Identification of drug–disease associations by using multiple drug and disease networks

2021 ◽  
Vol 16 ◽  
Author(s):  
Ying Yang ◽  
Lei Chen
Keyword(s):  
Drug Repositioning ◽  
Research Area ◽  
Small Dimension ◽  
Multiple Drug ◽  
Disease Networks ◽  
Therapeutic Uses ◽  
Disease Associations ◽  
New Research ◽  
The Cost ◽  
Novel Therapeutic

Background: Drug repositioning is a new research area in drug development. It aims to discover novel therapeutic uses of existing drugs. It could accelerate the process of designing novel drugs for some diseases and considerably decrease the cost. The traditional method to determine novel therapeutic uses of an existing drug is quite laborious. It is alternative to design computational methods to overcome such defect. Objective: This study aims to propose a novel model for the identification of drug–disease associations. Method: Twelve drug networks and three disease networks were built, which were fed into a powerful network-embedding algorithm called Mashup to produce informative drug and disease features. These features were combined to represent each drug–disease association. Classic classification algorithm, random forest, was used to build the model. Results: Tenfold cross-validation results indicated that the MCC, AUROC, and AUPR were 0.7156, 0.9280, and 0.9191, respectively. Conclusion: The proposed model showed good performance. Some tests indicated that a small dimension of drug features and a large dimension of disease features were beneficial for constructing the model. Moreover, the model was quite robust even if some drug or disease properties were not available.

Natural Products as a Promising Therapeutic Strategy to Target Cancer Stem Cells

2021 ◽  
Vol 28 ◽  
Author(s):  
Merve Erkisa ◽  
Melda Sariman ◽  
Oyku Gonul Geyik ◽  
Caner Geyik Geyik ◽  
Tatjana Stanojkovic ◽  
...  
Keyword(s):  
Stem Cells ◽  
Natural Products ◽  
Cancer Stem Cells ◽  
Immune Surveillance ◽  
Research Area ◽  
Chemical Library ◽  
New Research ◽  
The Cost ◽  
Near Future ◽  
Potential Use

: Cancer is still a deadly disease, and its treatment desperately needs to be managed in a very sophisticated way through fast-developing novel strategies. Most of the cancer cases eventually develop into recurrencies, for which cancer stem cells (CSCs) are thought to be responsible. They are considered as a subpopulation of all cancer cells of tumor tissue with aberrant regulation of self-renewal, unbalanced proliferation, and cell death properties. Moreover, CSCs show a serious degree of resistance to chemotherapy or radiotherapy and immune surveillance as well. Therefore, new classes of drugs are rushing into the market each year, which makes the cost of therapy increase dramatically. Natural products are also becoming a new research area as a diverse chemical library to suppress CSCs. Some of the products even show promise in this regard. So, the near future could witness the introduction of natural products as a source of new chemotherapy modalities, which may result in the development of novel anticancer drugs. They could also be a reasonably-priced alternative to highly expensive current treatments. Nowadays, considering the effects of natural compounds on targeting surface markers, signaling pathways, apoptosis, and escape from immunosurveillance have been a highly intriguing area in preclinical and clinical research. In this review, we present scientific advances regarding their potential use in the inhibition of CSCs and the mechanisms by which they kill the CSCs.

scholarly journals Predicting Drug-Disease Associations via Using Gaussian Interaction Profile and Kernel-Based Autoencoder

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Han-Jing Jiang ◽  
Yu-An Huang ◽  
Zhu-Hong You
Keyword(s):  
Case Studies ◽  
Computational Models ◽  
Cross Validation ◽  
Drug Repositioning ◽  
Feature Learning ◽  
Superior Performance ◽  
Reliable Model ◽  
Disease Associations ◽  
The Cost ◽  
Fold Cross Validation

