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Link and Node Prediction in Metabolic Networks with Probabilistic Logic [chapter]

Angelika Kimmig, Fabrizio Costa
2012 Lecture Notes in Computer Science  
Here we start to investigate two fundamental problems concerning automatic metabolic networks curation, namely link prediction and node prediction using ProbLog, a simple yet powerful extension of the  ...  logic programming language Prolog with independent random variables.  ...  Costa was supported by the GOA project 2008/08 Probabilistic Logic Learning and by the European Commission under the 7th Framework Programme, contract no. BISON-211898.  ... 
doi:10.1007/978-3-642-31830-6_29 fatcat:tqtgc5g7rbheznqtqokhoi5tpy

STATISTICAL RELATIONAL LEARNING: A STATE-OF-THE-ART REVIEW

Muhamet Kastrati, Marenglen Biba
2019 Journal of Engineering Technology and Applied Sciences  
As shown in Figure 1 , SRL combines a logic-based representation with probabilistic modeling and machine learning.  ...  SRL models are usually represented as combination of probabilistic graphical models (PGMs) with first-order logic (FOL) to handle the uncertainty and probabilistic correlations in relational domains.  ...  and link prediction.  ... 
doi:10.30931/jetas.594586 fatcat:qoei3pteibd6la4oqin6rvrxqi

Advances and challenges in logical modeling of cell cycle regulation: perspective for multi-scale, integrative yeast cell models

Matteo Barberis, Robert G. Todd, Lucas van der Zee, Jens Nielsen
2016 FEMS Yeast Research  
This article discusses the development of logical modeling of cell cycle regulation in Saccharomyces cerevisiae and perspectives for its integration with cellular networks for multi-scale models.  ...  proteins-and phenom- ena such as growth and temperature, represented as nodes in a logical network.  ...  This model was converted to a Boolean logic, where a prior knowledge network (PKN) with four nodes-comprehending the cyclins Clb5, Clb3 and Clb2, and the CKI Sic1-was generated (Linke et al. 2017) incorporating  ... 
doi:10.1093/femsyr/fow103 pmid:27993914 pmcid:PMC5225787 fatcat:sa432vs4abayngfcxgu2hratga

Challenges on Probabilistic Modeling for Evolving Networks [article]

Jianguo Ding, Pascal Bouvry
2013 arXiv   pre-print
This paper presents a survey on probabilistic modeling for evolving networks and identifies the new challenges which emerge on the probabilistic models and optimization strategies in the potential application  ...  With the emerging of new networks, such as wireless sensor networks, vehicle networks, P2P networks, cloud computing, mobile Internet, or social networks, the network dynamics and complexity expands from  ...  Nodes in the overlay networks can be connected by virtual or logical links, each of which corresponds to a path, perhaps through many physical links, in the underlying networks.  ... 
arXiv:1304.7820v2 fatcat:qvtskgtvpvh2npqbyjlghyu774

Modeling Gene Networks inSaccharomyces cerevisiaeBased on Gene Expression Profiles

Yulin Zhang, Kebo Lv, Shudong Wang, Jionglong Su, Dazhi Meng
2015 Computational and Mathematical Methods in Medicine  
logic networks.  ...  Based on these quantities the uncertainty coefficient was calculated for each gene triplet, following which, separate gene logic networks were constructed for the aerobic and anaerobic conditions.  ...  Attila Gulyás-Kovács at the Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, for their help in revising the paper.  ... 
doi:10.1155/2015/621264 pmid:26839582 pmcid:PMC4709922 fatcat:apnrmtripzcmfg5r653htnwexq

Consequences of Complexity within Biological Networks: Robustness and Health, or Vulnerability and Disease

Katrina M. Dipple, James K. Phelan, Edward R.B. McCabe
2001 Molecular Genetics and Metabolism  
A scale-free network is extremely inhomogeneous with a hub-and-spoke structure: most nodes have one or two links, but a few nodes have many links.  ...  , and logic circuits.  ... 
doi:10.1006/mgme.2001.3227 pmid:11592802 fatcat:cyxrrv2iqbhw3jkfk75itfgzla

Embedding Logical Queries on Knowledge Graphs [article]

William L. Hamilton, Payal Bajaj, Marinka Zitnik, Dan Jurafsky, Jure Leskovec
2019 arXiv   pre-print
We demonstrate the utility of this framework in two application studies on real-world datasets with millions of relations: predicting logical relationships in a network of drug-gene-disease interactions  ...  In our approach, we embed graph nodes in a low-dimensional space and represent logical operators as learned geometric operations (e.g., translation, rotation) in this embedding space.  ...  This research has been supported in part by NSF IIS-1149837, DARPA SIMPLEX, Stanford Data Science Initiative, Huawei, and Chan Zuckerberg Biohub.  ... 
arXiv:1806.01445v4 fatcat:s6qwg3sosrflnbqb372v45ha6e

Modelling techniques for biomolecular networks [article]

Gerhard Mayer
2020 arXiv   pre-print
First we shortly review the different kinds of network modelling methods for systems biology with an emphasis on the different subtypes of logical models, which we review in more detail.  ...  In the end we give a short review about the difference between quantitative and qualitative models and describe the mathematics that specifically deals with qualitative modelling.  ...  Networks with N nodes and an in-degree of K (K<=N) for every node are called N-K-nets.  ... 
arXiv:2003.00327v1 fatcat:ldcslhpgdrhfpavwypbx2c6qxu

