scholarly journals Drug Repurposing Using Biological Networks

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 1057
Author(s):  
Francisco Javier Somolinos ◽  
Carlos León ◽  
Sara Guerrero-Aspizua
Keyword(s):  
Computational Methods ◽  
Biological Networks ◽  
High Performance ◽  
Biological Network ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Network Models ◽  
Biomedical Data ◽  
Multiple Targets ◽  
Medical Indication

Drug repositioning is a strategy to identify new uses for existing, approved, or research drugs that are outside the scope of its original medical indication. Drug repurposing is based on the fact that one drug can act on multiple targets or that two diseases can have molecular similarities, among others. Currently, thanks to the rapid advancement of high-performance technologies, a massive amount of biological and biomedical data is being generated. This allows the use of computational methods and models based on biological networks to develop new possibilities for drug repurposing. Therefore, here, we provide an in-depth review of the main applications of drug repositioning that have been carried out using biological network models. The goal of this review is to show the usefulness of these computational methods to predict associations and to find candidate drugs for repositioning in new indications of certain diseases.

scholarly journals Overcoming sparseness of biomedical networks to identify drug repositioning candidates

2020 ◽  
Author(s):  
Aleksandar Poleksic
Keyword(s):  
Biological Networks ◽  
Drug Targets ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Drug Efficacy ◽  
Computational Procedure ◽  
Computational Method ◽  
Biomedical Data ◽  
Disease Associations ◽  
Disease Associated Genes

AbstractModeling complex biological systems is necessary to understand biochemical interactions behind pharmacological effects of drugs. Successful in silico drug repurposing requires a thorough exploration of diverse biochemical concepts and their relationships, including drug’s adverse reactions, drug targets, disease symptoms, as well as disease associated genes and their pathways, to name a few. We present a computational method for inferring drug-disease associations from complex but incomplete and biased biological networks. Our method employs the compressed sensing technique to overcome the sparseness of biomedical data and, in turn, to enrich the set of verified relationships between different biomedical entities. We present a strategy for identifying network paths supportive of drug efficacy as well as a computational procedure capable of combining different network patterns to better distinguish treatments from non-treatments. The data and programs are freely available at http://bioinfo.cs.uni.edu/AEONET.html.

scholarly journals Reconstruction of biological networks based on life science data integration

2010 ◽  
Vol 7 (2) ◽  
Author(s):  
Benjamin Kormeier ◽  
Klaus Hippe ◽  
Patrizio Arrigo ◽  
Thoralf Töpel ◽  
Sebastian Janowski ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Data Integration ◽  
Biological Networks ◽  
Life Science ◽  
Biological Network ◽  
Network Models ◽  
Biological Data ◽  
Science Data ◽  
Biological Data Integration

SummaryFor the implementation of the virtual cell, the fundamental question is how to model and simulate complex biological networks. Therefore, based on relevant molecular database and information systems, biological data integration is an essential step in constructing biological networks. In this paper, we will motivate the applications BioDWH - an integration toolkit for building life science data warehouses, CardioVINEdb - a information system for biological data in cardiovascular-disease and VANESA- a network editor for modeling and simulation of biological networks. Based on this integration process, the system supports the generation of biological network models. A case study of a cardiovascular-disease related gene-regulated biological network is also presented.

Computational methods for identifying the critical nodes in biological networks

2019 ◽  
Vol 21 (2) ◽  
pp. 486-497 ◽  
Author(s):  
Xiangrong Liu ◽  
Zengyan Hong ◽  
Juan Liu ◽  
Yuan Lin ◽  
Alfonso Rodríguez-Patón ◽  
...  
Keyword(s):  
Computational Methods ◽  
Biological Networks ◽  
Biological Network ◽  
Low Cost ◽  
Experimental Methods ◽  
Computing Methods ◽  
Critical Nodes

Abstract A biological network is complex. A group of critical nodes determines the quality and state of such a network. Increasing studies have shown that diseases and biological networks are closely and mutually related and that certain diseases are often caused by errors occurring in certain nodes in biological networks. Thus, studying biological networks and identifying critical nodes can help determine the key targets in treating diseases. The problem is how to find the critical nodes in a network efficiently and with low cost. Existing experimental methods in identifying critical nodes generally require much time, manpower and money. Accordingly, many scientists are attempting to solve this problem by researching efficient and low-cost computing methods. To facilitate calculations, biological networks are often modeled as several common networks. In this review, we classify biological networks according to the network types used by several kinds of common computational methods and introduce the computational methods used by each type of network.