Computational drug repositioning, designed to identify new indications for existing drugs, significantly reduced the cost and time involved in drug development. Prediction of drug-disease associations is promising for drug repositioning. Recent years have witnessed an increasing number of machine learning-based methods for calculating drug repositioning. In this paper, a novel feature learning method based on Gaussian interaction profile kernel and autoencoder (GIPAE) is proposed for drug-disease association. In order to further reduce the computation cost, both batch normalization layer and the full-connected layer are introduced to reduce training complexity. The experimental results of 10-fold cross validation indicate that the proposed method achieves superior performance on Fdataset and Cdataset with the AUCs of 93.30% and 96.03%, respectively, which were higher than many previous computational models. To further assess the accuracy of GIPAE, we conducted case studies on two complex human diseases. The top 20 drugs predicted, 14 obesity-related drugs, and 11 drugs related to Alzheimer's disease were validated in the CTD database. The results of cross validation and case studies indicated that GIPAE is a reliable model for predicting drug-disease associations.

Drug Repurposing: An Emerging Tool for Drug Reuse, Recycling and Discovery

2021 ◽  
Vol 13 ◽  
Author(s):  
Supriya Roy ◽  
Suneela Dhaneshwar ◽  
Bhavya Bhasin
Keyword(s):  
Drug Development ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Therapeutic Agents ◽  
Medical Disorders ◽  
Therapeutic Uses ◽  
Original Product ◽  
Pharmaceutical Industries ◽  
Repurposed Drugs ◽  
The Cost

: Drug repositioning or repurposing is a revolutionary breakthrough in drug development that focuses on rediscovering new uses for old therapeutic agents. Drug repositioning can be defined more precisely as the process of exploring new indications for an already approved drug while drug repurposing includes overall re-development approaches grounded in the identical chemical structure of the active drug moiety as in the original product The repositioning approach accelerates the drug development process, curtails the cost and risk inherent to drug development. The strategy focuses on the polypharmacology of drugs to unlocks novel opportunities for logically designing more efficient therapeutic agents for unmet medical disorders. Drug repositioning also expresses certain regulatory challenges that hamper its further utilization. The review outlines the eminent role of drug repositioning in new drug discovery, methods to predict the molecular targets of a drug molecule, advantages that the strategy offers to the pharmaceutical industries, explaining how the industrial collaborations with academics can assist in the discovering more repositioning opportunities. The focus of the review is to highlight the latest applications of drug repositioning in various disorders. The review also includes a comparison of old and new therapeutic uses of repurposed drugs, with the assessment of their novel mechanisms of action and pharmacological effects in the management of various disorders. Various restrictions and challenges that repurposed drugs come across during their development and regulatory phases are also highlighted.

Fuzzy multi-objective location and routing problem

2020 ◽  
Vol 39 (3) ◽  
pp. 3259-3273
Author(s):  
Nasser Shahsavari-Pour ◽  
Najmeh Bahram-Pour ◽  
Mojde Kazemi
Keyword(s):  
Firefly Algorithm ◽  
High Reliability ◽  
Research Area ◽  
Nsga Ii ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Allocation ◽  
Multi Objective ◽  
Location Routing ◽  
The Cost

The location-routing problem is a research area that simultaneously solves location-allocation and vehicle routing issues. It is critical to delivering emergency goods to customers with high reliability. In this paper, reliability in location and routing problems was considered as the probability of failure in depots, vehicles, and routs. The problem has two objectives, minimizing the cost and maximizing the reliability, the latter expressed by minimizing the expected cost of failure. First, a mathematical model of the problem was presented and due to its NP-hard nature, it was solved by a meta-heuristic approach using a NSGA-II algorithm and a discrete multi-objective firefly algorithm. The efficiency of these algorithms was studied through a complete set of examples and it was found that the multi-objective discrete firefly algorithm has a better Diversification Metric (DM) index; the Mean Ideal Distance (MID) and Spacing Metric (SM) indexes are only suitable for small to medium problems, losing their effectiveness for big problems.

scholarly journals Artificial intelligence-based computational framework for drug-target prioritization and inference of novel repositionable drugs for Alzheimer’s disease

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Shingo Tsuji ◽  
Takeshi Hase ◽  
Ayako Yachie-Kinoshita ◽  
Taiko Nishino ◽  
Samik Ghosh ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Drug Target ◽  
Target Genes ◽  
Drug Repositioning ◽  
Therapeutic Targets ◽  
Machine Learning Techniques ◽  
Computational Framework ◽  
Novel Therapeutic

