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Pioneering topological methods for network-based drug–target prediction by exploiting a brain-network self-organization theory
2017
Briefings in Bioinformatics
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Surprisingly, our results show that the bipartite topology alone, if adequately exploited by means of the recently proposed localcommunity-paradigm (LCP) theory-initially detected in brain-network topological ...
self-organization and afterwards generalized to any complex network-is able to suggest highly reliable predictions, with comparable performance with the state-of-the-artsupervised methods that exploit ...
difference between LCP theory and clustering in complex network; Maksim Kitsak for the useful discussion on the definition of CNs in bipartite topology; Timothy Ravasi for encouraging and supporting in ...
doi:10.1093/bib/bbx041
pmid:28453640
fatcat:gqmtdv52lzhtxhtkj7yoyvcaxa
Local-community network automata modelling based on length-three-paths for prediction of complex network structures in protein interactomes, food webs and more
[article]
2018
bioRxiv
pre-print
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Here we show that the same rule of complex network self-organization is valid across different physical scales and allows to predict protein interactions, food web trophic relations and world trade network ...
model essentially driven by topological neighbourhood information. ...
Acknowledgements We thank Alexander Mestiashvili and the BIOTEC System Administrators for their IT support, Gloria Marchesi for the administrative assistance and the Centre for Information Services and ...
doi:10.1101/346916
fatcat:etpuzr4u4jgodixeixgruy2ue4
A renaissance of neural networks in drug discovery
2016
Expert Opinion on Drug Discovery
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ANN) by Fukushima [4], self-organizing maps by Kohonen [5], and energy-based recurrent ANNs by Hopfield [6]. ...
Their focus is on backpropagation neural networks and their variants, self-organizing maps and associated methods, and a relatively new technique, deep learning. ...
The Associative Neural Network (ASNN) 355 method based on a model of thalamo cortical organization of the brain addresses this problem [67] . ...
doi:10.1080/17460441.2016.1201262
pmid:27295548
fatcat:2cbfrf6jbzbolkxkriudetdfm4
Graph Neural Networks and Their Current Applications in Bioinformatics
2021
Frontiers in Genetics
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Meanwhile, according to the specific applications for various omics data, we categorize and discuss the related studies in three aspects: disease prediction, drug discovery, and biomedical imaging. ...
Then, three representative tasks are proposed based on the three levels of structural information that can be learned by GNNs: node classification, link prediction, and graph generation. ...
FUNDING This research was funded by the National Natural Science Foundation of China (No. 61862067) and the Doctor Science Foundation of Yunnan Normal University (No. 01000205020503090). ...
doi:10.3389/fgene.2021.690049
fatcat:4p55ap6sivcy7h6dpne5fut6lu
Colloquium : Control of dynamics in brain networks
2018
Reviews of Modern Physics
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Efforts to address this gap include the construction of tools for the control of brain networks, mostly adapted from control and dynamical systems theory. ...
Informed by current opportunities for practical intervention, these theoretical contributions provide models that draw from a wide array of mathematical approaches. ...
grid and the identification of drug targets in a cancer signaling network. ...
doi:10.1103/revmodphys.90.031003
fatcat:ly3t2edoobaezdv7pzeumnyvry
Re-membering the body: applications of computational neuroscience to the top-down control of regeneration of limbs and other complex organs
2015
Integrative Biology
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Bioelectric signaling networks guide pattern formation and may implement a somatic memory system. ...
Deep parallels may exist between information processing in the brain and morphogenetic control mechanisms. ...
Bose, a pioneer of electrophysiology as a medium of information processing beyond animal nervous systems. ...
doi:10.1039/c5ib00221d
pmid:26571046
pmcid:PMC4667987
fatcat:tq4esq2r3zdgtedg7ah67b537u
Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches
2020
Biotechnology and Bioprocess Engineering
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This review provides a comprehensive, organized summary of the recent research trends in AI-guided drug discovery process including target identification, hit identification, ADMET prediction, lead optimization ...
Since artificial intelligence (AI) is leading the fourth industrial revolution, AI can be considered as a viable solution for unstable drug research and development. ...
Neither ethical approval nor informed consent was required for this study. ...
doi:10.1007/s12257-020-0049-y
pmid:33437151
pmcid:PMC7790479
fatcat:wqdmkkas2nb65gy3pymlgisuwi
Multiscale modeling of brain network organization
[article]
2021
arXiv
pre-print
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2022 accepted doi:10.1103/revmodphys.94.031002 arXiv:2111.13473v2 fatcat:naxnlxepyrhsrfglpqk3hv7ht4
Efforts are reviewed on the multilayer network properties underlying higher-order organization of neuronal assemblies, as well as on the identification of multimodal network-based biomarkers of brain pathologies ...