Application of Artificial Intelligence in Drug Repurposing: A mini-review

2021 ◽  
Vol 01 ◽  
Author(s):  
Gurudeeban Selvaraj ◽  
Satyavani Kaliamurthi ◽  
Gilles H. Peslherbe ◽  
Dong-Qing Wei
Keyword(s):  
Artificial Intelligence ◽  
Drug Repositioning ◽  
Target Identification ◽  
Drug Repurposing ◽  
Search Term ◽  
Biomedical Data ◽  
Drug Candidates ◽  
Drug Target Identification ◽  
Speed Up ◽  
Tools And Techniques

Background and aim: Advancement of extra-ordinary biomedical data (genomics, proteomics, metabolomics, drug libraries, and patient care data), evolution of super-computers, and continuous development of new algorithms that lead to a generous revolution in artificial intelligence (AI). Currently, many biotech and pharmaceutical companies made reasonable investments in and have co-operation with AI companies and increasing the chance of better healthcare tools development, includes biomarker and drug target identification, designing a new class of drugs and drug repurposing. Thus, the study is intended to project the pros and cons of AI in the application of drug repositioning. Methods: Using the search term “AI” and “drug repurposing” the relevant literatures retrieved and reviewed from different sources includes PubMed, Google Scholar, and Scopus. Results: Drug discovery is a lengthy process, however, leveraging the AI approaches in drug repurposing via quick virtual screening may enhance and speed-up the identification of potential drug candidates against communicable and non-communicable diseases. Therefore, in this mini-review, we have discussed different algorithms, tools and techniques, advantages, limitations on predicting the target in repurposing a drug. Conclusions: AI technology in drug repurposing with the association of pharmacology can efficiently identify drug candidates against pandemic diseases.

scholarly journals Drug Repurposing for Triple-Negative Breast Cancer

2020 ◽  
Vol 10 (4) ◽  
pp. 200
Author(s):  
Marta Ávalos-Moreno ◽  
Araceli López-Tejada ◽  
Jose L. Blaya-Cánovas ◽  
Francisca E. Cara-Lupiañez ◽  
Adrián González-González ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Biological Networks ◽  
Molecular Mechanisms ◽  
Triple Negative ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
High Rate ◽  
Investigational Drug ◽  
Oxide Synthase

Triple-negative breast cancer (TNBC) is the most aggressive type of breast cancer which presents a high rate of relapse, metastasis, and mortality. Nowadays, the absence of approved specific targeted therapies to eradicate TNBC remains one of the main challenges in clinical practice. Drug discovery is a long and costly process that can be dramatically improved by drug repurposing, which identifies new uses for existing drugs, both approved and investigational. Drug repositioning benefits from improvements in computational methods related to chemoinformatics, genomics, and systems biology. To the best of our knowledge, we propose a novel and inclusive classification of those approaches whereby drug repurposing can be achieved in silico: structure-based, transcriptional signatures-based, biological networks-based, and data-mining-based drug repositioning. This review specially emphasizes the most relevant research, both at preclinical and clinical settings, aimed at repurposing pre-existing drugs to treat TNBC on the basis of molecular mechanisms and signaling pathways such as androgen receptor, adrenergic receptor, STAT3, nitric oxide synthase, or AXL. Finally, because of the ability and relevance of cancer stem cells (CSCs) to drive tumor aggressiveness and poor clinical outcome, we also focus on those molecules repurposed to specifically target this cell population to tackle recurrence and metastases associated with the progression of TNBC.

scholarly journals Enhancement of COPD biological networks using a web-based collaboration interface

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 32 ◽  
Author(s):  
◽  
Stéphanie Boué ◽  
Brett Fields ◽  
Julia Hoeng ◽  
Jennifer Park ◽  
...  
Keyword(s):  
Biological Networks ◽  
Biological Network ◽  
Network Models ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Web Based ◽  
Data Driven Approach ◽  
Transcriptomics Data ◽  
Model Set ◽  
Biological Representation

The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development. With the increasing amounts of literature that are now available, crowdsourcing approaches offer new forms of collaboration for researchers to review biological findings, which can be applied to the construction and verification of complex biological networks. We report the construction of 50 biological network models relevant to lung biology and early COPD using an integrative systems biology and collaborative crowd-verification approach. By combining traditional literature curation with a data-driven approach that predicts molecular activities from transcriptomics data, we constructed an initial COPD network model set based on a previously published non-diseased lung-relevant model set. The crowd was given the opportunity to enhance and refine the networks on a website (https://bionet.sbvimprover.com/) and to add mechanistic detail, as well as critically review existing evidence and evidence added by other users, so as to enhance the accuracy of the biological representation of the processes captured in the networks. Finally, scientists and experts in the field discussed and refined the networks during an in-person jamboree meeting. Here, we describe examples of the changes made to three of these networks: Neutrophil Signaling, Macrophage Signaling, and Th1-Th2 Signaling. We describe an innovative approach to biological network construction that combines literature and data mining and a crowdsourcing approach to generate a comprehensive set of COPD-relevant models that can be used to help understand the mechanisms related to lung pathobiology. Registered users of the website can freely browse and download the networks.

scholarly journals The basis of easy controllability in Boolean networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Enrico Borriello ◽  
Bryan C. Daniels
Keyword(s):  
Biological Networks ◽  
Biological Network ◽  
Biological Systems ◽  
Network Models ◽  
Theoretical Explanation ◽  
Boolean Networks ◽  
Effective Control ◽  
External Control ◽  
Final State ◽  
Numerical Evidence