Abstract Background Identifying novel therapeutic targets is crucial for the successful development of drugs. However, the cost to experimentally identify therapeutic targets is huge and only approximately 400 genes are targets for FDA-approved drugs. As a result, it is inevitable to develop powerful computational tools that can identify potential novel therapeutic targets. Fortunately, the human protein-protein interaction network (PIN) could be a useful resource to achieve this objective. Methods In this study, we developed a deep learning-based computational framework that extracts low-dimensional representations of high-dimensional PIN data. Our computational framework uses latent features and state-of-the-art machine learning techniques to infer potential drug target genes. Results We applied our computational framework to prioritize novel putative target genes for Alzheimer’s disease and successfully identified key genes that may serve as novel therapeutic targets (e.g., DLG4, EGFR, RAC1, SYK, PTK2B, SOCS1). Furthermore, based on these putative targets, we could infer repositionable candidate-compounds for the disease (e.g., tamoxifen, bosutinib, and dasatinib). Conclusions Our deep learning-based computational framework could be a powerful tool to efficiently prioritize new therapeutic targets and enhance the drug repositioning strategy.

scholarly journals Amelioration of the Cost of Conjugative Plasmid Carriage in Eschericha coli K12

Genetics ◽  
2003 ◽  
Vol 165 (4) ◽  
pp. 1641-1649
Author(s):  
Cecilia Dahlberg ◽  
Lin Chao
Keyword(s):  
Drug Resistance ◽  
Multiple Drug Resistance ◽  
Conjugative Plasmid ◽  
Energetic Cost ◽  
Multiple Drug ◽  
Genetic Changes ◽  
Batch Cultures ◽  
Transfer Rates ◽  
Bacterial Plasmid ◽  
The Cost

Abstract Although plasmids can provide beneficial functions to their host bacteria, they might confer a physiological or energetic cost. This study examines how natural selection may reduce the cost of carrying conjugative plasmids with drug-resistance markers in the absence of antibiotic selection. We studied two plasmids, R1 and RP4, both of which carry multiple drug resistance genes and were shown to impose an initial fitness cost on Escherichia coli. To determine if and how the cost could be reduced, we subjected plasmid-containing bacteria to 1100 generations of evolution in batch cultures. Analysis of the evolved populations revealed that plasmid loss never occurred, but that the cost was reduced through genetic changes in both the plasmids and the bacteria. Changes in the plasmids were inferred by the demonstration that evolved plasmids no longer imposed a cost on their hosts when transferred to a plasmid-free clone of the ancestral E. coli. Changes in the bacteria were shown by the lowered cost when the ancestral plasmids were introduced into evolved bacteria that had been cured of their (evolved) plasmids. Additionally, changes in the bacteria were inferred because conjugative transfer rates of evolved R1 plasmids were lower in the evolved host than in the ancestral host. Our results suggest that once a conjugative bacterial plasmid has invaded a bacterial population it will remain even if the original selection is discontinued.

Music Neurotechnology for Sound Synthesis: Sound Synthesis with Spiking Neuronal Networks

Leonardo ◽  
2009 ◽  
Vol 42 (5) ◽  
pp. 439-442 ◽  
Author(s):  
Eduardo R. Miranda ◽  
John Matthias
Keyword(s):  
Cortical Neurons ◽  
Neuronal Networks ◽  
Research Area ◽  
Sound Synthesis ◽  
Ongoing Research ◽  
Computer Interfaces ◽  
Engineering Sciences ◽  
New Research ◽  
Sound Files

Music neurotechnology is a new research area emerging at the crossroads of neurobiology, engineering sciences and music. Examples of ongoing research into this new area include the development of brain-computer interfaces to control music systems and systems for automatic classification of sounds informed by the neurobiology of the human auditory apparatus. The authors introduce neurogranular sampling, a new sound synthesis technique based on spiking neuronal networks (SNN). They have implemented a neurogranular sampler using the SNN model developed by Izhikevich, which reproduces the spiking and bursting behavior of known types of cortical neurons. The neurogranular sampler works by taking short segments (or sound grains) from sound files and triggering them when any of the neurons fire.

