Papers for

biotechnology data teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Reflective AI edits help protein prediction with limited data labels

Does reflective LLM feedback survive a wet-lab-scale label budget?

Abstract: Can reflective language-model feedback improve a biological predictor when each cycle buys only one plate of labels? We separate three possible sources of benefit: proposing model edits, choosing measurements, and revising decisions through repeated feedback. We replay $36$ ProteinGym assays, starting with $96$ labels and acquiring $96$ per round for six rounds, with a sealed test set and a shared edit schema. The pre-specified comparison of full LLM control against random edits detects no advantage in test rank correlation ($Δz=-0.008$, $95\%$ CI $[-0.034,+0.018]$); equivalence is also unestablished. Exploratory ablations nevertheless identify useful model editing. With random acquisition fixed, LLM edits improve Fisher-transformed test Spearman by $0.043$ over no edits; with acquisition fixed to upper confidence bound, they improve normalized top-variant recovery by $0.023$. LLM acquisition alone does not detectably outperform fixed adaptive rules, and repeated feedback does not detectably outperform one initial recommendation under full control. Thus, the observed gains support constrained specialist editing, while the incremental value of recursive reflection remains unestablished at this label budget.

Sun 1 NovArtificial Intelligence

The gist

The paper studies whether using reflective feedback from a large language model (LLM) can improve how well a computer predicts biological data when only a small amount of measurement data can be collected at a time. The researchers replayed multiple biological tests using limited, phased data gathering to see if letting the AI suggest model changes or guide data collection makes predictions better. They found that AI-suggested model edits can improve prediction quality, but using the AI to choose which data to gather next or to repeatedly revise the model did not clearly help. This suggests focused AI edits have value, while recursive feedback needs more evidence with this limited data setup.

Open → 2611.01908v1

Self supervised learning improves imaging flow cytometry with fewer annotations

IFC-SSL-Bench: Benchmarking Self-Supervised Learning for Annotation-Efficient Multispectral Imaging Flow Cytometry

Abstract: Imaging Flow Cytometry (IFC) generates large multispectral datasets, yet the need for expert annotations bottlenecks the development of supervised deep learning models. While Self-Supervised Learning (SSL) offers a solution, its application to IFC remains fragmented and lacks systematic benchmarking. To address this, we present IFC-SSL-Bench, a modular framework for evaluating SSL in IFC. We benchmark six SSL approaches across four backbones on two heterogeneous datasets. By assessing downstream classification performance across annotation regimes of the training sets (1%-100%), we quantify annotation efficiency and identify optimal configurations for high-dimensional biological imaging. SSL matches the performance of fully supervised baselines trained on 100% of annotated data while using as little as 5% of annotations for morphological classification, with advantages in low-data regimes below 5%. Across both datasets, the contrastive approach MoCo-v2 paired with ConvNeXt backbones emerged as the top-performing configuration, with ConvNeXtV1-T best for immunological synapse classification and ConvNeXtV2-T for morphological classification. We identify a clear pattern in transfer dynamics: ImageNet initialization benefits morphological classification, consistent with its visual similarity to natural images, whereas random initialization better captures the more domain-specific immunological synapse classification. SSL elevates class-specific F1-scores by over 35 percentage points for structurally complex classes at 1% of annotated data. We synthesize these insights into a unified set of guidelines for deploying self-supervised workflows in high-throughput IFC, and release the benchmark as a resource for future work.

Thu 22 OctComputer Vision and Pattern Recognition

The gist

Imaging flow cytometry creates large and complex biological images but requires experts to label them, which is slow and expensive. The authors show that self-supervised learning, a way for computers to learn from unlabeled images, can nearly match fully supervised methods using only a small fraction of labeled data. They tested several learning methods and network designs on different datasets, finding the best setups for tasks like identifying cell shapes and immune cell interactions. Their work offers guidelines for efficiently deploying these techniques and provides a benchmark to help others compare future methods.

Open → 2610.31848v1