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Sameed Siddiqui

@sameedms.bsky.social

Californian lost in the Northeast ☀️. PhD @ MIT Computational and Systems Biology | MBA Fellow at MIT Sloan. @SabetiLab member

Posts Replies Media

Sameed Siddiqui @sameedms.bsky.social · 21/03/2025

Lyra’s subquadratic O(N log N) complexity dramatically reduces memory (125x–2600x less than Evo and ESM-1b) and accelerates inference—up to 239x faster than ESM-1b, processing sequences up to 1M length.

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Sameed Siddiqui @sameedms.bsky.social · 21/03/2025

Lyra achieves SOTA results in 6 out of 7 intrinsically disordered protein region tasks, with an average AUC of 0.89, outperforming a ProtT5-based model (avg AUC 0.86). Lyra accomplishes this using only 55K parameters, compared to ProtT5’s 3 billion parameters—a >50,000-fold reduction in model size.

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Sameed Siddiqui @sameedms.bsky.social · 21/03/2025

Lyra’s consistently strong performance across different tasks using orders of magnitude fewer parameters allows researchers to spend less time optimizing models and more time generating biological insights.

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Sameed Siddiqui @sameedms.bsky.social · 21/03/2025

To unify biological sequence modeling across DNA, RNA, and proteins into a single computational framework, we revisited epistasis—the phenomenon where mutations influence each other—which can be characterized by multilinear polynomials.

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Sameed Siddiqui @sameedms.bsky.social · 21/03/2025

🧬 Meet Lyra, a new paradigm for accessible, powerful modeling of biological sequences. Lyra is a lightweight SSM achieving SOTA performance across DNA, RNA, and protein tasks—yet up to 120,000x smaller than foundation models (ESM, Evo). Bonus: you can train it on your Mac. arxiv.org/abs/2503.16351

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