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Popescu VB, Kanhaiya K, Năstac DI, Czeizler E, Petre I. Network controllability solutions for computational drug repurposing using genetic algorithms. Sci Rep 2022; 12:1437. [PMID: 35082323 PMCID: PMC8791995 DOI: 10.1038/s41598-022-05335-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 12/29/2021] [Indexed: 12/22/2022] Open
Abstract
Control theory has seen recently impactful applications in network science, especially in connections with applications in network medicine. A key topic of research is that of finding minimal external interventions that offer control over the dynamics of a given network, a problem known as network controllability. We propose in this article a new solution for this problem based on genetic algorithms. We tailor our solution for applications in computational drug repurposing, seeking to maximize its use of FDA-approved drug targets in a given disease-specific protein-protein interaction network. We demonstrate our algorithm on several cancer networks and on several random networks with their edges distributed according to the Erdős-Rényi, the Scale-Free, and the Small World properties. Overall, we show that our new algorithm is more efficient in identifying relevant drug targets in a disease network, advancing the computational solutions needed for new therapeutic and drug repurposing approaches.
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Affiliation(s)
| | | | - Dumitru Iulian Năstac
- POLITEHNICA University of Bucharest, Faculty of Electronics, Telecommunications and Information Technology, 061071, Bucharest, Romania
| | - Eugen Czeizler
- Computer Science, Åbo Akademi University, 20500, Turku, Finland
- National Institute for Research and Development in Biological Sciences, 060031, Bucharest, Romania
| | - Ion Petre
- Department of Mathematics and Statistics, University of Turku, 20014, Turku, Finland.
- National Institute for Research and Development in Biological Sciences, 060031, Bucharest, Romania.
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Kaushik AC, Mehmood A, Dai X, Wei DQ. A comparative chemogenic analysis for predicting Drug-Target Pair via Machine Learning Approaches. Sci Rep 2020; 10:6870. [PMID: 32322011 PMCID: PMC7176722 DOI: 10.1038/s41598-020-63842-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 04/04/2020] [Indexed: 12/26/2022] Open
Abstract
A computational technique for predicting the DTIs has now turned out to be an indispensable job during the process of drug finding. It tapers the exploration room for interactions by propounding possible interaction contenders for authentication through experiments of wet-lab which are known for their expensiveness and time consumption. Chemogenomics, an emerging research area focused on the systematic examination of the biological impact of a broad series of minute molecular-weighting ligands on a broad raiment of macromolecular target spots. Additionally, with the advancement in time, the complexity of the algorithms is increasing which may result in the entry of big data technologies like Spark in this field soon. In the presented work, we intend to offer an inclusive idea and realistic evaluation of the computational Drug Target Interaction projection approaches, to perform as a guide and reference for researchers who are carrying out work in a similar direction. Precisely, we first explain the data utilized in computational Drug Target Interaction prediction attempts like this. We then sort and explain the best and most modern techniques for the prediction of DTIs. Then, a realistic assessment is executed to show the projection performance of several illustrative approaches in various situations. Ultimately, we underline possible opportunities for additional improvement of Drug Target Interaction projection enactment and also linked study objectives.
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Affiliation(s)
- Aman Chandra Kaushik
- Wuxi School of Medicine, Jiangnan University, Wuxi, China.
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China.
| | - Aamir Mehmood
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China
| | - Xiaofeng Dai
- Wuxi School of Medicine, Jiangnan University, Wuxi, China
| | - Dong-Qing Wei
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China.