Progress and Prospects for the Development of Computer-Generated Actors for Military Simulation: Part 1-Introduction and Background

2003 ◽  
Vol 12 (3) ◽  
pp. 311-325 ◽  
Author(s):  
Martin R. Stytz ◽  
Sheila B. Banks
Keyword(s):  
Research Area ◽  
Background Information ◽  
Life Cycle Costs ◽  
Material Management ◽  
Reasoning System ◽  
Force Structure ◽  
The Cost ◽  
Future Work ◽  
Level Training ◽  
Made In

The development of computer-generated synthetic environments, also calleddistributed virtual environments, for military simulation relies heavily upon computer-generated actors (CGAs) to provide accurate behaviors at reasonable cost so that the synthetic environments are useful, affordable, complex, and realistic. Unfortunately, the pace of synthetic environment development and the level of desired CGA performance continue to rise at a much faster rate than CGA capability improvements. This insatiable demand for realism in CGAs for synthetic environments arises from the growing understanding of the significant role that modeling and simulation can play in a variety of venues. These uses include training, analysis, procurement decisions, mission rehearsal, doctrine development, force-level and task-level training, information assurance, cyberwarfare, force structure analysis, sustainability analysis, life cycle costs analysis, material management, infrastructure analysis, and many others. In these and other uses of military synthetic environments, computer-generated actors play a central role because they have the potential to increase the realism of the environment while also reducing the cost of operating the environment. The progress made in addressing the technical challenges that must be overcome to realize effective and realistic CGAs for military simulation environments and the technical areas that should be the focus of future work are the subject of this series of papers, which survey the technologies and progress made in the construction and use of CGAs. In this, the first installment in the series of three papers, we introduce the topic of computer-generated actors and issues related to their performance and fidelity and other background information for this research area as related to military simulation. We also discuss CGA reasoning system techniques and architectures.

BGMSDDA: A bipartite graph diffusion algorithm with multiple similarity integration for drug-disease association prediction

2021 ◽  
Author(s):  
Guobo Xie ◽  
Jianming Li ◽  
Guosheng Gu ◽  
Yuping Sun ◽  
Zhiyi Lin ◽  
...  
Keyword(s):  
Bipartite Graph ◽  
Drug Repositioning ◽  
Disease Association ◽  
The Cost ◽  
Association Matrix

Drug repositioning, a method that relies on the information from the original drug-disease association matrix, aims to identify new indications for existing drugs and will greatly reduce the cost and...

A systematic review of e-tail product returns and an agenda for future research

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kamrul Ahsan ◽  
Shams Rahman
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Research Area ◽  
Future Research ◽  
Product Returns ◽  
Content Type ◽  
People Skills ◽  
New Research ◽  
Returns Management ◽  
The Impact

PurposeThis study conducts a systematic literature review of e-tail product returns research. E-tail product returns are essentially acquisition of products that have been sold through purely online or brick-and-click channels and then returned by consumer to business.Design/methodology/approachUsing a systematic literature review protocol, we identified 75 peer-reviewed articles on e-tail product returns, conducted bibliometric analysis and content analysis of the articles and summarised our findings.FindingsThe findings reveal that the subject of e-tail returns is a new research area; academics have started to investigate several aspects of e-tail returns through different research methodologies and theoretical foundations. Further research is required in leading e-commerce countries and on key areas such as omni-channel returns management, customer satisfaction and service, the impact of resources such as people skills, the benefits of technology and IT systems in managing e-tail returns.Practical implicationsThe study offers a summative account of current e-tail knowledge areas, which can serve as a reference guide for e-tailers to develop strategies for more efficient and competitive product returns.Originality/valueThis study contributes theoretically by developing clusters of key themes or knowledge areas about e-tail returns. It also provides a conceptual framework for e-tail returns management, which can be used as a springboard for further empirical research.

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