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Improved estimation of bovine weight trajectories using Support Vector Machine Classification

Jaime Alonso, Alfonso Villa, Antonio Bahamonde
2015 Computers and Electronics in Agriculture  
The benefits of livestock breeders are usually closely related to the weight of their animals.  ...  In this paper we present a method to anticipate the weight of each animal provided we know the past evolution of the herd. Our approach  ...  The authors would like to acknowledge the collaboration of the experts of the Association of Breeders (ASEAVA) during the acquisition of data stage.  ... 
doi:10.1016/j.compag.2014.10.001 fatcat:cd6zmzkgc5as3oox7iqz7fsqr4

Prediction of disulfide bonding pattern based on a support vector machine and multiple trajectory search

Hsuan-Hung Lin, Lin-Yu Tseng
2012 Information Sciences  
The support vector machine (SVM) is trained to compute the connectivity probabilities of cysteine pairs.  ...  The maximum weight perfect matching algorithm is then used to find the disulfide connectivity pattern.  ...  Support vector machine Support vector machine (SVM) is a supervised learning method used for classification [21] .  ... 
doi:10.1016/j.ins.2012.02.035 fatcat:lqagrewsgzgq3ogtqweuz2nyoq

Morphological assessment of beef cattle according to carcass value

Jaime Alonso, Antonio Bahamonde, Alfonso Villa, Ángel Rodríguez Castañón
2007 Livestock Science  
To derive this assessment, we used Artificial Intelligence tools based on Support Vector Machines (SVM).  ...  They allowed us to learn the estimations of the experts of the Association of Breeders (ASEAVA) about the value of carcasses of the animals in the sense that the score returned by the assessment function  ...  Acknowledgements The research reported in this paper is supported in part under the grant TIN2005-08288 from the Spanish Ministerio de Educación y Ciencia.  ... 
doi:10.1016/j.livsci.2006.09.027 fatcat:4to45degl5fgnen43tfffio2lq

The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016)

Shaoqing Wang, Xiancun Yang, Meixia Su, Qiang Liu, Tao Gong, Qi Mao, Shuguang Zhao, Fang Han, Keming Mao, Yixian Liu, Yanchun Zhu, Shuo Li (+149 others)
2016 BMC Medical Imaging  
There were significant differences in radiation dose and dosage of contrast agent (P < 0.05) between the two methods of using 3D-RA can improve the detection rate of micro-aneurysms, which bestows obvious  ...  Results The X-ray medical image of the tuberculosis was processed with the improved clonal selection algorithm and noise filtering, and the output medical image of our approach is better for diagnosis  ...  in MRI scanning based on wavelet entropy and kernel support vector machine trained by sequential minimal optimization Shuihua Wang 1,2,3 , Sidan Du 4 , Zhimin Chen 5,6 , Preetha Phillips 7,8 , Shuwen  ... 
doi:10.1186/s12880-016-0164-6 fatcat:cjwshnwiere6ziu5zc7sjic3xu

Machine Learning in Agriculture: A Review

Konstantinos Liakos, Patrizia Busato, Dimitrios Moshou, Simon Pearson, Dionysis Bochtis
2018 Sensors  
The filtering and classification of the presented articles demonstrate how agriculture will benefit from machine learning technologies.  ...  support and action.  ...  Based on SVM methods [103] , a method for the accurate estimation of bovine weight trajectories over time was presented. The accurate estimation of cattle weights is very important for breeders.  ... 
doi:10.3390/s18082674 pmid:30110960 fatcat:mc44hp67fbfrviogramffyasla

Regression plane concept for analysing continuous cellular processes with machine learning

