Veronica Venafra
@vero_venafra
PhD student in Bioinformatics at University of Rome Tor Vergata | Systems biology | Boolean modeling
ID: 882670890621816832
05-07-2017 18:41:54
16 Tweet
42 Followers
67 Following
It was great fun to use #signor data to decipher TKI-therapy #resistance mechanisms in FLT3-ITD positive #AML patients! I have such amazing collaborators!! Francesca Sacco Veronica Venafra Giorgia Massacci Martin Boettcher monia pugliese Natalie Krahmer biorxiv.org/content/10.110โฆ
Very proud to share this work of my group where we integrate #phosphoproteomics and our #signalingProfiler pipeline to unveil chemoresistance in #AML in collaboration with Livia Perfetto , Martin Boettcher and the haematological group @madgeburg!nature.com/articles/s4137โฆ
Up next is Veronica Venafra from Francesca Sacco lab who showcases her computational strategy to produce context specific networks of intracellular signalling, namely Signaling Profiler (available online!)
Last day at SysBioCurie, a short visiting full of great ideas and fruitful collaborations. Thanks a lot ๐ป๐งฌ
Our manuscript is out! nature.com/articles/s4138โฆ Proud of our curation effort to annotate #SFARI genes and #AutismSpetrumDisorders pathways in #SIGNOR! I am so grateful to all my collaborators, MartaIannuccelli, Alessandro Vitriolo, Gianni Cesareni, Giuseppe Testa et al! It was great!!
Our essay in Nat Cell Bio: "Bringing computation to biology by bridging the last mile" We argue to shift resources/rewards towards creating user-friendly software to bridge computation and biology-it yields huge returns! Shantanu Singh Free author link: rdcu.be/dv82F
Proud to share this new story from my group and Livia Perfettoโs team. Here we generate genotype specific predictive models of TKI resistance in FLT3 driven AML! Congrats Sara Latini Veronica Venafra!! doi.org/10.7554/eLife.โฆ
Happy to share our work in collaboration with Tommaso Schirinzi, Maria Wahle and Maxi Zwiebel from Matthias Mann Lab, Livia Perfetto and Veronica Venafra. Here, we show that blood cells, from Parkinson disease patients, display a stage-dependent remodelling of their phosphoproteome.
๐ Unlock the hidden signaling pathways within your multi-omic data! ๐ฌ SignalingProfiler 2.0 predicts protein activities and phenotypes and maps them into an intuitive network. ๐งฌ nature.com/articles/s4154โฆ #SystemsBiology #Multiomics Livia Perfetto Francesca Sacco Veronica Venafra