Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Drug Research Meets Network Science: Where Are We?

ABSTRACT: Network theory provides one of the most potent analysis tools for the study of complex systems. In this paper, we illustrate the network-based perspective in drug research and how it is coherent with the new paradigm of drug discovery. We first present data sources from which networks are built, then show some examples of how the networks can be used to investigate drug-related systems. A section is devoted to network-based inference applications, i.e., prediction methods based on interactomes, that can be used to identify putative drug-target interactions without resorting to 3D modeling. Finally, we present some aspects of Boolean networks dynamics, anticipating that it might become a very potent modeling framework to develop in silico screening protocols able to simulate phenotypic screening experiments. We conclude that network applications integrated with machine learning and 3D modeling methods will become an indispensable tool for computational drug discovery in the next years.

SUBMITTER: Recanatini M

PROVIDER: S-EPMC8007104 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Drug Research Meets Network Science: Where Are We?

Recanatini Maurizio M Cabrelle Chiara C

Journal of medicinal chemistry 20200508 16

Network theory provides one of the most potent analysis tools for the study of complex systems. In this paper, we illustrate the network-based perspective in drug research and how it is coherent with the new paradigm of drug discovery. We first present data sources from which networks are built, then show some examples of how the networks can be used to investigate drug-related systems. A section is devoted to network-based inference applications, i.e., prediction methods based on interactomes, ...[more]

PMID: 32338900

Similar Datasets

Network science meets respiratory medicine for OSAS phenotyping and severity prediction.

Project description:Obstructive sleep apnea syndrome (OSAS) is a common clinical condition. The way that OSAS risk factors associate and converge is not a random process. As such, defining OSAS phenotypes fosters personalized patient management and population screening. In this paper, we present a network-based observational, retrospective study on a cohort of 1,371 consecutive OSAS patients and 611 non-OSAS control patients in order to explore the risk factor associations and their correlation with OSAS comorbidities. To this end, we construct the Apnea Patients Network (APN) using patient compatibility relationships according to six objective parameters: age, gender, body mass index (BMI), blood pressure (BP), neck circumference (NC) and the Epworth sleepiness score (ESS). By running targeted network clustering algorithms, we identify eight patient phenotypes and corroborate them with the co-morbidity types. Also, by employing machine learning on the uncovered phenotypes, we derive a classification tree and introduce a computational framework which render the Sleep Apnea Syndrome Score (SASScore); our OSAS score is implemented as an easy-to-use, web-based computer program which requires less than one minute for processing one individual. Our evaluation, performed on a distinct validation database with 231 consecutive patients, reveals that OSAS prediction with SASScore has a significant specificity improvement (an increase of 234%) for only 8.2% sensitivity decrease in comparison with the state-of-the-art score STOP-BANG. The fact that SASScore has bigger specificity makes it appropriate for OSAS screening and risk prediction in big, general populations.

| S-EPMC5426352 | biostudies-literature

Citizen science meets eDNA: A new boom in research exploring urban wetland biodiversity.

Project description:•eDNA citizen science provides a comprehensive picture of the biodiversity.•Non-native species reduced the local fish diversity in urban wetlands.•Expanding water area can improve wetland biodiversity.

| S-EPMC10196805 | biostudies-literature

Oral Anticoagulant Therapy-When Art Meets Science.

Project description:Anticoagulant treatment is extremely important and frequently encountered in the therapy of various cardiovascular diseases. Vitamin K antagonists (VKA) are in use for the prevention and treatment of arterial and venous thromboembolism, despite the introduction of new direct-acting oral anticoagulants (NOAC). The VKA still have the clear recommendation in patients with a mechanical prosthetic heart valve replacement or moderate to severe mitral stenosis of the rheumatic origin, in deep vein thrombosis associated with congenital thrombophilia, and in cases where NOAC are prohibited by social condition (financial reason) or by comorbidities (extreme weight, severe renal or liver disease). VKA dosing required to reach the targeted therapeutic range varies largely between patients (inter-individual variability). This inter-individual variability depends on multiple environmental factors such as age, mass, diet, etc. but it is also influenced by genetic determinism. About 30 genes implicated in the metabolism coumarins derivatives were identified, the most important being CYP2C9 and VKORC, each with several polymorphisms. Herein, we review the data regarding genetic alterations in general and specific populations, highlight the diagnosis options in particular cases presenting with genetic alteration causing higher sensitivity and/or resistance to VKA therapy and underline the utility of NOAC in solving such rare and difficult problems.

| S-EPMC6832236 | biostudies-literature

DrugRep-HeSiaGraph: when heterogenous siamese neural network meets knowledge graphs for drug repurposing.

Project description:BackgroundDrug repurposing is an approach that holds promise for identifying new therapeutic uses for existing drugs. Recently, knowledge graphs have emerged as significant tools for addressing the challenges of drug repurposing. However, there are still major issues with constructing and embedding knowledge graphs.ResultsThis study proposes a two-step method called DrugRep-HeSiaGraph to address these challenges. The method integrates the drug-disease knowledge graph with the application of a heterogeneous siamese neural network. In the first step, a drug-disease knowledge graph named DDKG-V1 is constructed by defining new relationship types, and then numerical vector representations for the nodes are created using the distributional learning method. In the second step, a heterogeneous siamese neural network called HeSiaNet is applied to enrich the embedding of drugs and diseases by bringing them closer in a new unified latent space. Then, it predicts potential drug candidates for diseases. DrugRep-HeSiaGraph achieves impressive performance metrics, including an AUC-ROC of 91.16%, an AUC-PR of 90.32%, an accuracy of 84.63%, a BS of 0.119, and an MCC of 69.31%.ConclusionWe demonstrate the effectiveness of the proposed method in identifying potential drugs for COVID-19 as a case study. In addition, this study shows the role of dipeptidyl peptidase 4 (DPP-4) as a potential receptor for SARS-CoV-2 and the effectiveness of DPP-4 inhibitors in facing COVID-19. This highlights the practical application of the model in addressing real-world challenges in the field of drug repurposing. The code and data for DrugRep-HeSiaGraph are publicly available at https://github.com/CBRC-lab/DrugRep-HeSiaGraph .

| S-EPMC10548718 | biostudies-literature

Cancer research: India meets the West.

