justin P. Whalley,Lorna Witty,K Wray,L Xue,H Y Yeung,Z Yin,R K Young,Jonathan Youngs,P Zhang,Zurke Y-X. +202 more
TL;DR: In this article, a comprehensive multi-omic blood atlas was presented in patients with varying COVID-19 severity and compare with influenza, sepsis and healthy volunteers, identifying immune signatures and correlates of host response.
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