Network pharmacology: From BioAssay Response Data to Network

•Télécharger en tant que PPTX, PDF•

0 j'aime•695 vues

Network pharmacology, comparing protein pharmacology networks built from Ligand based approach (Similarity Ensemble Approach) with those built from BioAssay response data.

Technologie Santé & Médecine

Network Pharmacology: From
BioAssay Response Data to Network

Bin Chen, Nikil Wale, Kevin McConnell
and Eric Gifford

Cite: Bin Chen, Kevin McConnell, Nikil Wale, David Wild, Eric Gifford, Comparing Bioassay Response and
Similarity Ensemble Approaches to Probing Protein Pharmacology, Bioinformatics

Network of top 2% most highly correlated pairs by BARD using Spearman (Node presents assay, two nodes are linked if their similarity reaches certain
threshold. Node is colored by gene family. Solid edges indicate common edges between two networks. Some non obvious links are highlighted in dashed
red oval.

Network of top 2% most highly correlated pairs by BARD using SEA (Node presents assay, two nodes are linked if their similarity reaches certain
threshold. Node is colored by gene family. Solid edges indicate common edges between two networks. Some non obvious links are highlighted in dashed
red oval.

5-HT7 & D1
common active 536
spearman 0.571
TC=0.84
SEA: 1.2e-25

Contenu connexe

Similaire à Network pharmacology: From BioAssay Response Data to Network

Prediction of protein function

Lars Juhl Jensen

Austin Neurology & Neurosciences is an open access, peer reviewed, scholarly journal dedicated to publish articles covering all areas of Neurology & Neurological Sciences. The journal aims to promote research communications and provide a forum for doctors, researchers, physicians and healthcare professionals to find most recent advances in all areas of Neurology & Neurological Sciences. Austin Neurology & Neurosciences accepts original research articles, reviews, mini reviews, case reports and rapid communication covering all aspects of neurology & neurosciences. Austin Neurology & Neurosciences strongly supports the scientific up gradation and fortification in related scientific research community by enhancing access to peer reviewed scientific literary works. Austin Publishing Group also brings universally peer reviewed journals under one roof thereby promoting knowledge sharing, mutual promotion of multidisciplinary science.

Austin Neurology & Neurosciences

Austin Publishing Group

Analysis of gene expression

university of education,Lahore

Gene Array Analyzer

university of education,Lahore

Microarray profiling by ujjwal sirohi

ujjwal sirohi

Virtual screening studies in search of dopamine D1 receptor ligands as antips...

Monika Marcinkowska

PhD midterm report

Davide Rambaldi

jin-HMG2014-post

Jin Yu

• It is a technique that predicts the interaction between a macromolecules and a chemical molecule. • Most of the existing efforts to identify the binding sites in protein-protein interaction are based on analyzing the differences between interface residues and non-interface residues, often through the use of machine learning or statistical methods. • Its major application is to Identify the protein ligand binding sites is an important process in drug discovery and structure based drug design. • Earlier, detecting protein ligand binding site is expensive and time consuming by traditional experimental method. Hence, computational approches provide many effective strategies to deal with this issue.

IDENTIFICATION OF PROTEIN BINDING SITE.docx

SNEHA AGRAWAL GUPTA

FunGen JC Presentation - Mostafavi et al. (2019)

BrianSchilder

"Non-coding RNA mediated epigenetic regulation of agronomic traits in crop pl...

NileshJoshi74

Disintegration of the small world property with increasing diversity of chemi...

N. Sukumar

Friend NIEHS 2013-03-01

Sage Base

Gutell 114.jmb.2011.413.0473

Robin Gutell

For mRNA quantification, RT-PCR and DNA microarrays have been compared in few studies (RT-PCR). Healing callus of adult and juvenile rats after femur injury was found to be rich in mRNA at various stages of the healing process. We used both methods to examine ten samples and a total of 26 genes. Internal DNA probes tagged with 32P were employed in reverse transcription-polymerase chain reaction (RT-PCR) to identify genes (RT-PCR). Ten Affymetrix® Rat U34A cRNA microarrays were hybridized with biotin-labeled cRNA generated from mRNA. There was a wide range of correlation coefficients (r) between RT-PCR and microarray data for each gene. Meaning became genetically unique because of this diversity. Relatively lowly expressed genes had the highest r values. The distance between PCR primers and microarray probes was found to be higher than previously assumed, leading to a drop in agreement between microarray calls and PCR outcomes. Microarray research showed that RT-PCR expression levels for two genes had a "floor effect." As a result, PCR primers and microarray probes that overlap in mRNA expression levels can provide good agreement between these two techniques.

