An information theoretic approach to pedigree reconstruction
Author
Abstract
Suggested Citation
DOI: 10.1016/j.tpb.2015.09.006
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Matthieu Vignes & Jimmy Vandel & David Allouche & Nidal Ramadan-Alban & Christine Cierco-Ayrolles & Thomas Schiex & Brigitte Mangin & Simon de Givry, 2011. "Gene Regulatory Network Reconstruction Using Bayesian Networks, the Dantzig Selector, the Lasso and Their Meta-Analysis," PLOS ONE, Public Library of Science, vol. 6(12), pages 1-15, December.
- Ellis, Byron & Wong, Wing Hung, 2008. "Learning Causal Bayesian Network Structures From Experimental Data," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 778-789, June.
- Scutari, Marco, 2010. "Learning Bayesian Networks with the bnlearn R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i03).
- Almudevar, Anthony & LaCombe, Jason, 2012. "On the choice of prior density for the Bayesian analysis of pedigree structure," Theoretical Population Biology, Elsevier, vol. 81(2), pages 131-143.
- Sheehan, Nuala A. & Bartlett, Mark & Cussens, James, 2014. "Improved maximum likelihood reconstruction of complex multi-generational pedigrees," Theoretical Population Biology, Elsevier, vol. 97(C), pages 11-19.
- Cowell, Robert G., 2009. "Efficient maximum likelihood pedigree reconstruction," Theoretical Population Biology, Elsevier, vol. 76(4), pages 285-291.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Anderson, Eric C. & Ng, Thomas C., 2016. "Bayesian pedigree inference with small numbers of single nucleotide polymorphisms via a factor-graph representation," Theoretical Population Biology, Elsevier, vol. 107(C), pages 39-51.
- Cowell, Robert G., 2013. "A simple greedy algorithm for reconstructing pedigrees," Theoretical Population Biology, Elsevier, vol. 83(C), pages 55-63.
- Sun, M. & Jobling, M.A. & Taliun, D. & Pramstaller, P.P. & Egeland, T. & Sheehan, N.A., 2016. "On the use of dense SNP marker data for the identification of distant relative pairs," Theoretical Population Biology, Elsevier, vol. 107(C), pages 14-25.
- Almudevar, Anthony & LaCombe, Jason, 2012. "On the choice of prior density for the Bayesian analysis of pedigree structure," Theoretical Population Biology, Elsevier, vol. 81(2), pages 131-143.
- Prabal Das & D. A. Sachindra & Kironmala Chanda, 2022. "Machine Learning-Based Rainfall Forecasting with Multiple Non-Linear Feature Selection Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 6043-6071, December.
- Vuong, Quan-Hoang & La, Viet-Phuong, 2019. "The bayesvl R package. User guide v0.8.1," OSF Preprints w5dx6, Center for Open Science.
- F. Cugnata & G. Perucca & S. Salini, 2017. "Bayesian networks and the assessment of universities' value added," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(10), pages 1785-1806, July.
- Roland R. Ramsahai, 2020. "Connecting actuarial judgment to probabilistic learning techniques with graph theory," Papers 2007.15475, arXiv.org.
- Tang, Kayu & Parsons, David J. & Jude, Simon, 2019. "Comparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution system," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 24-36.
- Myriam Patricia Cifuentes & Clara Mercedes Suarez & Ricardo Cifuentes & Noel Malod-Dognin & Sam Windels & Jose Fernando Valderrama & Paul D. Juarez & R. Burciaga Valdez & Cynthia Colen & Charles Phill, 2022. "Big Data to Knowledge Analytics Reveals the Zika Virus Epidemic as Only One of Multiple Factors Contributing to a Year-Over-Year 28-Fold Increase in Microcephaly Incidence," IJERPH, MDPI, vol. 19(15), pages 1-21, July.
- Silvia de Juan & Maria Dulce Subida & Andres Ospina-Alvarez & Ainara Aguilar & Miriam Fernandez, 2020. "Disentangling the socio-ecological drivers behind illegal fishing in a small-scale fishery managed by a TURF system," Papers 2012.08970, arXiv.org.
- Meineri, Eric & Dahlberg, C. Johan & Hylander, Kristoffer, 2015. "Using Gaussian Bayesian Networks to disentangle direct and indirect associations between landscape physiography, environmental variables and species distribution," Ecological Modelling, Elsevier, vol. 313(C), pages 127-136.
- Michail Tsagris, 2021. "A New Scalable Bayesian Network Learning Algorithm with Applications to Economics," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 341-367, January.
- Michael J. Brusco & Douglas Steinley & Ashley L. Watts, 2022. "Disentangling relationships in symptom networks using matrix permutation methods," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 133-155, March.
- Sangsung Park & Sunghae Jun, 2020. "Patent Keyword Analysis of Disaster Artificial Intelligence Using Bayesian Network Modeling and Factor Analysis," Sustainability, MDPI, vol. 12(2), pages 1-11, January.
- Federica Cugnata & Silvia Salini & Elena Siletti, 2021. "Deepening Well-Being Evaluation with Different Data Sources: A Bayesian Networks Approach," IJERPH, MDPI, vol. 18(15), pages 1-10, July.
- Bibartiu, Otto & Dürr, Frank & Rothermel, Kurt & Ottenwälder, Beate & Grau, Andreas, 2021. "Scalable k-out-of-n models for dependability analysis with Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
- Lingfei Wang, 2021. "Single-cell normalization and association testing unifying CRISPR screen and gene co-expression analyses with Normalisr," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
- Bruce G. Marcot & Anca M. Hanea, 2021. "What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis?," Computational Statistics, Springer, vol. 36(3), pages 2009-2031, September.
- Ryan G. Lim & Osama Al-Dalahmah & Jie Wu & Maxwell P. Gold & Jack C. Reidling & Guomei Tang & Miriam Adam & David K. Dansu & Hye-Jin Park & Patrizia Casaccia & Ricardo Miramontes & Andrea M. Reyes-Ort, 2022. "Huntington disease oligodendrocyte maturation deficits revealed by single-nucleus RNAseq are rescued by thiamine-biotin supplementation," Nature Communications, Nature, vol. 13(1), pages 1-23, December.
More about this item
Keywords
Pedigree reconstruction; Graphical models; Minimum Description Length principle; Bayesian inference;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:thpobi:v:107:y:2016:i:c:p:52-64. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/intelligence .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.