Project description: Not available

| S-EPMC3172122 | biostudies-literature

Researcher reasoning meets computational capacity: Machine learning for social science.

Project description:Computational power and big data have created new opportunities to explore and understand the social world. A special synergy is possible when social scientists combine human attention to certain aspects of the problem with the power of algorithms to automate other aspects of the problem. We review selected exemplary applications where machine learning amplifies researcher coding, summarizes complex data, relaxes statistical assumptions, and targets researcher attention to further social science research. We aim to reduce perceived barriers to machine learning by summarizing several fundamental building blocks and their grounding in classical statistics. We present a few guiding principles and promising approaches where we see particular potential for machine learning to transform social science inquiry. We conclude that machine learning tools are increasingly accessible, worthy of attention, and ready to yield new discoveries for social research.

| S-EPMC10893965 | biostudies-literature

Advancing Science while Training Undergraduates: Recommendations from a Collaborative Biology Research Network.

Project description:Biology research is becoming increasingly dependent on large-scale, "big data," networked research initiatives. At the same time, there has been a corresponding effort to expand undergraduate participation in research to benefit student learning and persistence in science. This essay examines the confluence of this trend through eight years of a collaboration within a successful biology research network that explicitly incorporates undergraduates into large-scale scientific research. We draw upon interviews with faculty in this network to consider the interplay of scientific and pedagogical objectives at the heart of this undergraduate-focused network research project. We identify ways that this network has expanded and diversified access to scientific knowledge production for faculty and students and examine a goal conflict that emerged around the dual objectives of mentoring emerging scientists while producing high-quality scientific data for the larger biology community. Based on lessons learned within this network, we provide three recommendations that can support institutions and faculty engaging in networked research projects with undergraduates: (1) establish rigorous protocols to ensure data and database quality, (2) protect personnel time to coordinate network and scientific processes, and (3) select appropriate partners and establish explicit expectations for specific collaborations.

| S-EPMC8693944 | biostudies-literature

Academic-industry partnership advancing cannabis science: A Complementary Care Practice-Based Research Network.

Project description:ObjectivesData collected during routine care holds the potential to support hypothesis generation, study feasibility, and provide insight regarding how to address problems under real world conditions. Currently there are no practice-based research networks in Florida that focus on complementary care in general or medical marijuana specifically. Through an academic-industry partnership, we sought to develop a practice-based research network focused on cannabis science and create a de-identified database for analyses that support hypothesis generation, study feasibility estimation, and a network that also facilitates recruitment into future research studies.DesignThe Complementary Care Practice-Based Research Network (CC-PBRN) is a centralized repository, which contains electronic health records (EHR) from a private medical cannabis health system in the state of Florida.ResultsThis paper provides cross-sectional descriptive analyses of the CC-PBRN's 43,802 currently active patients. There are 24 clinics in the network with nine in North Florida, 11 in Central Florida, and 4 in South Florida.ConclusionThis network serves as a statewide resource for patient-reported outcomes (PRO) research in medical marijuana. The network currently has numerous outpatient practices with thousands of patients that are a potential source of research participants for both observational studies as well as traditional clinical trials. The outpatient clinical practices can also serve as units of analysis for pragmatic trials comparing different care protocols and organizational structure.

| S-EPMC10680467 | biostudies-literature

When Science Meets Creativity: Elevating Microbiology Education With Art-Two Personal Experiences.

Project description:Science communicators are more critical than ever in a time when misinformation and hoaxes dominate social media, especially during global emergencies like the COVID-19 outbreak. Researchers are good at communicating with peers but often struggle to explain complex ideas to the public. As shown previously, expanding outreach by combining science and art is not only possible, but effective. In the case of microbiology, the synergy between these two apparently divorced areas can help educating our communities and raising microbiological awareness, especially among students. Since Alexander Fleming's pioneering attempts, microorganisms have fascinated artists. Art may help us demystifying microbes and making them more approachable, improving public involvement with science. This article presents two personal experiences using art to teach and communicate scientific ideas (mainly from the microbiology field) to our fellow citizens. Above all, we emphasise the importance of contributing to the scientific literacy of our societies at all ages-including underrepresented and disadvantaged groups-by exploring novel ways to address this gap. By doing so, we align with the concepts and aims of the International Microbiology Literacy Initiative (IMiLI).

| S-EPMC11872682 | biostudies-literature

Theory Meets Empiry: A Citation Network Analysis.

Project description:According to a recent survey, ecologists and evolutionary biologists feel that theoretical and empirical research should coexist in a tight feedback loop but believe that the two domains actually interact very little. We evaluate this perception using a citation network analysis for two data sets, representing the literature on sexual selection and speciation. Overall, 54%-60% of citations come from a paper's own category, whereas 17%-23% are citations across categories. These cross-citations tend to focus on highly cited papers, and we observe a positive correlation between the numbers of citations a study receives within and across categories. We find evidence that reviews can function as integrators between the two literatures, argue that theoretical models are analogous to specific empirical study systems, and complement our analyses by studying a cocitation network. We conclude that theoretical and empirical research are more tightly connected than generally thought but that avenues exist to further increase this integration.

| S-EPMC6195343 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data