A complete understanding of the brain requires an integrated description of the numerous scales of neural organization. ...
Special thanks are given to A. Canal Garcia, M. Chavez, V. Latora, J. Martin-Buldu, ...
arXiv:2111.13473v1
fatcat:ac5vvdkvdvfx3mhlg5xgx57efy
Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future
[article]
2021
arXiv
pre-print
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We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure and electrical-based analysis. ...
As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interactive nodes connected by edges whose weights can be ...
[175] also proposed a GCN-based method for predicting missing infant brain DMRI data. ...
arXiv:2105.13137v1
fatcat:gm7d2ziagba7bj3g34u4t3k43y
A Review of Mathematical and Computational Methods in Cancer Dynamics
[article]
2022
arXiv
pre-print
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To conclude, the perspective cultivates an intuition for computational systems oncology in terms of nonlinear dynamics, information theory, inverse problems and complexity. ...
With longitudinal screening and time-series analysis of cellular dynamics, universally observed causal patterns pertaining to dynamical systems, may self-organize in the signaling or gene expression state-space ...
Thanks to Rik Bhattacharja (Concordia University) for redesigning the figures drafted by AU. Figure 1A was adapted from https://ha0ye.github.io/rEDM/articles/rEDM.html ...
arXiv:2201.02055v5
fatcat:hxhvnvagcbdeldwsb3zet3wavu
From Cells as Computation to Cells as Apps
[chapter]
2016
IFIP Advances in Information and Communication Technology
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Such a relatively new perspective, clearly pursued by systems biology, is contributing to the view that biology is, in several respects, a quantitative science. ...
Several in-silico, in-vitro and in-vivo results make such a possibility a very concrete one. ...
PS and LD thank Pier Luigi Luisi for insightful discussions on Maturana-Varela autopoiesis, cognition, minimal life, and embodiment. ...
doi:10.1007/978-3-319-47286-7_8
fatcat:i2nwaeixkzewxm7qs76ax2wfga
Molecular bionics – engineering biomaterials at the molecular level using biological principles
2019
Biomaterials
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The extreme difficulty of targeting this organ is summarized by the current paucity of stimuli-responsive materials designed for applications in the brain tissue [339] . ...
The new tiling models are able to predict the surface structure and topologies of viral capsids by exploiting the concept of symmetry to the full extent [258] . ...
doi:10.1016/j.biomaterials.2018.10.044
pmid:30419394
fatcat:6tun5vu7cjffbmuplbis5b2l74
Brain enhancement through cognitive training: a new insight from brain connectome
2015
Frontiers in Systems Neuroscience
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Although only a few studies have exploited the connectome approach for studying alterations of the brain network induced by cognitive training interventions so far, we believe that it would be a useful ...
Moreover, cognitive training interventions should have effects on brain sub-networks, not on a single brain region, and graph theoretical network metrics quantifying topological architecture of the brain ...
Acknowledgments The authors appreciate the National University of Singapore for supporting the Cognitive Engineering Group at the Singapore Institute for Neurotechnology (SINAPSE) under WBS Number R-719 ...
doi:10.3389/fnsys.2015.00044
pmid:25883555
pmcid:PMC4381643
fatcat:3vp36ydhfbbuni7en7veq6s2oi
A Brief History of Simulation Neuroscience
2019
Frontiers in Neuroinformatics
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cells are connected to each other, and how the seemingly infinite networks they form give rise to the vast diversity of brain functions. ...
Simulation neuroscience is currently the only methodology for systematically approaching the multiscale brain. ...
Network-based approaches propose to analyze these big, complex data and to model brain networks with theoretical and computational methods such as graph theory and algebraic topology, through statistical ...
doi:10.3389/fninf.2019.00032
pmid:31133838
pmcid:PMC6513977
fatcat:omyrnds7kngjlk4mm4gg5t7gqa
Modularity in Biological Networks
2021
Frontiers in Genetics
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Many biological networks are organized into a modular structure, so methods to discover such modules are essential if we are to understand the biological system as a whole. ...
However, most of the methods used in biology to this end, have a limited applicability, as they are very specific to the system they were developed for. ...
By extending the ideas of the DIAMOND/HuDiNe approaches it is possible to analyze the relationship between drug targets and disease-proteins through a topological proximity measure. ...
doi:10.3389/fgene.2021.701331
pmid:34594357
pmcid:PMC8477004
fatcat:2pw2vxzknzfc5hqmjltj2rfgeq
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