RT-PCR and DNA microarray measurement of mRNA cell proliferation

IJAEMSJORNAL

As increasing numbers of people choose to have their genomes sequenced and made available for research, more genomic data is available for analysis by machine learning approaches. Single Nucleotide Polymorphisms (SNPs) are known to be a major factor influencing many physical traits, diseases and other phenotypes. Using publicly available data and tools we predict phenotype from genotype using SNP data (1 to 2 million SNPs). We utilize data analysis and machine learning approaches only, no domain knowledge, so that our automated approach may be generally used to predict different phenotypes from genotype. In the first application of our method we predicted eye color with 87% accuracy.

Predicting phenotype from genotype with machine learning

Patricia Francis-Lyon

Topological analysis of coexpression networks in neoplastic tissues (BITS2012...

Roberto Anglani

University of Texas at Austin

butest

Structure and function of dna

UsmanShahzad1977

RAnalysis

Boshika Tara

Similaire à Network pharmacology: From BioAssay Response Data to Network (20)

Prediction of protein function

Austin Neurology & Neurosciences

Analysis of gene expression

Gene Array Analyzer

Microarray profiling by ujjwal sirohi

Virtual screening studies in search of dopamine D1 receptor ligands as antips...

PhD midterm report

jin-HMG2014-post

IDENTIFICATION OF PROTEIN BINDING SITE.docx

FunGen JC Presentation - Mostafavi et al. (2019)

"Non-coding RNA mediated epigenetic regulation of agronomic traits in crop pl...

Disintegration of the small world property with increasing diversity of chemi...

Friend NIEHS 2013-03-01

Gutell 114.jmb.2011.413.0473

RT-PCR and DNA microarray measurement of mRNA cell proliferation

Predicting phenotype from genotype with machine learning

Topological analysis of coexpression networks in neoplastic tissues (BITS2012...

University of Texas at Austin

Structure and function of dna

RAnalysis

Dernier

Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood

Juan lago vázquez

Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...

Zilliz

💥 You’re lucky! We’ve found two different (lead) developers that are willing to share their valuable lessons learned about using UiPath Document Understanding! Based on recent implementations in appealing use cases at Partou and SPIE. Don’t expect fancy videos or slide decks, but real and practical experiences that will help you with your own implementations. 📕 Topics that will be addressed: • Training the ML-model by humans: do or don't? • Rule-based versus AI extractors • Tips for finding use cases • How to start 👨‍🏫👨‍💻 Speakers: o Dion Morskieft, RPA Product Owner @Partou o Jack Klein-Schiphorst, Automation Developer @Tacstone Technology

DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam

UiPathCommunity

Following the popularity of “Cloud Revolution: Exploring the New Wave of Serverless Spatial Data,” we’re thrilled to announce this much-anticipated encore webinar. In this sequel, we’ll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR. Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios. Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects. Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you’re building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Safe Software

Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving

Edi Saputra

Accelerating FinTech Innovation: Unleashing API Economy and GenAI Vasa Krishnan, Chief Technology Officer - FinResults Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...

apidays

[BuildWithAI] Introduction to Gemini.pdf

Sandro Moreira

"I see eyes in my soup": How Delivery Hero implemented the safety system for ...

Zilliz

FWD Group - Insurer Innovation Award 2024

The Digital Insurer

AXA XL - Insurer Innovation Award Americas 2024

The Digital Insurer

In the thrilling conclusion to 2023, ransomware groups had a banner year, really outdoing themselves in the "make everyone's life miserable" department. LockBit 3.0 took gold in the hacking olympics, followed by the plucky upstarts Clop and ALPHV/BlackCat. Apparently, 48% of organizations were feeling left out and decided to get in on the cyber attack action. Business services won the "most likely to get digitally mugged" award, with education and retail nipping at their heels. Hackers expanded their repertoire beyond boring old encryption to the much more exciting world of extortion. The US, UK and Canada took top honors in the "countries most likely to pay up" category. Bitcoins were the currency of choice for discerning hackers, because who doesn't love untraceable money?