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Jeong H, Qian X, Yoon BJ. CUFID-query: accurate network querying through random walk based network flow estimation. BMC Bioinformatics 2017; 18:500. [PMID: 29297279 PMCID: PMC5751815 DOI: 10.1186/s12859-017-1899-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Functional modules in biological networks consist of numerous biomolecules and their complicated interactions. Recent studies have shown that biomolecules in a functional module tend to have similar interaction patterns and that such modules are often conserved across biological networks of different species. As a result, such conserved functional modules can be identified through comparative analysis of biological networks. RESULTS In this work, we propose a novel network querying algorithm based on the CUFID (Comparative network analysis Using the steady-state network Flow to IDentify orthologous proteins) framework combined with an efficient seed-and-extension approach. The proposed algorithm, CUFID-query, can accurately detect conserved functional modules as small subnetworks in the target network that are expected to perform similar functions to the given query functional module. The CUFID framework was recently developed for probabilistic pairwise global comparison of biological networks, and it has been applied to pairwise global network alignment, where the framework was shown to yield accurate network alignment results. In the proposed CUFID-query algorithm, we adopt the CUFID framework and extend it for local network alignment, specifically to solve network querying problems. First, in the seed selection phase, the proposed method utilizes the CUFID framework to compare the query and the target networks and to predict the probabilistic node-to-node correspondence between the networks. Next, the algorithm selects and greedily extends the seed in the target network by iteratively adding nodes that have frequent interactions with other nodes in the seed network, in a way that the conductance of the extended network is maximally reduced. Finally, CUFID-query removes irrelevant nodes from the querying results based on the personalized PageRank vector for the induced network that includes the fully extended network and its neighboring nodes. CONCLUSIONS Through extensive performance evaluation based on biological networks with known functional modules, we show that CUFID-query outperforms the existing state-of-the-art algorithms in terms of prediction accuracy and biological significance of the predictions.
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Affiliation(s)
- Hyundoo Jeong
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, 77843, TX, USA.,Department of Neuroogy, Baylor College of Medicine, Houston, TX, USA.,Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX, USA
| | - Xiaoning Qian
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, 77843, TX, USA.,TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering (CBGSE), College Station, TX, USA
| | - Byung-Jun Yoon
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, 77843, TX, USA. .,TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering (CBGSE), College Station, TX, USA.
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Theodosiou T, Efstathiou G, Papanikolaou N, Kyrpides NC, Bagos PG, Iliopoulos I, Pavlopoulos GA. NAP: The Network Analysis Profiler, a web tool for easier topological analysis and comparison of medium-scale biological networks. BMC Res Notes 2017; 10:278. [PMID: 28705239 PMCID: PMC5513407 DOI: 10.1186/s13104-017-2607-8] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Accepted: 07/07/2017] [Indexed: 01/08/2023] Open
Abstract
OBJECTIVE Nowadays, due to the technological advances of high-throughput techniques, Systems Biology has seen a tremendous growth of data generation. With network analysis, looking at biological systems at a higher level in order to better understand a system, its topology and the relationships between its components is of a great importance. Gene expression, signal transduction, protein/chemical interactions, biomedical literature co-occurrences, are few of the examples captured in biological network representations where nodes represent certain bioentities and edges represent the connections between them. Today, many tools for network visualization and analysis are available. Nevertheless, most of them are standalone applications that often (i) burden users with computing and calculation time depending on the network's size and (ii) focus on handling, editing and exploring a network interactively. While such functionality is of great importance, limited efforts have been made towards the comparison of the topological analysis of multiple networks. RESULTS Network Analysis Provider (NAP) is a comprehensive web tool to automate network profiling and intra/inter-network topology comparison. It is designed to bridge the gap between network analysis, statistics, graph theory and partially visualization in a user-friendly way. It is freely available and aims to become a very appealing tool for the broader community. It hosts a great plethora of topological analysis methods such as node and edge rankings. Few of its powerful characteristics are: its ability to enable easy profile comparisons across multiple networks, find their intersection and provide users with simplified, high quality plots of any of the offered topological characteristics against any other within the same network. It is written in R and Shiny, it is based on the igraph library and it is able to handle medium-scale weighted/unweighted, directed/undirected and bipartite graphs. NAP is available at http://bioinformatics.med.uoc.gr/NAP .
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Affiliation(s)
- Theodosios Theodosiou
- Bioinformatics & Computational Biology Laboratory, Division of Basic Sciences, University of Crete Medical School, 70013 Heraklion, Crete, Greece
| | - Georgios Efstathiou
- Bioinformatics & Computational Biology Laboratory, Division of Basic Sciences, University of Crete Medical School, 70013 Heraklion, Crete, Greece
| | - Nikolas Papanikolaou
- Bioinformatics & Computational Biology Laboratory, Division of Basic Sciences, University of Crete Medical School, 70013 Heraklion, Crete, Greece
| | - Nikos C Kyrpides
- Joint Genome Institute, Lawrence Berkeley Lab, United States Department of Energy, 2800 Mitchell Drive, Walnut Creek, CA, 94598, USA
| | - Pantelis G Bagos
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Papasiopoulou 2-4, Galaneika, 35100, Lamia, Greece
| | - Ioannis Iliopoulos
- Bioinformatics & Computational Biology Laboratory, Division of Basic Sciences, University of Crete Medical School, 70013 Heraklion, Crete, Greece.
