Daniel Marbach

[Daniel Marbach @danmarbach]

Proud of our @DR_E_A_M Challenge paper dissecting methods to identify disease modules in molecular networks https://t.co/EwsjmHAQDE

Joint work of >400 participants, data providers and organizers... collaborative science at its best, honored to be part!

[Nature Methods @naturemethods]

This @DR_E_A_M challenge compares 75 methods for the identification of disease modules from network data. Users will learn about practical guidelines for how to choose a method and about benchmarks for the analysis.
https://t.co/UgvEh2qlb8

[Daniel Marbach @danmarbach]

https://t.co/EwsjmHAQDE Lesson #5: Network modules reveal core disease genes and pathways.

While 1000s of genes may show disease association in GWAS, much more specific disease modules comprising only dozens of genes can be identified within networks.

[Daniel Marbach @danmarbach]

Proud of our @DR_E_A_M Challenge paper dissecting methods to identify disease modules in molecular networks https://t.co/EwsjmHAQDE

Joint work of >400 participants, data providers and organizers... collaborative science at its best, honored to be part!

[Daniel Marbach @danmarbach]

https://t.co/EwsjmHAQDE Lesson #4: First, identify modules in each network individually, without merging them.

Methods attempting to reveal integrated modules across networks failed to significantly improve predictions, likely because our networks were not sufficiently related.

[Suhaib Mohammed @SuhaibMo]

Always wanted to explore this https://t.co/4S58rbMa2r

[Sarvenaz Choobdar @datascience_ch]

Provided unpublished molecular networks by different collaborators were essential success of our @DR_E_A_M challenge on Disease Module Identification. 👏👏👏 https://t.co/g6wH05a2xg

[Enrico Ferrero @enricoferrero]

Fantastic @DR_E_A_M preprint describing results of the disease network module identification challenge:
https://t.co/MHdTsTkulv
#bioinformatics #machinelearning

[Saez-Rodriguez Group @saezlab]

Choobdar et al. - Disease Module Identification @DR_E_A_M Challenge outcome w. help from us & use of @omnipathdb https://t.co/irPzZieT7g

[Chanchal Kumar @kumarchanchal]

An interesting outcome of Disease Module Identification DREAM Challenge.  https://t.co/b96hMXROZU https://t.co/wnxtyJ5d0W

[Zoltán Kutalik @zkutalik]

When crowd-surfed molecular networks are married with GWAS we can have a high-resolution glimpse into disease biology https://t.co/B6f5m928oz

[Sarvenaz Choobdar @datascience_ch]

Our #dreamchallenge paper will be soon submitted to @CellCellPress. Awsome internatinal collaboration. #MachineLearning #NetworkScience#Biologicaldata https://t.co/fYXn6oEKVL

[Santo Fortunato @santo_fortunato]

The paper of the DREAM Challenge on molecular network modules is on bioarxiv. Proud to be part of the DREAM Team! https://t.co/UqQ6BqsTVd

[Himanshu Sinha @Himanshu_IITM]

Dream Challenge - Disease Module Identification preprint is out. Proud of our students who contributed to this effort @BeethikaT @karthikalam @karthikraman @ravi_iitm @IBSE_IITM @rbc_dsai_iitm @iitmadras https://t.co/WRWfeEXVFz

[Dr. Raghvendra Mall 🇮🇳🇧🇪🇶🇦🇺🇲🇦🇪👨‍💻 @MallRaghvendra]

Glad to be part of the DREAM Consortium for the contribution titled "Open Community Challenge Reveals Molecular Network Modules with Key Roles
in Diseases"
https://t.co/l7Yivs8A40

[Daniel Marbach @danmarbach]

Our latest preprint @DR_E_A_M:
Open Community Challenge Reveals Molecular Network Modules with Key Roles in Diseases
https://t.co/YbVLrf82cG

[Daniel Marbach @danmarbach]

@humancellatlas funded by #CZI. To know more than press release, I recommend these talks, #compbio will be key: https://t.co/4HUjpQUQkB

[Daniel Marbach @danmarbach]

Our paper on GWAS gene/pathway scoring ranks in top 50 #plosmostdownloaded out of 50mio downloads from @PLOS in 2016
https://t.co/dkqVthHg3a