Ransomware_Q4_2023. The report. [EN].pdf

Overkill Security

Axa Assurance Maroc - Insurer Innovation Award 2024

The Digital Insurer

Architecting Cloud Native Applications

WSO2

Dubai, known for its towering skyscrapers, luxurious lifestyle, and relentless pursuit of innovation, often finds itself in the global spotlight. However, amidst the glitz and glamour, the emirate faces its own set of challenges, including the occasional threat of flooding. In recent years, Dubai has experienced sporadic but significant floods, disrupting normalcy and posing unique challenges to its infrastructure. Among the critical nodes in this bustling metropolis is the Dubai International Airport, a vital hub connecting the world. This article delves into the intersection of Dubai flood events and the resilience demonstrated by the Dubai International Airport in the face of such challenges.

Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf

Orbitshub

Corporate and higher education. Two industries that, in the past, have had a clear divide with very little crossover. The difference in goals, learning styles and objectives paved the way for differing learning technologies platforms to evolve. Now, those stark lines are blurring as both sides are discovering they have content that’s relevant to the other. Join Tammy Rutherford as she walks through the pros and cons of corporate and higher ed collaborating. And the challenges of these different technology platforms working together for a brighter future.

Corporate and higher education May webinar.pptx

Rustici Software

EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER

MadyBayot

ICT role in 21st century education and its challenges

rafiqahmad00786416

The microservices honeymoon is over. When starting a new project or revamping a legacy monolith, teams started looking for alternatives to microservices. The Modular Monolith, or 'Modulith', is an architecture that reaps the benefits of (vertical) functional decoupling without the high costs associated with separate deployments. This talk will delve into the advantages and challenges of this progressive architecture, beginning with exploring the concept of a 'module', its internal structure, public API, and inter-module communication patterns. Supported by spring-modulith, the talk provides practical guidance on addressing the main challenges of a Modultith Architecture: finding and guarding module boundaries, data decoupling, and integration module-testing. You should not miss this talk if you are a software architect or tech lead seeking practical, scalable solutions. About the author With two decades of experience, Victor is a Java Champion working as a trainer for top companies in Europe. Five thousands developers in 120 companies attended his workshops, so he gets to debate every week the challenges that various projects struggle with. In return, Victor summarizes key points from these workshops in conference talks and online meetups for the European Software Crafters, the world’s largest developer community around architecture, refactoring, and testing. Discover how Victor can help you on victorrentea.ro : company training catalog, consultancy and YouTube playlists.

Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024

Victor Rentea

Exploring Multimodal Embeddings with Milvus

Zilliz

2024: Domino Containers - The Next Step. News from the Domino Container commu...

Martijn de Jong

Dernier (20)

Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood

Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...

DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving

Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...

[BuildWithAI] Introduction to Gemini.pdf

"I see eyes in my soup": How Delivery Hero implemented the safety system for ...

FWD Group - Insurer Innovation Award 2024

AXA XL - Insurer Innovation Award Americas 2024

Ransomware_Q4_2023. The report. [EN].pdf

Axa Assurance Maroc - Insurer Innovation Award 2024

Architecting Cloud Native Applications

Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf

Corporate and higher education May webinar.pptx

EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER

ICT role in 21st century education and its challenges

Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024

Exploring Multimodal Embeddings with Milvus

2024: Domino Containers - The Next Step. News from the Domino Container commu...

Network pharmacology: From BioAssay Response Data to Network

1. Network Pharmacology: From BioAssay Response Data to Network Bin Chen, Nikil Wale, Kevin McConnell and Eric Gifford Cite: Bin Chen, Kevin McConnell, Nikil Wale, David Wild, Eric Gifford, Comparing Bioassay Response and Similarity Ensemble Approaches to Probing Protein Pharmacology, Bioinformatics

9. Network of top 2% most highly correlated pairs by BARD using Spearman (Node presents assay, two nodes are linked if their similarity reaches certain threshold. Node is colored by gene family. Solid edges indicate common edges between two networks. Some non obvious links are highlighted in dashed red oval.

10. Network of top 2% most highly correlated pairs by BARD using SEA (Node presents assay, two nodes are linked if their similarity reaches certain threshold. Node is colored by gene family. Solid edges indicate common edges between two networks. Some non obvious links are highlighted in dashed red oval.

11.

12.

13.

14.

15.

16.

17.

18. 5-HT7 & D1 common active 536 spearman 0.571 TC=0.84 SEA: 1.2e-25

Network pharmacology: From BioAssay Response Data to Network

Recommandé

Recommandé

Contenu connexe

Similaire à Network pharmacology: From BioAssay Response Data to Network

Similaire à Network pharmacology: From BioAssay Response Data to Network (20)

Dernier

Dernier (20)

Network pharmacology: From BioAssay Response Data to Network