Marinka Zitnik (@marinkazitnik) / X

Marinka Zitnik

3,041 posts

Marinka Zitnik

@marinkazitnik

Associate Professor at Harvard | @Harvard @KempnerInst @broadinstitute | @ProjectTDC @AI_for_Science @ScientistTools | openscientist.ai

Harvard

zitniklab.hms.harvard.edu

Joined July 2009

Marinka Zitnik
@marinkazitnik
Jan 22, 2023
Excited to teach a course on #biomedical #AI this semester @Harvard @harvardmed Follow our course materials, reading lists and paper highlights which we will release throughout the semester zitniklab.hms.harvard.edu/BMI702 Thankful for star TA team @YEktefaie @richardjchen Y Huang
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Marinka Zitnik
@marinkazitnik
Oct 31, 2022
Deep learning on #graphs is poised to address major gaps in biology and medicine In Nature Biomed Eng @natBME, we describe the next generation of #graphAI #GNN methods and opportunities that build models from data using structure, geometry & knowledge rdcu.be/cYE8G
Marinka Zitnik
@marinkazitnik
Aug 2, 2023
Excited to share our @Nature paper on the role of AI in scientific discovery 🌟🔬 #AI4Science AI is transforming discovery across sciences 🤖🔍 From hypothesis generation to data interpretation, it is reshaping all stages of research in ways we could not imagine using
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Marinka Zitnik
@marinkazitnik
Jul 5, 2022
Can we infuse #structure into a time series (#TS) model from a diverse dataset so as to greatly improve #generalization on new TS coming from different datasets? Yes, via a new principle called #Representational Time-Frequency Consistency (TF-C) arxiv.org/abs/2206.08496 1/3
Marinka Zitnik
@marinkazitnik
Jul 1, 2025
📢 AI-enabled drug discovery reaches clinical milestone rdcu.be/eugUu Few AI-designed drug candidates have gone beyond in silico benchmarks. Now, a study in @NatureMedicine @biogerontology reports a successful phase 2a trial of rentosertib, an AI-discovered drug and
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Marinka Zitnik
@marinkazitnik
Apr 13, 2021
Survey on Representation Learning for Networks in Biology and Medicine arxiv.org/abs/2104.04883 Long-standing principles of biomed nets (often unspoken in ML) provide grounding for representation learning, explain successes & limitations @_michellemli @KexinHuang5 #netbio #GNN #ML
Marinka Zitnik
@marinkazitnik
Sep 25, 2024
Excited to share TxGNN, a model that identifies potential therapies from existing medicines for thousands of diseases. Trained across 17,080 diseases, TxGNN predicts drug candidates for conditions with limited or no treatment options, including rare diseases @NatureMedicine
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Marinka Zitnik
@marinkazitnik
Oct 31, 2024
Excited to share our perspective in @CellCellPress, where we discuss “AI scientists” as collaborative AI agents designed to empower biomedical research cell.com/cell/fulltext/… While the concept of an “AI scientist” is aspirational, advances in agent-based AI are paving the way
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Marinka Zitnik
@marinkazitnik
May 6, 2025
📢 🧬 New preprint! Can we predict which cancer patients will benefit, before treatment begins? @WanXiang_Shen Immunotherapy saves lives but many patients don’t respond to treatment, and we still lack reliable tools to predict who will benefit We introduce COMPASS, foundation
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Marinka Zitnik
@marinkazitnik
Jul 20, 2023
Introducing PINNACLE, a contextual graph AI model for comprehensive protein understanding PINNACLE dynamically adjusts its outputs based on molecular contexts in which it operates Providing outputs tailored to molecular contexts is essential for broader use of foundational
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Marinka Zitnik
@marinkazitnik
Jul 22, 2024
Excited to share our new paper on Contextual AI models for context-specific prediction in biology in @naturemethods led by stellar @_michellemli rdcu.be/dOxQ7 Understanding how proteins work and developing new therapies requires knowing which cell types proteins act
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Marinka Zitnik
@marinkazitnik
Jan 6, 2024
Introducing PDGrapher - Combinatorial prediction of therapeutically useful chemical and genetic perturbations using causally-inspired neural networks Many methods learn responses to perturbations, but PDGrapher is addressing the inverse problem, which is to infer the perturbagen
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Marinka Zitnik
@marinkazitnik
May 29, 2024
On knowing a gene: Distributional hypothesis of gene function Just as words derive meanings from context, genes can switch their roles with biological surroundings. Traditional gene annotations miss this Advances in transformers suggest a new perspective: gene functions as
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Marinka Zitnik
@marinkazitnik
Dec 11, 2024
Medicine thrives on knowledge, yet clinical vocabularies are fragmented. AI struggles to unify this knowledge, creating a gap in precision medicine. Excited to share unified clinical vocabulary embeddings, a project led by stellar @ruthie_johnson Half of healthcare foundation AI
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