Sara Masarone (@StelMasara) header
Sara Masarone (@StelMasara) profile picture

Sara Masarone

@StelMasara

PhD student at @turinginst and @QMUL | Medical AI | Omics London

saramasarone.github.io
Joined May 2012
  • Tweets1.5K
  • Following574
  • Followers216
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Photo by Sara Masarone (@StelMasara)Photo by Sara Masarone (@StelMasara)Photo by Sara Masarone (@StelMasara)Photo by Sara Masarone (@StelMasara)Photo by Sara Masarone (@StelMasara)Photo by Sara Masarone (@StelMasara)Photo by Sara Masarone (@StelMasara)
Our most recent attempt to demystify AI for biomarker discovery - aimed at lab scientists and less experienced computational biologists, you can read it here link.springer.com/article/10…
The benefits and pitfalls of machine learning for biomarker discovery

The benefits and pitfalls of machine learning for biomarker discovery

Cell and Tissue Research - Prospects for the discovery of robust and reproducible biomarkers have improved considerably with the development of sensitive omics platforms that can enable measurement...

link.springer.com
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Sara Masarone retweeted
Owkin (@OwkinScience) profile picture
Owkin@OwkinScience26 Apr 2023
Meet PyDESeq2 – a Python-based software package for #RNAseq differential expression analysis based on DESeq2. Owkin is open-sourcing PyDESeq2 to support the growing community of bioinformaticians adopting Python for their data analysis workflows. Check out PyDESeq2 on GitHub at github.com/owkin/PyDESeq2 Read more at owkin.com/en/publications-an… #bioinformatics #python
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Sara Masarone retweeted
Michael Love (@mikelove) profile picture
Michael Love@mikelove27 Apr 2023
This is a really well done python reimplementation of DESeq2 and apeglm, I’ve checked the code closely. Nice work @BorisMuzc @Kramarek et al
Owkin (@OwkinScience) profile pictureOwkin@OwkinScience26 Apr 2023
Meet PyDESeq2 – a Python-based software package for #RNAseq differential expression analysis based on DESeq2. Owkin is open-sourcing PyDESeq2 to support the growing community of bioinformaticians adopting Python for their data analysis workflows. Check out PyDESeq2 on GitHub at github.com/owkin/PyDESeq2 Read more at owkin.com/en/publications-an… #bioinformatics #python
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Check out new work from @Inflammatix_Inc machine learning and assay development teams members @JRJB_C (while interning at Inflammatix), @DataForager and others! A glimpse into a pipeline for how we go from genes identified using public data to a clinical point-of-care test.
Jacob Bradley (@JRJB_C) profile pictureJacob Bradley@JRJB_C4 Feb 2023
New pre-print out! We look at how to optimise loop-mediated isothermal amplification (LAMP) assays, a fast and flexible alternative to PCR for DNA/RNA measurement. In particular, we look at how to use LAMP for quantitative measurement of gene expression. biorxiv.org/content/10.1101/…
Hierarchical Bayesian modeling identifies key considerations in the development of quantitative...

Hierarchical Bayesian modeling identifies key considerations in the development of quantitative...

Motivation Loop-mediated isothermal amplification (LAMP) is a rapidly growing, fast, and cost-effective technique for detection of DNA/RNA in point-of-care biomedical applications. However, it...

biorxiv.org
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Had a lot of fun at @NeurIPSConf last week! Join us today (online) at @lmrl_bio to learn about state of the art ML applications in biology/medicine/chemistry! #poster19
Image from Sara Masarone (@StelMasara)
Image from Sara Masarone (@StelMasara)
Image from Sara Masarone (@StelMasara)
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We have a wonderful lineup of EIGHT tutorials this year! 🎉 Be sure to check them out on Dec 10 and 11! You must be registered in the main LoG conference to participate. For more information: logconference.org/schedule-t…
Image from Learning on Graphs Conference 2026 (@LogConference)
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Sara Masarone retweeted
Patrick Schwab (@schwabpa) profile picture
Patrick Schwab
@schwabpa4 Dec 2022
You couldn't make it to #NeurIPS2022 this year? Nothing to worry - I curated a summary for you below focussing on key papers, presentations and workshops in the buzzing space of ML in Biology and Healthcare 👇
Image from Patrick Schwab (@schwabpa)
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The best part of #NeurIPS22 is obviously the significant boats gallery.
Image from billy bubba bingus-drumpfus (@wgrathwohl)
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Sara Masarone retweeted
Michael Love (@mikelove) profile picture
Michael Love@mikelove22 Nov 2022
Stefano Mangiola @steman_research & I are planning a tutorial on "Tidy Genomic Analysis" Dec 12, 4:30 PM EST Dec 13, 8:30 AM AEDT 60 min + 30 min Q/A - Tidy single-cell and bulk transcriptomics - Tidy enrichment with plyranges and nullranges Sign up: unc.zoom.us/meeting/register…
tidybulk workflow

ALT tidybulk workflow

plyranges and nullranges logos

ALT plyranges and nullranges logos

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🙏 re-tweet or apply! 📢📢A #DataScientist/#Bioinformatician post is available within @QMUL @SmedleyDamian's team to work on data from the @NIH - funded MorPhiC project (Molecular Phenotypes of Null Alleles in Cells) ** Deadline: 25 Nov 👇👇👇 qmul.ac.uk/jobs/vacancies/it…
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Thank you for coming! Hope to see you all at #biodata24!
Image from Michael Schatz (@mike_schatz)
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The first session today is on Machine Learning co-chaired by @drklly and @marinkazitnik #biodata22
Image from Michael Schatz (@mike_schatz)
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Next is Sara Masarone @turinginst with a talk on representation learning and its use to effectively integrate and interpret omics data #biodata22 openreview.net/forum?id=FRE7…
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Data integration as the key to making progress on understanding disease and improving patient outcomes
Image from Michael Schatz (@mike_schatz)
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Up next is @StelMasara discussing “Representation learning to effectively integrate and interpret omics data” #biodata22 saramasarone.github.io/

Currently: Senior ML Researcher/Engineer at Ignota Labs - 2023 - 2026

saramasarone.github.io
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Great talk by @marinkazitnik introducing GNNs applications in biomedicine and presenting the Therapeutics Data Commons initiative! @ProjectTDC #biodata22
Image from Sara Masarone (@StelMasara)
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Sara Masarone retweeted
Yingxin Lin (@LinYingxin) profile picture
Yingxin Lin@LinYingxin20 Jan 2022
Very excited that our latest work scJoint integrates atlas-scale single-cell RNA-seq and ATAC-seq data with transfer learning is finally out @NatureBiotech! go.nature.com/3FNNIm6 It is a joint work and a great collaboration between Wong Lab @Stanford and @sydneybioinfo
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NICE presentation from @StelMasara introducing a model based on #variational #autoencoders to integrate multiple data types (e.g. GE, DNAm) and regularizes the embeddings by a feature (e.g. gene or protein) known to be imp in that condition #biodata22 🔗 github.com/saramasarone/RMV-…
Image from Stephanie Hicks, PhD (@stephaniehicks)
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