Gerard Baquer

Postdoc @BWHNeurosurgery @HarvardMed - computational cancer biology, spatial multiomics 💻🦠💊 "All models are wrong, some are useful"

[Gerard Baquer @GerardBaquer]

Editors and authors👋! I'm looking to review more papers in spatial omics🔬, especially in computational integration and analysis of spatial metabolomics, proteomics, and transcriptomics💻. Please RT and DM! @imaging_ms @IMSSNews @SpatialOmics

[Daniel Wahl @DanWahlMD]

New discovery from our team defining how human brain cancers rewire the metabolism of the normal brain published today @Nature and led by @AndrewScottPhD https://t.co/Ij95esKmd3

[SEES Lab @sees_lab]

New paper out in @OxfordJournals Briefings in Bioinformatics

📰SingleFrag: a deep learning tool for MS/MS fragment and spectral prediction and metabolite annotation https://t.co/EhQqa3S5WE

[Ming "Tommy" Tang @tangming2005]

GSEA is one of the highly cited methods (> 50K! ) for gene set enrichment analysis. Do you really understand it?

[Jeffrey West @mathoncbro]

AI-driven 3D Spatial Transcriptomics
https://t.co/2HbfgFauNU

[Nature Reviews Cancer @NatureRevCancer]

🥳 Our 3rd most downloaded article in 2024:

A guide to artificial intelligence for cancer researchers by @RaqPerezLopez, @GhaffariNarmin, @AI4Pathology & @jnkath

https://t.co/qF6cvIZQxo

@Medizin_TUD, @NCT_HD, @DataScienceDKFZ, @katherlab
@NatResCancer #NRCtop10of2024

[Oscar Yanes @yaneslab]

🚀 New paper alert! 🚀 Happy to introduce #ChemEmbed, a deep learning framework for metabolite identification that enhances MS/MS data and leverages multidimensional molecular embeddings. A 🧵 on how it works ⬇️ #metabolomics #MachineLearning #DeepLearning

[Lukas Valihrach @LukasValihrach]

SpatialLeiden: spatially aware Leiden clustering
https://t.co/tIOkhzWDms

[Gerard Baquer @GerardBaquer]

@guillemsimeon Molt bé Guillem. Que vagi tot genial.

[shako @shakoistsLog]

Ending a Claude instance that helped you deal with some real shit in your life when the context has become too long and it's starting to go haywire.

[Lukas Valihrach @LukasValihrach]

Impact of Segmentation Errors in Analysis of Spatial Transcriptomics Data👍 https://t.co/Mwojk4qTEE

[antisense. @razoralign]

CORAL: Learning single-cell spatial context through integrated spatial multiomics https://t.co/bVUs4MpnsN

[BSC-CNS @BSC_CNS]

⚛️💻BSC presents the first quantum computer in Spain developed with 100% European technology

🌐The new system will be available to the research community, companies & public organisations in Spain, thus strengthening technological & industrial development throughout the country

➡https://t.co/eGMNh2eFwV

#NextGenerationEU #PlandeRecuperación

[Nature Reviews Cancer @NatureRevCancer]

🥳 Our 8th most downloaded article in 2024:

Exploiting temporal aspects of cancer immunotherapy by Lesterhuis et al.
https://t.co/zvWsL1KnyB

#immunotherapy #cancermicroenvironment

@UWAresearch @UWnews@NatResCancer #NRCtop10of2024

[Nature Reviews Cancer @NatureRevCancer]

🥳 Our 9th most downloaded article in 2024:

Tertiary lymphoid structures in anticancer immunity by @DieuNosjean et al.
https://t.co/76MlP0L8m9

#immunotherapy #cancermicroenvironment #tumourimmunology

@Inserm @NatResCancer #NRCtop10of2024

[Neil Renic @NC_Renic]

If everyone used footnotes instead of endnotes

[Reviewer 2 @GrumpyReviewer2]

#AcademicChatter #AcademicX

[Fabian Theis @fabian_theis]

‚Foundation models for biology‘ are one of 6 techs to watch in 2025 according to @Nature. Agreed, but not sure yet how to evaluate at scale. ie what is ‚general biological intelligence’? https://t.co/55G8Ty9ojx

[Neil Renic @NC_Renic]

Your first draft doesn't need to be perfect, it needs to be a complete pile of garbage that makes you question your worth as a person

[Pranam Chatterjee @pranamanam]

Okay, another VERY exciting paper drop! 🎉📜 So you guys may know the story of how @GametoGen was started? 🐣 My part of the story begins with a visit to @geochurch at @harvardmed, with a (perhaps naive) goal: to make an oocyte from a stem cell. 🧫 With @GametoGen's support, I started working on it together with @krammecc, and later with @mpsmela1. 5 years later, after many rejections and sleepless nights of experiments/analysis, our paper describing oogonia differentiation from iPSCs is finally published in @emboreports! 🎉

📜:https://t.co/j7M2zfRZdG

Ever since the generation of iPSCs from somatic cells, stem cell differentiation to different cell types has been a major challenge. Some of the toughest cells to make are gametes (oocytes and sperm) -- they have crazy methylation dynamics, they are haploid, and they can obviously produce offspring !👶 The search space for the right cocktail of molecules and transcription factors (TFs) to make these is complicated and vast -- a perfect problem for an algorithm! 💻

As a part of our STAMPScreen pipeline (https://t.co/VV7VeJjm6t), we developed an adaptive PageRank-based algorithm that could rank "central" transcription factors in the gene regulatory networks of differentiating cells. 📊 After screening the top 46 TFs ID'ed for oocyte production in iPSCs (while also pushing them through primordial germ cell-like cell (hPGCLC) formation), we identified 3 that both independently and together (D3), drove DDX4+ oogonia formation, as well as 3 that drove improved hPGCLC formation! 🤩

We have since showed that cells we engineered with these factors, termed induced oogonia-like cells (iOLCs), can be generated in just FOUR DAYS (not like the previous FOUR MONTH protocol) and exhibit late-stage oogonia-like gene expression, epigenetics, and protein markers. 🧫 They are defined by the expression of mitotic oogonia markers (DDX4, DAZL, and KI67) and the absence of earlier primordial germ cell and pluripotency markers (OCT4 and SOX17). Remarkably, these iOLCs are capable of feeder-free expansion culture while retaining their oogonia-like identity post-sorting, making this a super rapid, scalable, and robust system to study human germline development! 💨

Honestly, this was only possible via my incredible partnership with @krammecc (now the CSO at @GametoGen!) and our brilliant collaboration with @mpsmela1, as well as with @Patrick11F, my @DukeU undergrad Shrey + the entire team! 🙌 It was just sheer perseverance for years as we CONSTANTLY got rejected by journals and had to keep adding new data and analysis (I thought it would never end). We were also so lucky to have the guidance of the Shioda Lab at @MassGenBrigham and the team at the @wyssinstitute through the process! 🤝Just so many great scientists who put their everything into this! 🫶

I know a lot of you know me as a peptide/protein designer (or probably just as the sequence-only guy), but this shows you how impactful good algorithms can be! Who would have guessed a graph theory algorithm could be used to make human oogonia?! 🙃 I'm just really excited for my lab to continue work in this space and make an impact in reproductive medicine with our new generative models! 💻

I hope you can take a read of our paper and let us know your thoughts! 🙏

[Xingyi He @XingyiHe1]

Excited to share our work MatchAnything:
We pre-train strong universal image matching models that exhibit remarkable generalizability on unseen multi-modality matching and registration tasks.

Project page: https://t.co/o5GisUJ7RT
Huggingface Demo: https://t.co/qbz33QBulI