Deep learning pose estimation for multi-cattle lameness detection

Autor: Shaun Barney, Satnam Dlay, Andrew Crowe, Ilias Kyriazakis, Matthew Leach

Publikováno v: Barney, S, Dlay, S, Crowe, A, Kyriazakis, I & Leach, M 2023, ' Deep learning pose estimation for multi-cattle lameness detection ', Scientific Reports, vol. 13, no. 1, 4499 . https://doi.org/10.1038/s41598-023-31297-1

The objective of this study was to develop a fully automated multiple-cow real-time lameness detection system using a deep learning approach for cattle detection and pose estimation that could be deployed across dairy farms. Utilising computer vision

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9906e10d529e59a4e567c88b159d56dc
https://pure.qub.ac.uk/en/publications/809e5e8d-d5d3-48d8-8210-e16f67b7ad3d

Developing ovine mammary terminal duct lobular units have a dynamic mucosal and stromal immune microenvironment

Autor: Katie L. Davies, Katherine Hughes, Clare M. C. Gillis, Abigail L. Fowden, Paul Rees, John W. Wills, Dorottya Nagy

Publikováno v: Communications Biology, Vol 4, Iss 1, Pp 1-11 (2021)
Communications Biology

Funder: University of Cambridge | Girton College, University of Cambridge (Girton); doi: https://doi.org/10.13039/501100000621
Funder: University of Cambridge Herchel-Smith Fund
Funder: British Veterinary Association Animal Welfare Foundati

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5f69be3568e3f17aa71ef9983eb84db8
https://doaj.org/article/2ae7a95b13c14b32b8557c957d71bdaf

Application of information theoretic feature selection and machine learning methods for the development of genetic risk prediction models

Autor: Jalali-Najafabadi, Farideh, Stadler, Michael, Dand, Nick, Jadon, Deepak, Soomro, Mehreen, Ho, Pauline, Marzo-Ortega, Helen, Helliwell, Philip, Korendowych, Eleanor, Simpson, Michael A, Packham, Jonathan, Smith, Catherine H, Barker, Jonathan N, McHugh, Neil, Warren, Richard B, Barton, Anne, Bowes, John, BADBIR Study Group, BSTOP Study Group

Publikováno v: Scientific Reports
Scientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
BADBIR Study Group 2021, ' Application of information theoretic feature selection and machine learning methods for the development of genetic risk prediction models ', Scientific Reports, vol. 11, no. 1, 23335 . https://doi.org/10.1038/s41598-021-00854-x

In view of the growth of clinical risk prediction models using genetic data, there is an increasing need for studies that use appropriate methods to select the optimum number of features from a large number of genetic variants with a high degree of r

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::affbe1177f917391e44295ba70d6ca49

