Simona De Summa

Senior Researcher, Bioinformatician and data scientist involved in translational cancer research. IRCCS-Istituto Tumori Giovanni Paolo II, Bari, Italy

[Top Science @isciverse]

The importance of stupidity in scientific research

#PhD #PhDlife #academics #AcademicChatter #AcademicTwitter #Postdocs #Research #Science

[Simona De Summa @SimonaSumma]

Our very first presentation of spatial transcriptomics data to unveil the risk of recurrence in stage II colon cancer at @siapec.
An incredible team in an inspirational meeting.
See you soon!

[Patologia Molecolare e Medicina di Precisione @pmmpitalia]

Hot topic per la prima giornata del congresso #SIAPeC-IAP :
🔬 "Patologia #Molecolare e Medicina di Precisione in #Oncologinecologia" 🧬discusso da due relatori d'onore @FabioPagni1 @UmbertoMalapel1 💯

@pmmpitalia

[Simona De Summa @SimonaSumma]

The trip in the spatial context of early stage colon cancer is far to be concluded.
Single cell transcriptomes and spatial coordinates are clearly evidencing hidden biological processes responsible for metastatization.
Stay tuned…

[dana_peer @dana_peer]

One needs to a solid understanding of statistics, knowledge and awareness of artifacts, and there is no substitution for experience, no single course teaches this. I always encourage everyone to consult with experienced computational colleagues on new pipelines

[dana_peer @dana_peer]

I particularly like this paper: Demonstrating (1) The power of perturbing a cell-subset to understand the cell-cell interactions in the TME. (2) Using mouse derived signatures to query human clinical data and (3) Using gene programs to suggest combinatorial therapy approaches.

[Simona De Summa @SimonaSumma]

@Dio D’altronde avevi detto dei bambini “lasciate che vengano a me”… dei bambini mica dei giovani uomini

[bioRxiv Bioinfo @biorxiv_bioinfo]

Genesis: A Modular Protein Language Modelling Approach to Immunogenicity Prediction https://t.co/AjGzlbAEJQ #biorxiv_bioinfo

[Simona De Summa @SimonaSumma]

The very beginning of our spatial journey!
#spatialtranscriptomics #10X #visium #coloncancer

[Davide Cittaro @daweonline]

Kudos to @ValentinaGiansa for this and thanks to all people involved!
👇👇👇
Scalable Integration of Multiomic Single Cell Data Using Generative Adversarial Networks https://t.co/D0pzC7iaFT

[Simona De Summa @SimonaSumma]

See you with new vision for the future.
#AACR2024

[Simona De Summa @SimonaSumma]

Not only AI.
“Sometimes when you want to understand complex messy biological data, simple rigorous statistics infused with biological understanding allows you to find structure in this biological data and understand what’s going on”
Thank you @dana_peer
#AACR2024

[Simona De Summa @SimonaSumma]

Very glad for the interest on my poster during yesterday’s poster session.
Computational methods for drug repurposing are a very hot topic.
#AACR2024

[Renato Ostuni @OstuniLab]

AI is data hungry and in biology we might not (yet) have enough to be fully reliable. Results of any computational analysis should resonate with biology and be robust when parameters are changed - couldn't agree more with @dana_peer and her vision #AACR24

[Simona De Summa @SimonaSumma]

A dreamer and a Nobel Prize!
@JimAllisonPhD #AACR2024

[Simona De Summa @SimonaSumma]

Female computational biologists rule!
#AACR2024

[dana_peer @dana_peer]

the greatest computational biologist of them all Aviv Regev how is the opening plenary of #AACR24, female computational biologists are transforming cancer and Aviv is rocking it

[Simona De Summa @SimonaSumma]

#AACR2024
The very beginning.

[Simona De Summa @SimonaSumma]

#AACR2024
Here we go!

[Dr. Jean Fan @JEFworks]

Just finished teaching another semester of genomic #datavisualization 🥳

Discover the awesome #dataviz made by students in the class for various #singlecell #spatialtranscriptomics data

Check out the course notes + #Rstats code to explore for yourself: https://t.co/eikhx46fOb

[Yüksel Ürün @DrYukselUrun]

Can AI crack the code of tissue images?
🔬🔬🔬UNI, a powerful AI model, analyzes millions of tissue images like a pro, trained on a massive dataset across 20 tissue types. This could revolutionize diagnoses with faster, more accurate results! #AI #healthcare #pathology
@oncoalert @NatureMedicine @harvardmed @BrighamWomens @broadinstitute
https://t.co/FfnOSqlADm