Riya Sinha (@riyavsinha) profile picture

Riya Sinha

@riyavsinha

CS PhD Student @ Stanford | ML x Genomics

Joined March 2021
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  • Followers90
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Riya Sinha retweeted
Ben Viggiano (@bviggiano_13) profile picture
Ben Viggiano@bviggiano_13Jun 23
Performing bioinformatics analysis and building biological design pipelines can mean a lot of infrastructure fighting: fragmented tools, idiosyncratic formats, and APIs that are hard to compose. That friction slows researchers down, and as this Anthropic blog post lays out, it's now a major bottleneck for modern agents as well. Today, we're releasing Proto, a design platform for generative biology. This thread is on the layer that makes it work: proto-tools, a universal open-source infrastructure layer for biological models and tools.
Image from Ben Viggiano (@bviggiano_13)
Anthropic (@AnthropicAI) profile pictureAnthropic
@AnthropicAIJun 8
New Science Blog: Why has AI advanced faster in coding than in biology? To agents, bio databases are like cities built before cars—maddening to drive in because they're designed for different traffic. How do we build infrastructure agents can use? anthropic.com/research/agent…
Paving the way for AI agents in biology

Paving the way for AI agents in biology

In this Anthropic Science post, Laura Luebbert argues that we need to make biological data infrastructure more agent-friendly.

anthropic.com
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📣 Announcing Terminal-Bench Science: benchmarking AI agents on real scientific workflows – now open for task contributions👇 tbench.ai/news/tb-science-an… @AnthropicAI, @OpenAI, and @GoogleDeepMind use Terminal-Bench to evaluate AI on coding tasks. We're now extending it to scientific workflows. 1/6🧵
Image from Steven Dillmann (@StevenDillmann)
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Riya Sinha retweeted
Aman Patel (@amanpatel100) profile picture
Aman Patel@amanpatel100Feb 12
Excited to announce our latest work! We present ARSENAL, a short-context DNA language model specifically designed to learn important sequence features in noncoding regulatory DNA. Why is such a model important? Read on to find out! biorxiv.org/content/10.64898…
Short-Context Regulatory DNA Language Models with Motif-Discovery Regularization

Short-Context Regulatory DNA Language Models with Motif-Discovery Regularization

Self-supervised DNA language models (DNALMs) are typically trained at massive scale on whole genomes and long contexts. However, regulatory sequence features are sparse, heterogeneous, and dominated...

biorxiv.org
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Excited for a major milestone in a collab effort led by @jengreitz to map enhancers & interpret variants in the human genome: The E2G Portal e2g.stanford.edu collates predictions of enhancer-gene regulatory interactions across >1,600 cell types & tissues. Use cases 👇1/
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If you want to self-deploy #LangChain #LangGraph Runnables, check out langgraph_serve! It's an extension of #LangServe that supports modification of the LangGraph graph state, and resuming execution after graph interruptions. github.com/riyavsinha/langgr…
GitHub - riyavsinha/langgraph-serve: LangServe extension to interface with LangGraph Runnables

GitHub - riyavsinha/langgraph-serve: LangServe extension to interface with LangGraph Runnables

LangServe extension to interface with LangGraph Runnables - riyavsinha/langgraph-serve

github.com
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Very excited to release tangermeme v0.2.0! This release focuses on attribution methods, seqlet calling, plotting, and variant effect scores, and tweaks the API. Docs: tangermeme.readthedocs.io/en… GitHub: github.com/jmschrei/tangerme… Get it with `pip install tangermeme`! 1/
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You've probably seen attribution tracks where the height of each letter is its "importance" to a predictive model and motifs pop out. But the technical details behind how these are calculated can matter a lot -- and I'm worried many may be done incorrectly. 1/
Image from Jacob Schreiber (@jmschreiber91)
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