scholarly journals Adapting machine-learning algorithms to design gene circuits

2017 ◽  
Author(s):  
Tom Hiscock
Keyword(s):  
Machine Learning ◽  
Cell Fate ◽  
Biological Networks ◽  
Machine Learning Algorithms ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Gene Circuits ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Networks

AbstractBiological systems rely on complex networks, such as transcriptional circuits and protein-protein interaction networks, to perform a variety of functions e.g. responding to stimuli, directing cell fate, or patterning an embryo. Mathematical models are often used to ask: given some network, what function does it perform? However, we often want precisely the opposite i.e. given some circuit – either observedin vivo, or desired for some engineering objective – what biological networks could execute this function? Here, we adapt optimization algorithms from machine learning to rapidly screen and design gene circuits capable of performing arbitrary functions. We demonstrate the power of this approach by designing circuits (1) that recapitulate importantin vivophenomena, such as oscillators, and (2) to perform complex tasks for synthetic biology, such as counting noisy biological events. Our method can be readily applied to biological networks of any type and size, and is provided as an open-source and easy-to-use python module, GeneNet.

scholarly journals ANAT 3.0: a framework for elucidating functional protein subnetworks using graph-theoretic and machine learning approaches

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
L. F. Signorini ◽  
T. Almozlino ◽  
R. Sharan
Keyword(s):  
Machine Learning ◽  
Protein Interaction ◽  
Network Reconstruction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Reconstruction Algorithms ◽  
Functional Protein ◽  
Protein Protein Interaction ◽  
Graphical Tool ◽  
Protein Protein Interaction Networks

Abstract Background ANAT is a Cytoscape plugin for the inference of functional protein–protein interaction networks in yeast and human. It is a flexible graphical tool for scientists to explore and elucidate the protein–protein interaction pathways of a process under study. Results Here we present ANAT3.0, which comes with updated PPI network databases of 544,455 (human) and 155,504 (yeast) interactions, and a new machine-learning layer for refined network elucidation. Together they improve network reconstruction to more than twofold increase in the quality of reconstructing known signaling pathways from KEGG. Conclusions ANAT3.0 includes improved network reconstruction algorithms and more comprehensive protein–protein interaction networks than previous versions. ANAT is available for download on the Cytoscape Appstore and at https://www.cs.tau.ac.il/~bnet/ANAT/.

scholarly journals Refining modules to determine functionally significant clusters in molecular networks

2019 ◽  
Author(s):  
Rama Kaalia ◽  
Jagath C. Rajapakse
Keyword(s):  
Protein Interaction ◽  
Biological Networks ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Molecular Networks ◽  
Resolution Limit ◽  
Protein Protein Interaction ◽  
Functional Coverage ◽  
Topological Modules ◽  
Protein Protein Interaction Networks

AbstractModule detection algorithms relying on modularity maximization suffer from an inherent resolution limit that hinders detection of small topological modules, especially in molecular networks where most biological processes are believed to form small and compact communities. We propose a novel modular refinement approach that helps finding functionally significant modules of molecular networks. The module refinement algorithm improves the quality of topological modules in protein-protein interaction networks by finding biologically functionally significant modules. The algorithm is based on the fact that functional modules in biology do not necessarily represent those corresponding to maximum modularity. Larger modules corresponding to maximal modularity are incrementally re-modularized again under specific constraints so that smaller yet topologically and biologically valid modules are recovered. We show improvement in quality and functional coverage of modules using experiments on synthetic and real protein-protein interaction networks. Results were also compared with six existing methods available for clustering biological networks. In conclusion, the proposed algorithm finds smaller but functionally relevant modules that are undetected by classical quality maximization approaches for modular detection. The refinement procedure helps to detect more functionally enriched modules in protein-protein interaction networks, which are also more coherent with functionally characterised gene sets.

scholarly journals Refining modules to determine functionally significant clusters in molecular networks

BMC Genomics ◽  
2019 ◽  
Vol 20 (S9) ◽  
Author(s):  
Rama Kaalia ◽  
Jagath C. Rajapakse
Keyword(s):  
Protein Interaction ◽  
Biological Networks ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Molecular Networks ◽  
Resolution Limit ◽  
Protein Protein Interaction ◽  
Functional Coverage ◽  
Topological Modules ◽  
Protein Protein Interaction Networks

Abstract Background Module detection algorithms relying on modularity maximization suffer from an inherent resolution limit that hinders detection of small topological modules, especially in molecular networks where most biological processes are believed to form small and compact communities. We propose a novel modular refinement approach that helps finding functionally significant modules of molecular networks. Results The module refinement algorithm improves the quality of topological modules in protein-protein interaction networks by finding biologically functionally significant modules. The algorithm is based on the fact that functional modules in biology do not necessarily represent those corresponding to maximum modularity. Larger modules corresponding to maximal modularity are incrementally re-modularized again under specific constraints so that smaller yet topologically and biologically valid modules are recovered. We show improvement in quality and functional coverage of modules using experiments on synthetic and real protein-protein interaction networks. We also compare our results with six existing methods available for clustering biological networks. Conclusion The proposed algorithm finds smaller but functionally relevant modules that are undetected by classical quality maximization approaches for modular detection. The refinement procedure helps to detect more functionally enriched modules in protein-protein interaction networks, which are also more coherent with functionally characterised gene sets.

scholarly journals A machine learning framework for predicting drug–drug interactions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Suyu Mei ◽  
Kun Zhang
Keyword(s):  
Machine Learning ◽  
Drug Interactions ◽  
Drug Target ◽  
Target Genes ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Protein Protein Interaction ◽  
Learning Framework ◽  
Protein Protein Interaction Networks ◽  
Statistical Metrics

AbstractUnderstanding drug–drug interactions is an essential step to reduce the risk of adverse drug events before clinical drug co-prescription. Existing methods, commonly integrating heterogeneous data to increase model performance, often suffer from a high model complexity, As such, how to elucidate the molecular mechanisms underlying drug–drug interactions while preserving rational biological interpretability is a challenging task in computational modeling for drug discovery. In this study, we attempt to investigate drug–drug interactions via the associations between genes that two drugs target. For this purpose, we propose a simple f drug target profile representation to depict drugs and drug pairs, from which an l2-regularized logistic regression model is built to predict drug–drug interactions. Furthermore, we define several statistical metrics in the context of human protein–protein interaction networks and signaling pathways to measure the interaction intensity, interaction efficacy and action range between two drugs. Large-scale empirical studies including both cross validation and independent test show that the proposed drug target profiles-based machine learning framework outperforms existing data integration-based methods. The proposed statistical metrics show that two drugs easily interact in the cases that they target common genes; or their target genes connect via short paths in protein–protein interaction networks; or their target genes are located at signaling pathways that have cross-talks. The unravelled mechanisms could provide biological insights into potential adverse drug reactions of co-prescribed drugs.

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