DrugComb

A FAIR web portal for standardization, prediction and annotation of drug combination screening data (

[TangLab @NetPharMed]

How to improve the reproducibility of drug sensitivity screens? Make your data FAIR! Check our community-driven efforts here: https://t.co/me77lBoCC9
@EatrisEric @FIMM_UH @ncats_nih_gov @UCPH_BRIC @WennerbergLab @EoscLife @IMTM_Olomouc @EuOpenscreen @MarioNegriIRCCS

[TangLab @NetPharMed]

Can deep-learning improve the encoding of molecular structure of a drug to understand their interactions? Check on our comparative study! Congratulations on @b_zagidullin @epitkane @gyuanfan

[SynergyFinder @SynergyFinder]

Great resources for drug sensitivity data!

[DrugComb @DrugComb]

DrugComb update is out! Check more details at https://t.co/rr3In6phPD

[TangLab @NetPharMed]

I am speaking at MCBIOS and MAQC Joint 2021 Virtual Conference. Please checkout the details of my talk at: https://t.co/sYfq9vDcU3

[TangLab @NetPharMed]

DrugComb update paper is out! Much more drug sensitivity data resources and web applications are made available!
https://t.co/e3ELRgL7OB

[Emmy Verschuren @emmyverschuren]

Congrats to the team! This was fun and educational.

[TangLab @NetPharMed]

I am happy to be the guest editor for the special issue of 'Network Pharmacology Modelling for Drug Discovery' in the journal of Processes. Hope to receive more insights from you! https://t.co/YGRFZUKB63

[SynergyFinder @SynergyFinder]

SynergyFinder is updated! Please go to https://t.co/vkUgYZjGeZ to find more! @DrugComb @NetPharMed @DrugTargetCommons

[TangLab @NetPharMed]

Enlightening cancer drug discovery with challenge-winning AI solutions | University of Helsinki https://t.co/JwBcinSzIQ @DR_E_A_M @iCAN_Finland @FIMM_UH

[Mohieddin @MohieddinJafari]

Very happy that our work on the modeling of the concepts in traditional medicine with multipartite networks is out (https://t.co/uVRVwT9bsu ) @NetPharMed @DrugComb
@FrontiersIn @FrontPharmacol

[DrugComb @DrugComb]

We are looking for a talented post-doc to leverage the huge amount of drug combination/drug target interaction data to model the drug-target-disease networks! Apply by 30.9 via https://t.co/tBB3znvYiK

[DrugComb @DrugComb]

@ggonzalezp16 Hi sorry for late reply, the problem that was caused by the server was fixed and DrugComb is currently working fine
Please don't hesitate to contact me if you need any help
Jehad Aldahdooh
[email protected]

[Mohieddin @MohieddinJafari]

Very happy that our work on the ratio of coherency between the expression level and the causal relationships among the gene pairs is out(https://t.co/4iFDFahlyo)#mdpibiomolecules
"Can We Assume the Gene Expression Profile as a Proxy for Signaling Network Activity?" @Biomol_MDPI

[bioRxiv Microbiology @biorxiv_micrbio]

RNA Atlas of Human Bacterial Pathogens Uncovers Stress Dynamics Linked to Infection https://t.co/p4KPOwFKnw #biorxiv_micrbio

[DrugComb @DrugComb]

Packages used by DrugComb analysis service are updated under R (3.6.0) to:
- TidyComb https://t.co/N7FvStbmXG (version 0.0.5)
- SynergyFinder https://t.co/eAaOqiTkxb (version 2.0.3)

[DrugComb @DrugComb]

New single drug screen data from beatAML is uploaded to DrugComb.
There are 528 cell lines (new primary cultured AML cells), 123 molecules (4 new molecule),
and 47650 single drug dose response blocks.

[DrugComb @DrugComb]

DrugComb API is available right now. More details can be found in the following link:
https://t.co/Ls34mNcoLG

[DrugComb @DrugComb]

Single drug data is added to DrugComb comes from PharmacoDB dataset v1.1.1. It contains single drug dose-response experiments from 7 original datasets: UHNBreast, gCSI, CCLE, FIMM, GRAY, GDSC1000, CTRPv2. There are totally 650894 single drug-cell experiments.

[DrugComb @DrugComb]

DrugComb https://t.co/t78TnRBboQ via @YouTube
#DrugComb #Drug_combination_data_portal