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Link and Node Prediction in Metabolic Networks with Probabilistic Logic
[chapter]
2012
Lecture Notes in Computer Science
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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
2019
Journal of Engineering Technology and Applied Sciences
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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
2016
FEMS Yeast Research
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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]
2013
arXiv
pre-print
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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
2015
Computational and Mathematical Methods in Medicine
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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
2001
Molecular Genetics and Metabolism
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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]
2019
arXiv
pre-print
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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]
2020
arXiv
pre-print
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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
2017
Biocomputing 2018
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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
2019
PLoS Computational Biology
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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
2012
The Journal of Artificial Intelligence Research
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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
2015
Nucleic Acids Research
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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
2019
Frontiers in Microbiology
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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
2018
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
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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
2008
Machine Learning
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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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