Abel Szkalisity, Filippo Piccinini, Attila Beleon, Tamas Balassa, Istvan Gergely Varga, Ede Migh, Csaba Molnar, Lassi Paavolainen, Sanna Timonen, Indranil Banerjee, Elina Ikonen, Yohei Yamauchi (+5 others)
2021 Nature Communications  
Using multi-parametric active regression we introduce the Regression Plane (RP), a user-friendly discovery tool enabling class-free phenotypic supervised machine learning, to describe and explore biological  ...  First, we compare traditional classification with regression in a simulated experimental setup.  ...  A.Sz. and E.I. acknowledges support from University of Helsinki (Centre of Excellence matching funds) and Academy of Finland (project 324929).  ... 
doi:10.1038/s41467-021-22866-x pmid:33953203 fatcat:64pyorb4wfgvzfgcwufddtftfu

Extracting Diffusive States of Rho GTPase in Live Cells: Towards In Vivo Biochemistry

Peter K. Koo, Matthew Weitzman, Chandran R. Sabanaygam, Kenneth L. van Golen, Simon G. J. Mochrie, Gilad Haran
2015 PLoS Computational Biology  
Here, we introduce a novel, machine-learning based classification methodology, which we call perturbation expectation-maximization (pEM), that simultaneously analyzes a population of protein trajectories  ...  Here, we introduce a novel, unsupervised, machine-learning based classification methodology, which we call perturbation expectation-maximization (pEM), that simultaneously analyzes a PLOS Computational  ...  Acknowledgments We thank the Delaware Bioimaging Center for their support and guidance. Author Contributions  ... 
doi:10.1371/journal.pcbi.1004297 pmid:26512894 pmcid:PMC4626024 fatcat:llkb433qlvhonixgaeh566ybju

Regression plane concept: analysing continuous cellular processes with machine learning [article]

Szkalisity Abel, Filippo Piccinini, Attila Beleon, Tamas Balassa, Istvan Gergely Varga, Ede Migh, Lassi Paavolainen, Sanna Timonen, Indranil Banerjee, Yohei Yamauchi, Istvan Ando, Jaakko Peltonen (+3 others)
2020 biorxiv/medrxiv   pre-print
Biological processes are inherently continuous, and the chance of phenotypic discovery is significantly restricted by discretising them.  ...  Using multi-parametric active regression we introduce a novel concept to describe and explore biological data in a continuous manner.  ...  We assessed the performance of the proposed active learning methods with 4 regression models: Random Forest, Gaussian Process, Neural Network and Support Vector Machine; on 2 of our datasets: Lipid droplets  ... 
doi:10.1101/2020.09.01.276089 fatcat:dp4f6sh66zepbdbkkpjig5rbm4

Visual Localisation and Individual Identification of Holstein Friesian Cattle via Deep Learning

William Andrew, Colin Greatwood, Tilo Burghardt
2017 2017 IEEE International Conference on Computer Vision Workshops (ICCVW)  
We thank the University of Bristol Veterinary Sciences School, particularly Dr Becky Whay and Prof Mike Mendl, for permitting project data capture.  ...  Acknowledgements: this work was supported by the EPSRC Centre for Doctoral Training in Future Autonomous and Robotic Systems (FARSCOPE) at the Bristol Robotics Laboratory.  ...  Vector Machine (RBF-SVM).  ... 
doi:10.1109/iccvw.2017.336 dblp:conf/iccvw/AndrewGB17 fatcat:fynpaedybbh2lp7bwzku22b6nu

Single-cell RNA-seq of human induced pluripotent stem cells reveals cellular heterogeneity and cell state transitions between subpopulations

Quan H. Nguyen, Samuel W. Lukowski, Han Sheng Chiu, Anne Senabouth, Timothy J.C. Bruxner, Angelika N. Christ, Nathan J. Palpant, Joseph E. Powell
2018 Genome Research  
Our method identified four transcriptionally distinct predictor gene sets composed of 165 unique genes that denote the specific pluripotency states; using these sets, we developed a multigenic machine  ...  Finally, we developed an innovative method to predict cells transitioning between subpopulations and support our conclusions with results from two orthogonal pseudotime trajectory methods.  ...  This work was supported by the Australian National Health and Medical Research Council (NHMRC) grants APP1083405 and APP1107599.  ... 
doi:10.1101/gr.223925.117 pmid:29752298 fatcat:tdxu5l3j5ncijkbdv7gi4u3kaa

