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Network Medicine: New Paradigm in the -Omics Era

Nancy Lan Guo
2011 Anatomy & Physiology  
Dettling M, Buhlmann P (2003) Boosting for tumor classification with gene expression data. Bioinformatics 19: 1061-1069. 67.  ...  of a particular set of markers with the highest capacity for molecular diagnostics/prognostics [28, 29] .  ... 
doi:10.4172/2161-0940.1000e106 pmid:24634802 pmcid:PMC3951174 fatcat:q5zyxa4rizchzpxe7i6kzlfj5e

Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine

Dmitry Grapov, Johannes Fahrmann, Kwanjeera Wanichthanarak, Sakda Khoomrung
2018 Omics  
Many approaches incorporate complex artificial neural network architectures and are collectively referred to as deep learning (DL) applications.  ...  New approaches for highly integrated manufacturing and automation such as the Industry 4.0 and the Internet of things are also converging with ML methodologies.  ...  This systems approach enables robust characterization of biochemical signatures reflective of organismal phenotypes.  ... 
doi:10.1089/omi.2018.0097 pmid:30124358 pmcid:PMC6207407 fatcat:erurua5dzjfwvnrtadicw2rowa

Recent Advances in Network-based Methods for Disease Gene Prediction [article]

Sezin Kircali Ata, Min Wu, Yuan Fang, Le Ou-Yang, Chee Keong Kwoh, Xiao-Li Li
2020 arXiv   pre-print
Secondly, we categorize existing network-based efforts into network diffusion methods, traditional machine learning methods with handcrafted graph features and graph representation learning methods.  ...  In this survey, we aim to provide a comprehensive and an up-to-date review of network-based methods for disease gene prediction. We also conduct an empirical analysis on 14 state-of-the-art methods.  ...  ., node classification and link prediction) for disease gene prediction with different types of graph inputs.  ... 
arXiv:2007.10848v1 fatcat:zhrspbsj6zfpfhwa42mzjp4lvy

Inference of RhoGAP/GTPase regulation using single-cell morphological data from a combinatorial RNAi screen

O. Nir, C. Bakal, N. Perrimon, B. Berger
2010 Genome Research  
Augmented by knowledge of the basic structure of RhoGAP/GTPase signaling, namely, that GAPs act directly upstream of GTPases, we apply our framework for identifying genetic interactions to predict signaling  ...  Here, we describe a systematic computational framework based on a classification model for identifying genetic interactions using high-dimensional single-cell morphological data from genetic screens, apply  ...  We thank John Aach for aid with image processing and Michael Baym and Uri Laserson for helpful input.  ... 
doi:10.1101/gr.100248.109 pmid:20144944 pmcid:PMC2840989 fatcat:xysgkvoiyrgsdlvw25dlgkxa64

Integrating bioinformatics approaches for a comprehensive interpretation of metabolomics datasets

Dinesh Kumar Barupal, Sili Fan, Oliver Fiehn
2018 Current Opinion in Biotechnology  
Starting from statistical analyses, a range of new tools are available, including metabolite set enrichment analysis, pathway and network visualization, pathway prediction, biochemical databases and text  ...  However, interpreting those datasets in biological contexts remains a challenge, especially because many identified metabolites are not found in biochemical pathway databases.  ...  A reaction network graph can be created with or without side-products or co-factors in biochemical reactions [50, 51] .  ... 
doi:10.1016/j.copbio.2018.01.010 pmid:29413745 pmcid:PMC6358024 fatcat:ilgq56m3vfcnzdehdz22wj34xi

Biological network analysis with deep learning

Giulia Muzio, Leslie O'Bray, Karsten Borgwardt
2020 Briefings in Bioinformatics  
One major trend in the field is to use deep learning for this goal and, more specifically, to use methods that work with networks, the so-called graph neural networks (GNNs).  ...  The rise of this data has created a need for new computational tools to analyze networks.  ...  Funding This work was supported in part from the Alfried Krupp Prize for Young University Teachers of the Alfried Krupp von Bohlen und Halbach-Stiftung (K.B.) and in part from the European Union's Horizon  ... 
doi:10.1093/bib/bbaa257 pmid:33169146 pmcid:PMC7986589 fatcat:x7salmmidjei3og6ripsizkbam

Biochemical connectionism

Michael A. Lones, Alexander P. Turner, Luis A. Fuente, Susan Stepney, Leo S. D. Caves, Andy M. Tyrrell
2013 Natural Computing  
In particular, we focus on three features of biochemical networks that make them distinct from neural networks: their diverse, complex nodal processes, their emergent organisation, and the dynamical behaviours  ...  relationship between biochemical networks and biological evolution can guide us in this endeavour.  ...  Neural pathways are the patterns of connectivity that determine signal flow through a neural network.  ... 
doi:10.1007/s11047-013-9400-y fatcat:fdaar77b7ffdhho3fu6b4zd4nq

New Methods for Analyzing Complex Biomedical Systems and Signals

Irini Reljin, Zoran Obradović, Mirjana B. Popović, Valeri Mladenov
2018 Complexity  
Also, our sincere thanks go to reviewers for their dedicated efforts in evaluating the submitted papers and providing helpful comments.  ...  Finally, we thank the journal's Editorial Board for their help and assistance. Irini Reljin Zoran Obradović Mirjana B. Popović Valeri Mladenov Hindawi Volume 2018  ...  Gligorijević et al. described a novel method for identifying individuals with high risk of death after acute myocardial infarction by using artificial neural networks (ANNs).  ... 
doi:10.1155/2018/6405121 fatcat:dipl23qshrf3liqkcik4kw3amm

