Coarse composition suffices: tabular in-context learning for multi-activity antimicrobial peptide profiling

• URL: http://arxiv.org/abs/2608.30337v2
• Date: Sun, 06 Sep 2026 06:00:44 GMT
• Title: Coarse composition suffices: tabular in-context learning for multi-activity antimicrobial peptide profiling
• Abstract summary: We show that a simple, sequence-only pipeline can match and surpass multimodal, structure-conditioned deep models.<n>On ESCAPE (82,359 peptides; five labels), a label-powerset TabPFN model achieves mAP-5 = 77.8%, improving on the previously best reported 72.1%.
• Score: 2.571440349338848
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Antimicrobial peptides (AMPs) often act against multiple pathogen classes, making multi-label activity prediction a more realistic screening target than binary antimicrobial classification. The ESCAPE benchmark formalizes this setting, but leading approaches typically rely on multimodal, structure-conditioned deep models that are costly to train and tune. We show that a simple, sequence-only pipeline can match and surpass these methods by combining 330 interpretable sequence descriptors with TabPFN, a tabular foundation model that performs in-context prediction in a single forward pass without gradient-based training or hyperparameter search. On ESCAPE (82,359 peptides; five labels), a label-powerset TabPFN model achieves mAP-5 = 77.8%, improving on the previously best reported 72.1%. A probabilistic classifier chain is the first method to match or exceed the best published average precision on each of the five labels simultaneously. The gains persist under the prior state-of-the-art single-fold training protocol, indicating they are not a training-set-size artefact, and are largest for remote homologues (+11.2 points below 30% sequence identity). Ablations further show that predicted structure is unnecessary at inference and that performance is not driven by any single descriptor family: ten global physicochemical scalars recover 91% of full-feature performance. Finally, explicitly modelling label dependence yields targeted benefits for scarce activities and supports ranking which activity to assay next from partial positive evidence.

Related papers

• Fine-Tuning Over Architectural Complexity: Broad-Coverage PII Detection on PIIBench with DeBERTa [0.0]
  PII detection systems are frequently trained within narrow source or domain boundaries, limiting coverage when deployed on heterogeneous text.<n>We study model fine-tuning on a corrected multi-source PIIBench preparation spanning 82 retained entity types across ten source datasets.<n>We evaluate three DeBERTa-based approaches: direct token classification fine-tuning, a source-conditioned hierarchical model (SC+H), and a three-phase curriculum extension (SC+H+Curr)
  arXiv  Detail & Related papers  (2026-05-25T13:12:42Z)
• Multitask Multimodal Fusion with Tabular Foundation Models for Peak and Durability Prediction of Pertussis Booster Response [0.0]
  Pertussis booster vaccination produces immune responses that vary widely across individuals in both peak magnitude and long-term durability.<n>Most computational models target only one, missing the full boost-and-wane trajectory.<n>Jointly predicting both is non-trivial because the two endpoints are biologically dissociated rather than redundant.
  arXiv  Detail & Related papers  (2026-05-13T01:00:50Z)
• PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities [86.63247982275396]
  PRIME is a missing-aware multimodal self-supervised pretraining framework.<n>It learns robust and transferable representations from partially observed cohorts.<n>We evaluate PRIME on The Cancer Genome Atlas with label-free pretraining on 32 cancer types.
  arXiv  Detail & Related papers  (2026-04-05T21:14:27Z)
• Investigating Knowledge Distillation Through Neural Networks for Protein Binding Affinity Prediction [0.22369578015657954]
  Trade-off between predictive accuracy and data availability makes it difficult to predict protein--protein binding affinity accurately.<n>We suggest a regression framework based on knowledge distillation that uses protein structural data during training and only needs sequence data during inference.
  arXiv  Detail & Related papers  (2026-01-07T08:43:08Z)
• FlowDock: Geometric Flow Matching for Generative Protein-Ligand Docking and Affinity Prediction [3.8366697175402225]
  FlowDock is the first deep geometric generative model that learns to map unbound (apo) structures to their bound (holo) counterparts.<n>FlowDock provides predicted structural confidence scores and binding affinity values with each of its generated protein-ligand complex structures.
  arXiv  Detail & Related papers  (2024-12-14T20:54:37Z)
• Probably Approximately Precision and Recall Learning [60.00180898830079]
  A key challenge in machine learning is the prevalence of one-sided feedback.<n>We introduce a Probably Approximately Correct (PAC) framework in which hypotheses are set functions that map each input to a set of labels.<n>We develop new algorithms that learn from positive data alone, achieving optimal sample complexity in the realizable case.
  arXiv  Detail & Related papers  (2024-11-20T04:21:07Z)
• Rethinking Classifier Re-Training in Long-Tailed Recognition: A Simple Logits Retargeting Approach [102.0769560460338]
  We develop a simple logits approach (LORT) without the requirement of prior knowledge of the number of samples per class. Our method achieves state-of-the-art performance on various imbalanced datasets, including CIFAR100-LT, ImageNet-LT, and iNaturalist 2018.
  arXiv  Detail & Related papers  (2024-03-01T03:27:08Z)
• Self-Supervised Pretraining Improves Performance and Inference Efficiency in Multiple Lung Ultrasound Interpretation Tasks [65.23740556896654]
  We investigated whether self-supervised pretraining could produce a neural network feature extractor applicable to multiple classification tasks in lung ultrasound analysis. When fine-tuning on three lung ultrasound tasks, pretrained models resulted in an improvement of the average across-task area under the receiver operating curve (AUC) by 0.032 and 0.061 on local and external test sets respectively.
  arXiv  Detail & Related papers  (2023-09-05T21:36:42Z)
• RanPAC: Random Projections and Pre-trained Models for Continual Learning [59.07316955610658]
  Continual learning (CL) aims to learn different tasks (such as classification) in a non-stationary data stream without forgetting old ones. We propose a concise and effective approach for CL with pre-trained models.
  arXiv  Detail & Related papers  (2023-07-05T12:49:02Z)
• Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model [93.9943278892735]
  Key problem in protein sequence representation learning is to capture the co-evolutionary information reflected by the inter-residue co-variation in the sequences. We propose a novel method to capture this information directly by pre-training via a dedicated language model, i.e., Pairwise Masked Language Model (PMLM) Our result shows that the proposed method can effectively capture the interresidue correlations and improves the performance of contact prediction by up to 9% compared to the baseline.
  arXiv  Detail & Related papers  (2021-10-29T04:01:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.