Aging induces aberrant state transition kinetics in murine muscle stem cells

Jacob C. Kimmel, Ara B. Hwang, Annarita Scaramozza, Wallace F. Marshall, Andrew S. Brack
2020 Development  
These results support a view of the aged stem cell phenotype as a combination of differences in the location of stable cell states and differences in transition rates between them.  ...  We find that the activation trajectory is conserved in aged cells, and develop effective machine learning classifiers for cell age.  ...  Acknowledgements The authors thank DeLaine Larson and Kurt Thorn from the UCSF Nikon Imaging Center for helpful advice and technical support.  ... 
doi:10.1242/dev.183855 pmid:32198156 pmcid:PMC7225128 fatcat:lbhq3bzl3fc2rimcupn4xrqnyi

Economic complexity unfolded: Interpretable model for the productive structure of economies

Zoran Utkovski, Melanie F. Pradier, Viktor Stojkoski, Fernando Perez-Cruz, Ljupco Kocarev, Alejandro Raul Hernandez Montoya
2018 PLoS ONE  
in a country's mix of products.  ...  Economic complexity reflects the amount of knowledge that is embedded in the productive structure of an economy.  ...  the year 1995; and iii) results estimated by using the Harmonized System (HS) rev. 1992 classification disaggregated to six digit level (see Section E of S1 Supporting Information for more details).  ... 
doi:10.1371/journal.pone.0200822 pmid:30086166 pmcid:PMC6080758 fatcat:g2vzsjrixbbxtgvkku7o73st5i

Robotic Ultrasound Scanning With Real-Time Image-Based Force Adjustment: Quick Response for Enabling Physical Distancing During the COVID-19 Pandemic

Mojtaba Akbari, Jay Carriere, Tyler Meyer, Ron Sloboda, Siraj Husain, Nawaid Usmani, Mahdi Tavakoli
2021 Frontiers in Robotics and AI  
The SVM was trained using bovine and porcine biological tissue, the system was then tested experimentally on plastisol phantom tissue.  ...  This US image feedback will be used to automatically adjust the US probe contact force based on the quality of the image frame.  ...  ACKNOWLEDGMENTS This research was supported by the Canada Foundation for Innovation (CFI), the Natural Sciences and Engineering Research Council (NSERC) of Canada, the Canadian Institutes of Health Research  ... 
doi:10.3389/frobt.2021.645424 pmid:33829043 pmcid:PMC8019797 fatcat:lyw6uuyufnhr7namd7okx5wawi

Multiple particle tracking detects changes in brain extracellular matrix structure and predicts neurodevelopmental age [article]

Michael McKenna, David Shackelford, Hugo Ferreira Pontes, Brendan K Ball, Torahito Gao, Elizabeth Nance
2020 bioRxiv   pre-print
Using the age-dependent dataset, we applied extreme gradient boosting (XGBoost) to generate models capable of classifying nanoparticle trajectories.  ...  Collectively, this work demonstrates the utility of MPT combined with machine learning for measuring changes in brain ECM structure and predicting associated complex features such as developmental age.  ...  Chad Curtis, who established the Python-based analysis pipeline that was used to track and quantify nanoparticle trajectories.  ... 
doi:10.1101/2020.04.20.050112 fatcat:qs6jcgsyrnfwtjdywolgjwktya

CellCycleTRACER accounts for cell cycle and volume in mass cytometry data

Maria Anna Rapsomaniki, Xiao-Kang Lun, Stefan Woerner, Marco Laumanns, Bernd Bodenmiller, María Rodríguez Martínez
2018 Nature Communications  
Acknowledgements We would like to thank the Bodenmiller laboratory for support and fruitful discussions,  ...  forests, LASSO, logistic regression, support vector machines, and latent variable models 26, 28, 30, 31 .  ...  from each class and computed an optimal set of weights.  ... 
doi:10.1038/s41467-018-03005-5 pmid:29434325 pmcid:PMC5809393 fatcat:vhucp2kw5beh3jdmtszbqbvjqy
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