arXiv:2607. 27651v2 Announce Type: replace Abstract: Learned rules select samples for follow-up measurements in high-throughput experiments.
By Jia Bi, Samuel Pinilla, Chenyang Zhu
arXiv:2607. 27651v1 Announce Type: new Abstract: Adaptive laboratories choose measurements during experiments, yet most methods begin after adaptation is permitted.
By Jia Bi, Samuel Pinilla, Chenyang Zhu
The paper introduces AssayBench-Loop, a large benchmark of 1,389 CRISPR screens across five phenotype categories, and builds on it to develop AssayLoop, a sequential experimental design framework that combines a transformer-based acquisition policy (AssayFormer) trained on historical data with LLM-derived biological priors. AssayLoop achieves a 5.67‑fold enrichment over random selection, recovering 27.7% of hits after testing only about 5% of the library, and outperforms existing adaptive-design methods and standalone LLMs. The authors also present AssayLLM, extending the approach directly to an LLM via task‑specific post‑training, and show that performance improves with more historical training data and transfers to unseen phenotype categories.
By Carl Edwards, Edward De Brouwer, Xiner Li, Namkyeong Lee, Ehsan Hajiramezanali, Anne Biton, Sara Mostafavi, Gabriele Scalia
arXiv:2604. 11305v3 Announce Type: replace Abstract: Conformal selection (CS) uses calibration data to identify test inputs whose unobserved outcomes are likely to satisfy a pre-specified minimal quality requirement, while controlling the false discovery rate (FDR).
By Meiyi Zhu, Osvaldo Simeone
The study evaluates whether a portfolio of compact, semantically named descriptor blocks can match the performance of a 2048‑dimensional CheMeleon embedding in low‑data molecular assays. Using a fixed 11‑dimensional physicochemical base and greedily adding provenance‑screened blocks, the portfolio achieves a mean test AUC of 0.762 across nine ADME/Tox assays, comparable to CheMeleon’s 0.764 and better than Mordred’s 0.756. The results meet a predeclared pooled parity threshold but not all per‑assay thresholds, and further analysis confirms the competitiveness of the auditable representation while highlighting unresolved assay‑level differences.
By Yiqi Yao, Miquel Duran-Frigola
The paper investigates how to allocate limited amyloid PET scans in Alzheimer’s research by comparing simple target‑aligned validation strategies to more complex uncertainty‑based sampling. Using the A4/LEARN PET archive, it shows that for the APOE4 carrier versus non‑carrier contrast, balancing scans by APOE4 status nearly matches the performance of a target‑specific scoring approach, while generic uncertainty sampling performs worse. For other analyses, such as age‑slope or cutoff‑indexed PET positivity, target‑specific scoring yields greater gains, underscoring that measurement allocation should align with the specific claim being validated.
By Eliuvish Han Cui