AbstractEffective control of biological systems can often be achieved through the control of a surprisingly small number of distinct variables. We bring clarity to such results using the formalism of Boolean dynamical networks, analyzing the effectiveness of external control in selecting a desired final state when that state is among the original attractors of the dynamics. Analyzing 49 existing biological network models, we find strong numerical evidence that the average number of nodes that must be forced scales logarithmically with the number of original attractors. This suggests that biological networks may be typically easy to control even when the number of interacting components is large. We provide a theoretical explanation of the scaling by separating controlling nodes into three types: those that act as inputs, those that distinguish among attractors, and any remaining nodes. We further identify characteristics of dynamics that can invalidate this scaling, and speculate about how this relates more broadly to non-biological systems.

scholarly journals Two distributed semi-supervised algorithms for mining of complex networks using Giraph

2018 ◽  
Author(s):  
Erfan Farhangi Maleki ◽  
Nasser Ghadiri ◽  
Maryam Lotfi Shahreza ◽  
Zeinab Maleki
Keyword(s):  
Complex Networks ◽  
Heterogeneous Networks ◽  
Biological Networks ◽  
Biological Network ◽  
Drug Repositioning ◽  
Label Propagation ◽  
Large Graphs ◽  
The Subject ◽  
Scalable Methods

AbstractBackground and ObjectiveHeterogeneous complex networks are large graphs consisting of different types of nodes and edges. The process of mining and knowledge extraction from these networks is so complicated. Moreover, the scale of these networks is steadily increasing. Thus, scalable methods are required.MethodsIn this paper, two distributed label propagation algorithms for heterogeneous networks, namely DHLP-1 and DHLP-2 have been introduced. Biological networks are one type of the heterogeneous complex networks. As a case study, we have measured the efficiency of our proposed DHLP-1 and DHLP-2 algorithms on a biological network consisting of drugs, diseases, and targets. The subject we have studied in this network is drug repositioning but our algorithms can be used as general methods for heterogeneous networks other than the biological network.ResultsWe compared the proposed algorithms with similar non-distributed versions of them namely MINProp and Heter-LP. The experiments revealed the good performance of the algorithms in terms of running time and accuracy.

scholarly journals Enhancement of COPD biological networks using a web-based collaboration interface

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 32 ◽  
Author(s):  
◽  
Stéphanie Boué ◽  
Brett Fields ◽  
Julia Hoeng ◽  
Jennifer Park ◽  
...  
Keyword(s):  
Biological Networks ◽  
Biological Network ◽  
Network Models ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Web Based ◽  
Data Driven Approach ◽  
Transcriptomics Data ◽  
Model Set ◽  
Biological Representation

The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development. With the increasing amounts of literature that are now available, crowdsourcing approaches offer new forms of collaboration for researchers to review biological findings, which can be applied to the construction and verification of complex biological networks. We report the construction of 50 biological network models relevant to lung biology and early COPD using an integrative systems biology and collaborative crowd-verification approach. By combining traditional literature curation with a data-driven approach that predicts molecular activities from transcriptomics data, we constructed an initial COPD network model set based on a previously published non-diseased lung-relevant model set. The crowd was given the opportunity to enhance and refine the networks on a website (https://bionet.sbvimprover.com/) and to add mechanistic detail, as well as critically review existing evidence and evidence added by other users, so as to enhance the accuracy of the biological representation of the processes captured in the networks. Finally, scientists and experts in the field discussed and refined the networks during an in-person jamboree meeting. Here, we describe examples of the changes made to three of these networks: Neutrophil Signaling, Macrophage Signaling, and Th1-Th2 Signaling. We describe an innovative approach to biological network construction that combines literature and data mining and a crowdsourcing approach to generate a comprehensive set of COPD-relevant models that can be used to help understand the mechanisms related to lung pathobiology. Registered users of the website can freely browse and download the networks.

scholarly journals Leveraging Distributed Biomedical Knowledge Sources to Discover Novel Uses for Known Drugs

2019 ◽  
Author(s):  
Finn Womack ◽  
Jason McClelland ◽  
David Koslicki
Keyword(s):  
Structural Information ◽  
Drug Repositioning ◽  
Low Cost ◽  
Drug Repurposing ◽  
Biological Knowledge ◽  
Biomedical Data ◽  
Learning Approaches ◽  
Data Types ◽  
Biomedical Knowledge ◽  
Promising Tool

AbstractComputational drug repurposing, also called drug repositioning, is a low cost, promising tool for finding new uses for existing drugs. With the continued growth of repositories of biomedical data and knowledge, increasingly varied kinds of information are available to train machine learning approaches to drug repurposing. However, existing efforts to integrate a diversity of data sources have been limited to only a small selection of data types, typically gene expression data, drug structural information, and protein interaction networks. In this study, we leverage a graph-based approach to integrate biological knowledge from 20 publicly accessible repositories to represent information involving 11 distinct bioentity types. We then employ a graph node embedding scheme and use utilize a random forest model to make novel predictions about which drugs can be used to treat certain diseases. Utilizing this approach, we find a performance improvement over existing computational drug repurposing approaches and find promising drug repositioning targets, including drug and disease pairs currently in clinical trials.

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