| | - Georgios A Pavlopoulos
- Bioinformatics & Computational Biology Laboratory, Division of Basic Sciences, University of Crete Medical School, 70013 Heraklion, Crete, Greece. .,Joint Genome Institute, Lawrence Berkeley Lab, United States Department of Energy, 2800 Mitchell Drive, Walnut Creek, CA, 94598, USA.
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Jeong H, Yoon BJ. SEQUOIA: significance enhanced network querying through context-sensitive random walk and minimization of network conductance. BMC SYSTEMS BIOLOGY 2017; 11:20. [PMID: 28361708 PMCID: PMC5374659 DOI: 10.1186/s12918-017-0404-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
BACKGROUND Network querying algorithms provide computational means to identify conserved network modules in large-scale biological networks that are similar to known functional modules, such as pathways or molecular complexes. Two main challenges for network querying algorithms are the high computational complexity of detecting potential isomorphism between the query and the target graphs and ensuring the biological significance of the query results. RESULTS In this paper, we propose SEQUOIA, a novel network querying algorithm that effectively addresses these issues by utilizing a context-sensitive random walk (CSRW) model for network comparison and minimizing the network conductance of potential matches in the target network. The CSRW model, inspired by the pair hidden Markov model (pair-HMM) that has been widely used for sequence comparison and alignment, can accurately assess the node-to-node correspondence between different graphs by accounting for node insertions and deletions. The proposed algorithm identifies high-scoring network regions based on the CSRW scores, which are subsequently extended by maximally reducing the network conductance of the identified subnetworks. CONCLUSIONS Performance assessment based on real PPI networks and known molecular complexes show that SEQUOIA outperforms existing methods and clearly enhances the biological significance of the query results. The source code and datasets can be downloaded from http://www.ece.tamu.edu/~bjyoon/SEQUOIA .
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Affiliation(s)
- Hyundoo Jeong
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
| | - Byung-Jun Yoon
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA.
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Hu J, Reinert K. LocalAli: an evolutionary-based local alignment approach to identify functionally conserved modules in multiple networks. ACTA ACUST UNITED AC 2014; 31:363-72. [PMID: 25282642 DOI: 10.1093/bioinformatics/btu652] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
MOTIVATION Sequences and protein interaction data are of significance to understand the underlying molecular mechanism of organisms. Local network alignment is one of key systematic ways for predicting protein functions, identifying functional modules and understanding the phylogeny from these data. Most of currently existing tools, however, encounter their limitations, which are mainly concerned with scoring scheme, speed and scalability. Therefore, there are growing demands for sophisticated network evolution models and efficient local alignment algorithms. RESULTS We developed a fast and scalable local network alignment tool called LocalAli for the identification of functionally conserved modules in multiple networks. In this algorithm, we firstly proposed a new framework to reconstruct the evolution history of conserved modules based on a maximum-parsimony evolutionary model. By relying on this model, LocalAli facilitates interpretation of resulting local alignments in terms of conserved modules, which have been evolved from a common ancestral module through a series of evolutionary events. A meta-heuristic method simulated annealing was used to search for the optimal or near-optimal inner nodes (i.e. ancestral modules) of the evolutionary tree. To evaluate the performance and the statistical significance, LocalAli were tested on 26 real datasets and 1040 randomly generated datasets. The results suggest that LocalAli outperforms all existing algorithms in terms of coverage, consistency and scalability, meanwhile retains a high precision in the identification of functionally coherent subnetworks. AVAILABILITY The source code and test datasets are freely available for download under the GNU GPL v3 license at https://code.google.com/p/localali/. CONTACT jialu.hu@fu-berlin.de or knut.reinert@fu-berlin.de. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jialu Hu
- Department of Mathematics and Computer Science, Freie Universität Berlin, Takustrasse 9, 14195 Berlin, Germany
| | - Knut Reinert
- Department of Mathematics and Computer Science, Freie Universität Berlin, Takustrasse 9, 14195 Berlin, Germany
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