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Hard and soft EM in bayesian network learning from incomplete data

Ruggieri, Andrea ; Stranieri, Francesco ; Stella, Fabio ; Scutari, Marco

In: Algorithms, 2020, vol. 13, no. 12, p. 17

Incomplete data are a common feature in many domains, from clinical trials to industrial applications. Bayesian networks (BNs) are often used in these domains because of their graphical and causal interpretations. BN parameter learning from incomplete data is usually implemented with the Expectation-Maximisation algorithm (EM), which computes the relevant sufficient statistics (“soft EM”) ...

Probabilistic models with deep neural networks

Masegosa, Andrés R. ; Cabañas, Rafael ; Langseth, Helge ; Nielsen, Thomas D. ; Salmerón, Antonio

In: Entropy, 2021, vol. 23, no. 1, p. 27 p

Recent advances in statistical inference have significantly expanded the toolbox of probabilistic modeling. Historically, probabilistic modeling has been constrained to very restricted model classes, where exact or approximate probabilistic inference is feasible. However, developments in variational inference, a general form of approximate probabilistic inference that originated in statistical...

Changes in autopsy rates among cancer patients and their impact on cancer statistics from a public health point of view: a longitudinal study from 1980 to 2010 with data from Cancer Registry Zurich

Bieri, Uwe ; Moch, Holger ; Dehler, Silvia ; Korol, Dimitri ; Rohrmann, Sabine

In: Virchows Archiv, 2015, vol. 466, no. 6, p. 637-643

Taming the BEAST—A Community Teaching Material Resource for BEAST 2

Barido-Sottani, Joëlle ; Bošková, Veronika ; Plessis, Louis Du ; Kühnert, Denise ; Magnus, Carsten ; Mitov, Venelin ; Müller, Nicola F. ; PečErska, Jūlija ; Rasmussen, David A. ; Zhang, Chi ; Drummond, Alexei J. ; Heath, Tracy A. ; Pybus, Oliver G. ; Vaughan, Timothy G. ; Stadler, Tanja

In: Systematic Biology, 2018, vol. 67, no. 1, p. 170-174

TiMEx: a waiting time model for mutually exclusive cancer alterations

Constantinescu, Simona ; Szczurek, Ewa ; Mohammadi, Pejman ; Rahnenführer, Jörg ; Beerenwinkel, Niko

In: Bioinformatics, 2016, vol. 32, no. 7, p. 968-975

Network-based integration of multi-omics data for prioritizing cancer genes

Dimitrakopoulos, Christos ; Hindupur, Sravanth Kumar ; Häfliger, Luca ; Behr, Jonas ; Montazeri, Hesam ; Hall, Michael N. ; Beerenwinkel, Niko ; Birol, Inanc ; Birol, Inanc

In: Bioinformatics, 2018, vol. 34, no. 14, p. 2441-2448

NGS-pipe: a flexible, easily extendable and highly configurable framework for NGS analysis

Singer, Jochen ; Ruscheweyh, Hans-Joachim ; Hofmann, Ariane L. ; Thurnherr, Thomas ; Singer, Franziska ; Toussaint, Nora C. ; Ng, Charlotte K Y. ; Piscuoglio, Salvatore ; Beisel, Christian ; Christofori, Gerhard ; Dummer, Reinhard ; Hall, Michael N. ; Krek, Wilhelm ; Levesque, Mitchell P. ; Manz, Markus G. ; Moch, Holger ; Papassotiropoulos, Andreas ; Stekhoven, Daniel J. ; Wild, Peter ; Wüst, Thomas ; Rinn, Bernd ; Beerenwinkel, Niko ; Berger, Bonnie ; Berger, Bonnie

In: Bioinformatics, 2018, vol. 34, no. 1, p. 107-108

Improved pathway reconstruction from RNA interference screens by exploiting off-target effects

Srivatsa, Sumana ; Kuipers, Jack ; Schmich, Fabian ; Eicher, Simone ; Emmenlauer, Mario ; Dehio, Christoph ; Beerenwinkel, Niko

In: Bioinformatics, 2018, vol. 34, no. 13, p. i519-i527

Efficient computational strategies for doubly intractable problems with applications to Bayesian social networks

Caimo, Alberto ; Mira, Antonietta

In: Statistics and Computing, 2015, vol. 25, no. 1, p. 113-125

Synthetic biology: applying biological circuits beyond novel therapies

Dobrin, Anton ; Saxena, Pratik ; Fussenegger, Martin

In: Integrative Biology, 2016, vol. 8, no. 4, p. 409-430