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

Autor: Menden M, Wang D, Mason M, Szalai B, Bulusu K, Guan Y, Yu T, Kang J, Jeon M, Wolfinger R, Nguyen T, Zaslavskiy M, Jang I, Ghazoui Z, Ahsen M, Vogel R, Neto E, Norman T, Tang E, Garnett M, Di Veroli G, Fawell S, Stolovitzky G, Guinney J, Dry J, Saez-Rodriguez J, Abante J, Abecassis B, Aben N, Aghamirzaie D, Aittokallio T, Akhtari F, Al-lazikani B, Alam T, Allam A, Allen C, de Almeida M, Altarawy D, Alves V, Amadoz A, Anchang B, Antolin A, Ash J, Aznar V, Ba-alawi W, Bagheri M, Bajic V, Ball G, Ballester P, Baptista D, Bare C, Bateson M, Bender A, Bertrand D, Wijayawardena B, Boroevich K, Bosdriesz E, Bougouffa S, Bounova G, Brouwer T, Bryant B, Calaza M, Calderone A, Calza S, Capuzzi S, Carbonell-Caballero J, Carlin D, Carter H, Castagnoli L, Celebi R, Cesareni G, Chang H, Chen G, Chen H, Cheng L, Chernomoretz A, Chicco D, Cho K, Cho S, Choi D, Choi J, Choi K, Choi M, De Cock M, Coker E, Cortes-Ciriano I, Cserzo M, Cubuk C, Curtis C, Van Daele D, Dang C, Dijkstra T, Dopazo J, Draghici S, Drosou A, Dumontier M, Ehrhart F, Eid F, ElHefnawi M, Elmarakeby H, van Engelen B, Engin H, de Esch I, Evelo C, Falcao A, Farag S, Fernandez-Lozano C, Fisch K, Flobak A, Fornari C, Foroushani A, Fotso D, Fourches D, Friend S, Frigessi A, Gao F, Gao X, Gerold J, Gestraud P, Ghosh S, Gillberg J, Godoy-Lorite A, Godynyuk L, Godzik A, Goldenberg A, Gomez-Cabrero D, Gonen M, de Graaf C, Gray H, Grechkin M, Guimera R, Guney E, Haibe-Kains B, Han Y, Hase T, He D, He L, Heath L, Hellton K, Helmer-Citterich M, Hidalgo M, Hidru D, Hill S, Hochreiter S, Hong S, Hovig E, Hsueh Y, Hu Z, Huang J, Huang R, Hunyady L, Hwang J, Hwang T, Hwang W, Hwang Y, Isayev O, Walk O, Jack J, Jahandideh S, Ji J, Jo Y, Kamola P, Kanev G, Karacosta L, Karimi M, Kaski S, Kazanov M, Khamis A, Khan S, Kiani N, Kim A, Kim J, Kim K, Kim S, Kim Y, Kirk P, Kitano H, Klambauer G, Knowles D, Ko M, Kohn-Luque A, Kooistra A, Kuenemann M, Kuiper M, Kurz C, Kwon M, van Laarhoven T, Laegreid A, Lederer S, Lee H, Lee J, Lee Y, Leppaho E, Lewis R, Li J, Li L, Liley J, Lim W, Lin C, Liu Y, Lopez Y, Low J, Lysenko A, Machado D, Madhukar N, De Maeyer D, Malpartida A, Mamitsuka H, Marabita F, Marchal K, Marttinen P, Mason D, Mazaheri A, Mehmood A, Mehreen A, Michaut M, Miller R, Mitsopoulos C, Modos D, Van Moerbeke M, Moo K, Motsinger-Reif A, Movva R, Muraru S, Muratov E, Mushthofa M, Nagarajan N, Nakken S, Nath A, Neuvial P, Newton R, Ning Z, De Niz C, Oliva B, Olsen C, Palmeri A, Panesar B, Papadopoulos S, Park J, Park S, Pawitan Y, Peluso D, Pendyala S, Peng J, Perfetto L, Pirro S, Plevritis S, Politi R, Poon H, Porta E, Prellner I, Preuer K, Pujana M, Ramnarine R, Reid J, Reyal F, Richardson S, Ricketts C, Rieswijk L, Rocha M, Rodriguez-Gonzalvez C, Roell K, Rotroff D, de Ruiter J, Rukawa P, Sadacca B, Safikhani Z, Safitri F, Sales-Pardo M, Sauer S, Schlichting M, Seoane J, Serra J, Shang M, Sharma A, Sharma H, Shen Y, Shiga M, Shin M, Shkedy Z, Shopsowitz K, Sinai S, Skola D, Smirnov P, Soerensen I, Soerensen P, Song J, Song S, Soufan O, Spitzmueller A, Steipe B, Suphavilai C, Tamayo S, Tamborero D, Tang J, Tanoli Z, Tarres-Deulofeu M, Tegner J, Thommesen L, Tonekaboni S, Tran H, De Troyer E, Truong A, Tsunoda T, Turu G, Tzeng G, Verbeke L, Videla S, Vis D, Voronkov A, Votis K, Wang A, Wang H, Wang P, Wang S, Wang W, Wang X, Wennerberg K, Wernisch L, Wessels L, van Westen G, Westerman B, White S, Willighagen E, Wurdinger T, Xie L, Xie S, Xu H, Yadav B, Yau C, Yeerna H, Yin J, Yu M, Yun S, Zakharov A, Zamichos A, Zanin M, Zeng L, Zenil H, Zhang F, Zhang P, Zhang W, Zhao H, Zhao L, Zheng W, Zoufir A, Zucknick M, AstraZeneca-Sanger Drug Combinatio

Publikováno v: Nature Communications, Vol 10, Iss 1, Pp 1-17 (2019)
Nature Communications
Nature Communications, 10, 2674
r-IGTP. Repositorio Institucional de Producción Científica del Instituto de Investigación Germans Trias i Pujol
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Nature Communications, 10, pp. 1-17
Nature Communications 10, 2674 (2019). doi:10.1038/s41467-019-09799-2
Dipòsit Digital de la UB
Universidad de Barcelona
Nature communications, 10 (1
AstraZeneca-Sanger Drug Combination DREAM Consortium 2019, ' Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen ', Nature Communications, vol. 10, no. 1, 2674 . https://doi.org/10.1038/s41467-019-09799-2
Nature Communications, 10:2674. Nature Publishing Group
NATURE COMMUNICATIONS
Nature Communications, 10, 1-17
Nature communications, vol 10, iss 1
Menden, M P, Wang, D, Mason, M J, Szalai, B, Bulusu, K C, Guan, Y, Yu, T, Kang, J, Jeon, M, Wolfinger, R, Nguyen, T, Zaslavskiy, M, AstraZeneca-Sanger Drug Combination DREAM Consortium, Jang, I S, Ghazoui, Z, Ahsen, M E, Vogel, R, Neto, E C, Norman, T, Tang, E K Y, Garnett, M J, Veroli, G Y D, Fawell, S, Stolovitzky, G, Guinney, J, Dry, J R & Saez-Rodriguez, J 2019, ' Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen ', Nature Communications, vol. 10, no. 1, 2674 . https://doi.org/10.1038/s41467-019-09799-2
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Repositório Científico de Acesso Aberto de Portugal
Repositório Científico de Acesso Aberto de Portugal (RCAAP)
instacron:RCAAP
Nature Communications, 10(1):2674. Nature Publishing Group
Recercat. Dipósit de la Recerca de Catalunya

PubMed: 31209238
The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1827ac4799243e82b5025526f2e5a77a
http://link.springer.com/article/10.1038/s41467-019-09799-2

Genome-wide investigation of gene-cancer associations for the prediction of novel therapeutic targets in oncology

Autor: Hendrik Weisser, Adrián Bazaga, Dan Leggate

Publikováno v: Scientific Reports
Scientific Reports, Vol 10, Iss 1, Pp 1-10 (2020)

A major cause of failed drug discovery programs is suboptimal target selection, resulting in the development of drug candidates that are potent inhibitors, but ineffective at treating the disease. In the genomics era, the availability of large biomed

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0d0f0a13eb8277afb90af9dcc86e4de2
http://europepmc.org/articles/PMC7330039