Results 31 to 40 of about 151,404 (250)

Bayesian graphical models for computational network biology

BMC Bioinformatics, 2018
Background Computational network biology is an emerging interdisciplinary research area. Among many other network approaches, probabilistic graphical models provide a comprehensive probabilistic characterization of interaction patterns between molecules ...
Yang Ni, Peter Müller, Lin Wei, Yuan Ji
doaj   +1 more source

Modeling cumulative biological phenomena with Suppes-Bayes Causal Networks [PDF]

, 2018
Several diseases related to cell proliferation are characterized by the accumulation of somatic DNA changes, with respect to wildtype conditions. Cancer and HIV are two common examples of such diseases, where the mutational load in the cancerous/viral ...
Antoniotti, Marco, Caravagna, Giulio, Graudenzi, Alex, Ramazzotti, Daniele   +3 more
core   +2 more sources

Deep Probabilistic Graphical Modeling

, 2020
Probabilistic graphical modeling (PGM) provides a framework for formulating an interpretable generative process of data and expressing uncertainty about unknowns, but it lacks flexibility. Deep learning (DL) is an alternative framework for learning from data that has achieved great empirical success in recent years.
openaire   +3 more sources

Probabilistic Graphical Model Representation in Phylogenetics [PDF]

Systematic Biology, 2014
Recent years have seen a rapid expansion of the model space explored in statistical phylogenetics, emphasizing the need for new approaches to statistical model representation and software development. Clear communication and representation of the chosen model is crucial for: (1) reproducibility of an analysis, (2) model development and (3) software ...
Höhna, Sebastian, Heath, Tracy A., Boussau, Bastien, Landis, Michael J., Ronquist, Fredrik, Huelsenbeck, John P.   +5 more
openaire   +4 more sources

An Introduction to Probabilistic Graphical Models

Proceedings of the International Florida Artificial Intelligence Research Society Conference
This tutorial covers an introduction to Probabilistic Graphical Models (PGM), such as Bayesian Networks and Markov Random Fields, for reasoning under uncertainty in intelligent systems.
Luigi Portinale
doaj   +1 more source

Summarizing Complex Graphical Models of Multiple Chronic Conditions Using the Second Eigenvalue of Graph Laplacian: Algorithm Development and Validation

JMIR Medical Informatics, 2020
BackgroundIt is important but challenging to understand the interactions of multiple chronic conditions (MCC) and how they develop over time in patients and populations.
Faruqui, Syed Hasib Akhter, Alaeddini, Adel, Chang, Mike C, Shirinkam, Sara, Jaramillo, Carlos, NajafiRad, Peyman, Wang, Jing, Pugh, Mary Jo   +7 more
doaj   +1 more source

Modeling hepatic fibrosis in TP53 knockout iPSC‐derived human liver organoids

Molecular Oncology, EarlyView.
This study developed iPSC‐derived human liver organoids with TP53 gene knockout to model human liver fibrosis. These organoids showed elevated myofibroblast activation, early disease markers, and advanced fibrotic hallmarks. The use of profibrotic differentiation medium further amplified the fibrotic signature seen in the organoids.
Mustafa Karabicici, Soheil Akbari, Ceyda Caliskan, Canan Celiker, Ozden Oz, Leman Binokay, Gökhan Karakulah, Serif Senturk, Esra Erdal   +8 more
wiley   +1 more source

Graphical User Interface for the Development of Probabilistic Convolutional Neural Networks

Signals, 2023
Through the development of artificial intelligence, some capabilities of human beings have been replicated in computers. Among the developed models, convolutional neural networks stand out considerably because they make it possible for systems to have ...
Aníbal Chaves, Fábio Mendonça, Sheikh Shanawaz Mostafa, Fernando Morgado-Dias   +3 more
doaj   +1 more source

A Progressive Explanation of Inference in ‘Hybrid’ Bayesian Networks for Supporting Clinical Decision Making [PDF]

, 2016
Many Bayesian networks (BNs) have been developed as decision support tools. However, far fewer have been used in practice. Sometimes it is assumed that an accurate prediction is enough for useful decision support but this neglects the importance of trust:
Conference on Probabilistic Graphical Models, Kyrimi, E, MARSH, DWR   +2 more
core  

Molecular characterisation of human penile carcinoma and generation of paired epithelial primary cell lines

Molecular Oncology, EarlyView.
Generation of two normal and tumour (cancerous) paired human cell lines using an established tissue culture technique and their characterisation is described. Cell lines were characterised at cellular, protein, chromosome and gene expression levels and for HPV status.
Simon Broad, Mahmood Hachim, Tom Bertin, Karolina Penderecka, Rifat Hamoudi, Saif Khan, Rui Henrique, Natalie Bergmoser, Manit Arya, Kalle Sipila, Matteo Vietri Rudan, Asif Muneer, Aamir Ahmed   +12 more
wiley   +1 more source