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Tiffany Amariuta

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#artificial intelligence Review Sep 2026

DISCERN: Can AI Agents Work Like Scientists and Guide Discovery?

Reliable automated research requires agents to vet data, verify analyses, and generate hypotheses grounded in trustworthy evidence, potentially reducing routine scientific workload while allowing scientists to focus on interpretation and discovery. Existing benchmarks often only assess analytical task completion or hyp...

Nan Huang, Mario Tapia-Pacheco, Kun Zhou et al. · 0 citations
Open access Jul 2026

Multimodal gene prioritization reveals nonlinear regulatory architecture in childhood-onset asthma

A polygenic transcriptome risk score (PTRS) is constructed using random forest to integrate gene-level effects across critical tissues and cell types and demonstrates that modeling nonlinear interactions between variant- and gene-level effects across both bulk tissue and single cell eQTL data improves the ability to de...

N. Huang, Michelle Franc Ragsac, X. Gui et al. · 0 citations

EvoLen: Evolution-Guided Tokenization for DNA Language Model

EvoLen is a tokenizer that combines evolutionary stratification with length-aware decoding to better preserve motif-scale functional sequence units and demonstrates that tokenization introduces a critical inductive bias and that incorporating evolutionary information yields more biologically meaningful and interpretabl...

Nan Huang, Xiaoxiao Zhou, Junxia Cui et al. · 0 citations

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