Non-linear dimensionality reduction of signaling networks

Sergii Ivakhno, J Douglas Armstrong
2007 BMC Systems Biology  
with graph-based clustering.  ...  signaling network in human epithelial cancer cells treated with different combinations of TNF, epidermal growth factor (EGF) and insulin and (II) combination of signal transduction pathways stimulated  ...  Acknowledgements We thank to Douglas Lauffenburger, John Albeck and Kevin Janes for useful discussions and comments on the manuscript.  ... 
doi:10.1186/1752-0509-1-27 pmid:17559646 pmcid:PMC1925119 fatcat:drnrbt5vdngg3ascehcdfu5xia

Decomposition of metabolic network into functional modules based on the global connectivity structure of reaction graph

H.-W. Ma, X.-M. Zhao, Y.-J. Yuan, A.-P. Zeng
2004 Bioinformatics  
Results: In this work, we use a reaction graph representation of a metabolic network for the identification of its global connectivity structure and for decomposition.  ...  Network decomposition is also necessary for functional analysis of metabolism by pathway analysis methods that are often hampered by the problem of combinatorial explosion due to the complexity of metabolic  ...  This small-world structure is regarded as one of the design principles of many robust and error-tolerant networks such as the computer network, neural network and certain social and economic networks  ... 
doi:10.1093/bioinformatics/bth167 pmid:15037506 fatcat:7llelvl2tbb7heyrjmzhpvlkue

Network-based methods for disease-gene prediction [article]

Lorenzo Madeddu, Giovanni Stilo, Paola Velardi
2019 arXiv   pre-print
Our method successfully compares with the best known system for disease gene prediction, and other state-of-the-art graph-based methods.  ...  To exploit at best latent information in the network, we propose an extended version of random walks, named Random Watcher-Walker (RW^2), which is able to learn rich representations of disease genes (or  ...  Graph Convolutional Networks (GCN) [21] , an inductive semi-supervised method to classify nodes in a network, based on an efficient variant of convolutional neural networks, which operates directly on  ... 
arXiv:1902.10117v1 fatcat:r3dbx33xozdtzmivyo42cur2ye

Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning

Gennady M. Verkhivker, Steve Agajanian, Guang Hu, Peng Tao
2020 Frontiers in Molecular Biosciences  
The wealth of structural and functional information along with diversity and complexity of allosteric mechanisms in therapeutically important protein families have provided a well-suited platform for development  ...  and discovery of allosteric modulators for therapeutically important protein targets.  ...  ACKNOWLEDGMENTS The authors acknowledge the technical assistance of Schmid College Grand Challenge Initiative Postdoctoral Fellow Dr. Anne Sonnenschein.  ... 
doi:10.3389/fmolb.2020.00136 pmid:32733918 pmcid:PMC7363947 fatcat:vxoqxun6ebhdveqlbwi7l7rfui

Random Hypergraph Models of Learning and Memory in Biomolecular Networks: Shorter-Term Adaptability vs. Longer-Term Persistency

Byoung-Tak Zhang
2007 2007 IEEE Symposium on Foundations of Computational Intelligence  
We aim to develop computational models of learning and memory inspired by the biomolecular networks embedded in their environment.  ...  Recent progress in genomics and proteomics makes it possible to understand the biological networks at the systems level.  ...  ACKNOWLEDGEMENTS The author would like to thank Joo-Kyung Kim, Sun Kim, and Ha-Young Jang for performing simulations.  ... 
doi:10.1109/foci.2007.371494 dblp:conf/foci/Zhang07 fatcat:yvt2gd3k5zdujcrwrol4rcsxty

Building attention and edge message passing neural networks for bioactivity and physical–chemical property prediction

M. Withnall, E. Lindelöf, O. Engkvist, H. Chen
2020 Journal of Cheminformatics  
Neural Message Passing for graphs is a promising and relatively recent approach for applying Machine Learning to networked data.  ...  Our results consistently perform on-par with other state-of-the-art machine learning approaches, and set a new standard on sparse multi-task virtual screening targets.  ...  Acknowledgements We would like to thank Zhenqin Wu for assistance in reproducing the MolNet datasets for use in this publication, and Dr. Igor Tetko for comments that improved the manuscript.  ... 
doi:10.1186/s13321-019-0407-y pmid:33430988 fatcat:5rdchnpqwzhgtcrrjdptdrnfci

Computational modeling and experimental analysis for the diagnosis of cell survival/death for Akt protein

Ayodeji Olalekan Salau, Shruti Jain
2020 Journal of Genetic Engineering and Biotechnology  
Radial basis function (RBF) and multiple-layer perceptron (MLP) were used for cell survival/death classification.  ...  deviation for 5-0-5 ng/ml combinations of TNF-EGF-Insulin.  ...  Details of any previous or concurrent submissions This paper has not been submitted anywhere else for review or has not been published previously.  ... 
doi:10.1186/s43141-020-00026-w pmid:32314080 fatcat:7m6qffd2qjcyjcpzn5gb5f5lt4
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