Arpan Sarkar retweetledi
Arpan Sarkar
10 posts
Arpan Sarkar
@arpsark
PhD candidate in the Eddy Lab @Harvard interested in protein ML for modeling protein domains and predicting homology
Katılım Mayıs 2024
243 Takip Edilen41 Takipçiler
Excited to be a part of this team! Check out our work investigating scientific problem solving in LLMs!
Alan Li@alanli2020
1/9 🚀 New paper: Demystifying Scientific Problem-Solving in LLMs — How does reasoning enhancement affect knowledge recall, and do LLMs benefit from external knowledge complimentary to reasoning? Tldr; 📊 SciReas: holistic and efficient evaluation suite for scientific reasoning 🧠 KRUX: a novel framework to study knowledge vs reasoning in LLMs 🔑 Findings: knowledge is a bottleneck; reasoners + in-context knowledge help; long CoT helps knowledge recall/utilization
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Arpan Sarkar retweetledi
(1/7) Very excited to share my first PhD preprint on the interactions of two of my favorite mobile genetic elements: phages and group II introns!
biorxiv.org/content/10.110…
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Arpan Sarkar retweetledi
Super excited to share my postdoc work investigating how mating and parental behaviors evolve using wild species of mice combined with single nucleus RNA-sequencing of the hypothalamus 🐭🧠🧬!
biorxiv.org/content/10.110…
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@sokrypton It's also important to look at the homology between any domain in any seq in test to any domain in any seq in training, and I think we do a good job of assessing performance at various domain-level max % identity thresholds in our work on PSALM (biorxiv.org/content/10.110…)
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📈 One standard graph in all bio deep learning papers should be: max similarity to anything in training set vs performance. (Reviewers shouldn't have to guess if there might be overfitting issues).
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We're excited to share PSALM -- keep an eye out for subsequent releases!
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PSALM annotates sequences with greater sensitivity and specificity than profile HMM-based methods (on identical training sets). PSALM has a very low residue-level FPR, benefits strongly from additional examples, and annotates clans even at low percent identity to training data
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PSALM uses a hierarchical approach that considers both individual protein domain families and clans (determined by Pfam). Modeling clans is an interpretable intermediate step that helps identify functional regions that lack clear family-level annotations
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We propose PSALM, which extends ESM-2 to predict *residue*-level protein sequence annotations. PSALM accurately annotates domain boundaries, multi-domain proteins, and even domains that are currently unannotated in sequence databases
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Excited to share our new work (with @KumareshSpikes): "Protein Sequence Domain Annotation using Language Models" (PSALM)
biorxiv.org/content/10.110…
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