OWL-NETS: Transforming OWL Representations for Improved Network Inference

Tiffany J. Callahan, William A. Baumgartner, Michael Bada, Adrianne L. Stefanski, Ignacio Tripodi, Elizabeth K. White, Lawrence E. Hunter
2017 Biocomputing 2018  
Using several examples built with the Open Biomedical Ontologies, we show that OWL-NETS can leverage existing ontology-based knowledge representations and network inference methods to generate novel, biologically-relevant  ...  The application of inductive inference methods, like machine learning and network inference are vital for extending our current knowledge.  ...  to collapse the nodes and edges that are necessary to logically represent relationships between biological entities in OWL, but are not themselves biologically meaningful and interfere with network inference  ... 
doi:10.1142/9789813235533_0013 fatcat:mrxyb6oknfb5de7hrny46p4uey

Connecting signaling and metabolic pathways in EGF receptor-mediated oncogenesis of glioblastoma

Arup K. Bag, Sapan Mandloi, Saulius Jarmalavicius, Susmita Mondal, Krishna Kumar, Chhabinath Mandal, Peter Walden, Saikat Chakrabarti, Chitra Mandal, Marcel Schilling
2019 PLoS Computational Biology  
Key nodes and paths identified by in silico perturbation were validated experimentally when inhibition of signaling pathway proteins altered expression of metabolic proteins as predicted by the model.  ...  As malignant transformation requires synchronization of growth-driving signaling (S) and metabolic (M) pathways, defining cancer-specific S-M interconnected networks (SMINs) could lead to better understanding  ...  The probabilistic network-based dynamic model uses experimental data and deals with the uncertainty of systems to predict drug targets and understand the effect of therapeutics.  ... 
doi:10.1371/journal.pcbi.1007090 pmid:31386654 pmcid:PMC6684045 fatcat:ccrgk7al7valxpcitkbbsomm7m

Transforming Graph Data for Statistical Relational Learning

R. A. Rossi, L. K. McDowell, D. W. Aha, J. Neville
2012 The Journal of Artificial Intelligence Research  
More specifically, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) system  ...  In particular, appropriate transformations of the nodes, links, and/or features of the data can dramatically affect the capabilities and results of SRL algorithms.  ...  Luke McDowell was supported in part by NSF award number 1116439 and by a grant from ONR. This research was also partly supported by the NSF under the contract number IIS-1149789.  ... 
doi:10.1613/jair.3659 fatcat:tumtadmqgven3jevn6ywcqiuka

PheNetic: network-based interpretation of molecular profiling data

Dries De Maeyer, Bram Weytjens, Joris Renkens, Luc De Raedt, Kathleen Marchal
2015 Nucleic Acids Research  
In this paper, we therefore introduce 'PheNetic', a userfriendly web server for inferring a sub-network based on probabilistic logical querying.  ...  The inferred sub-networks can be interactively visualized in the browser.  ...  In this context, we have previously developed PheNetic, which uses probabilistic logical querying to infer sub-networks from omics-derived gene lists (7) .  ... 
doi:10.1093/nar/gkv347 pmid:25878035 pmcid:PMC4489255 fatcat:js5nprj4kzfafojm4ck2x2csha

Probabilistic Modeling of Microbial Metabolic Networks for Integrating Partial Quantitative Knowledge Within the Nitrogen Cycle

Damien Eveillard, Nicholas J. Bouskill, Damien Vintache, Julien Gras, Bess B. Ward, Jérémie Bourdon
2019 Frontiers in Microbiology  
with their surroundings remains incomplete.  ...  One approach to overcoming such complications is the integration of partial qualitative and quantitative descriptions into more complex networks.  ...  To test our model, we simulated variations in chemical factors using a Markov Chain simulation algorithm parameterized with computed probabilities, and compared the predictions with the available time  ... 
doi:10.3389/fmicb.2018.03298 pmid:30745899 pmcid:PMC6360161 fatcat:7y6pmcnldfehrbjrxxciz2nbru

OWL-NETS: Transforming OWL Representations for Improved Network Inference

Tiffany J Callahan, William A Baumgartner, Michael Bada, Adrianne L Stefanski, Ignacio Tripodi, Elizabeth K White, Lawrence E Hunter
2018 Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing  
Using several examples built with the Open Biomedical Ontologies, we show that OWL-NETS can leverage existing ontology-based knowledge representations and network inference methods to generate novel, biologically-relevant  ...  The application of inductive inference methods, like machine learning and network inference are vital for extending our current knowledge.  ...  Acknowledgments We thank Marc Daya and Laura Stevens as well as Drs. Anis Karimpour-Fard, Daniel McShan, and Carsten Goerg for their feedback on the development of OWL-NETS.  ... 
pmid:29218876 pmcid:PMC5737627 fatcat:7b77fx3nlvbo3n37doy2svlige

Learning probabilistic logic models from probabilistic examples

Jianzhong Chen, Stephen Muggleton, José Santos
2008 Machine Learning  
We revisit an application developed originally using abductive Inductive Logic Programming (ILP) for modeling inhibition in metabolic networks.  ...  We now apply two Probabilistic ILP (PILP) approaches-abductive Stochastic Logic Programs (SLPs) and PRogramming In Statistical modeling (PRISM) to the application.  ...  Acknowledgements The authors would like to acknowledge support from the Royal Academy of Engineering/Microsoft Research Chair on 'Automated Microfluidic Experimentation using Probabilistic Inductive Logic  ... 
doi:10.1007/s10994-008-5076-4 pmid:19888348 pmcid:PMC2771423 fatcat:vpnu5djquncwfgsfvxlzsaoe3i
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