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Shashi, M.S. Profile
Shashi, M.S.

@shashi_lab

Followers
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Following
249
Media
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Statuses
114

Ph.D. Student @RutgersU | Alumni @UOGTriton | Metabolomics of Bacteria, Plant, Human | All views & posts are my own.

Joined January 2025
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@shashi_lab
Shashi, M.S.
4 months
“Times are changing but order matters: Transferable prediction of small molecule liquid chromatography retention times” https://t.co/kzPuofRhgp
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@shashi_lab
Shashi, M.S.
5 months
First @MetabolomicsSoc 2025 conference and not the last. Great experience seeing the applications, translational research, and developments in the field. Crazy to meet and see many of the pioneers. Great vibes all around!! Dr. Wu and I presenting a small piece of my
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@MetabolitesMDPI
Metabolites MDPI
6 months
✨✨Meet Us at the 21st Annual International #Conference of the Metabolomics Society (#Metabolomics2025)! 🎉 📅 Date: 22–26 June 2025 📍 Location: Prague, Czech Republic 🏷️ #MetabolitesMDPI #MetabolomicsResearch 🔗 For More Info: https://t.co/wH32N8PPqy
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@biocrates_life
biocrates life sciences
6 months
🚀 Headed to #Metabolomics2025? Visit us June 22 - 26 in Prague!📍 Booth #G1 | Discover the new MxP® Quant 1000 kit | 🧩 Join the online jigsaw puzzle #challenge | 🎤 Join our vendor talk & #posterpresentations | More info on our Metabolomics 2025 page 👉 https://t.co/z3bv7dmLvz
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@tAnaSci
The Analytical Scientist
6 months
🧪 Is the dark metabolome just noise? In Part 1 of our 5-part series, El Abiead & Dorrestein push back on claims that most LC-MS/MS features are fragments — and explain why this matters for science & careers. 🔗 Read: https://t.co/JbUiCpQ6MK #Metabolomics #MassSpec
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@selcukorkmaz
Selçuk Korkmaz
6 months
🚀 Ever tried training multiple ML models in one line of code? With fastml, you can: fastml(data = df, label = "target") 🎯 Auto preprocessing 📊 Compare models instantly 🧠 SHAP, ROC, accuracy & more Perfect for fast prototyping in R! 💻⚡️ #rstats #MachineLearning
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@shashi_lab
Shashi, M.S.
6 months
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@MetabolitesMDPI
Metabolites MDPI
6 months
🌟 Editor's Choice Paper 📖 #Bridging #Ethnobotanical Knowledge and Multi-Omics Approaches for #Plant-Derived #Natural #Product Discovery 🧑🏻‍🔬By Prof. Justin J.J. van der Hooft, Dr. Fidele Tugizimana, et al. 🔗 https://t.co/oUATZtpQLv
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@AIHealthMIT
MIT Jameel Clinic for AI & Health
6 months
Delighted to announce the release of Boltz-2, which demonstrates unprecedented accuracy in predicting structure and binding affinity! Congrats to @GabriCorso and @jeremyWohlwend on this breakthrough achievement! 📄Paper: https://t.co/UFInT5vDf0 💻Code: https://t.co/4J6ldsgNgM
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@shashi_lab
Shashi, M.S.
6 months
If anyone is attending the @MetabolomicsSoc conference in Prague, let me know I would love to connect!!
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@shashi_lab
Shashi, M.S.
6 months
I was also 1/2 recipients to receive the Steve Berger travel award from @Agilent. Amazing hospitality from the team and had the opportunity to demo pre-product MassHunter Explorer 2.0!
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@shashi_lab
Shashi, M.S.
6 months
My first #ASMS2025 was an incredible experience where I presented “Restoration of Antibiotic-Impacted Microbiome in Human Babies: Pilot Metabolomic Study on the Efficacy of Autologous Fecal Microbiota Transplantation” Huge thanks to Dr. Maria Gloria Dominguez Bello, Dr. James
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@shashi_lab
Shashi, M.S.
6 months
Major update for untargeted workflows using Agilent’s MassHunter Explorer.. annotation by MS/MS, custom libraries, and integrating SIRIUS for unknowns #ASMS2025
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@kadzuis
Gary Siuzdak
6 months
An AI BioSync rendition of METLIN 960K as a dot matrix. Celebrating the creation of METLIN with 960,000 molecular standards, and going live in its original classic form - reengineered. 🔗 Sign in for access: https://t.co/mXamURkHde 📖 Read more: DOI: 10.1002/ansa.70012
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@DemichevLab
Vadim Demichev
6 months
DIA-NN 2.2.0 is released! In this update we have focused on analysis speed, which is becoming increasingly critical given the rapid transition to high-throughput proteomics. The new DIA-NN 2.2.0 Enterprise achieves up to ~1.6x median speedup on 64-cores under Windows (please see
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@shashi_lab
Shashi, M.S.
6 months
I think this will change many things. adds a new dimension for a metabolomist. Surprised (but not surprised cause of age of the field) this has only just been done.. Mass spec data is ultimately continuous data. Accelerate
@NatureBiotech
Nature Biotechnology
7 months
Self-supervised learning of molecular representations from millions of tandem mass spectra using DreaMS https://t.co/G1RMCWVA50
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@shashi_lab
Shashi, M.S.
6 months
🚨🚨
@SabinehazanMD
sabine hazan md
7 months
Me in a cage 🤣🤣You know me better than that @houmanhemmati. The advantage I have is I am a clinician seeing the microbiome in action and he is not. To understand the microbiome, one needs to reach a cure. To reach a cure one needs to practice the art of medicine. Case of
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@shashi_lab
Shashi, M.S.
6 months
Cholesterol binds to orthosteric binding pocket in hTAS2R14 due to hydrophobic interactions, now lets take a look at other bitter-related compounds... cc: @masha_niv, might be of your interest!
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@shashi_lab
Shashi, M.S.
6 months
Bitter taste receptor activation by cholesterol and an intracellular tastant https://t.co/9x2E3bZ3XM
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@NatureBiotech
Nature Biotechnology
7 months
Self-supervised learning of molecular representations from millions of tandem mass spectra using DreaMS https://t.co/G1RMCWVA50
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