bioMoR: Biology-Guided Mixture-of-Recursions for Effective Genomic Learning

• URL: http://arxiv.org/abs/2608.06727v2
• Date: Mon, 10 Aug 2026 07:33:40 GMT
• Title: bioMoR: Biology-Guided Mixture-of-Recursions for Effective Genomic Learning
• Abstract summary: Mixture-of-Recursions (MoR) improves efficiency through adaptive token-choice or expert-choice routing.<n>We propose bioMoR, which is the first framework to apply MoR to gene-level and pathway-level learning.
• Score: 11.154086943903694
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transformer models for high-dimensional omics analysis process thousands of genes or pathways, although only a subset requires deep computation. Mixture-of-Recursions (MoR) improves efficiency through adaptive token-choice or expert-choice routing. We propose bioMoR, which, to the best of our knowledge, is the first framework to apply MoR to gene-level and pathway-level learning. Our contributions include identifying three locations for integrating structured biological knowledge within an MoR backbone: graph-based information sharing refines token embeddings, a structural bias guides self-attention toward biologically related tokens, and a graph-aware router uses neighborhood information to determine each token's recursion depth. These techniques are centered on our insight that additional knowledge of token interaction can effectively help models construct embeddings and select which tokens should be learned more deeply. Across eight benchmarks spanning diverse omics data types and evaluated under a unified five-fold cross-validation protocol, bioMoR improves average macro-F1 by 8.2 percentage points and balanced accuracy by 7.1 percentage points over the strongest biology-agnostic MoR baseline while using 75 percent fewer parameters and up to 58 percent fewer FLOPs than a non-recursive Transformer. The selected marker genes or pathways provide biological interpretability, while their token-specific recursion depths reveal how computation is allocated.

Related papers

• BioM-JEPA: joint-embedding prediction of graph-connected gene blocks in single cells [64.21963170814706]
  BioM-JEPA predicts aggregate representations of graph-connected gene blocks.<n>A student network infers each target-block representation from the remaining genes in a cell.<n>Across CellBench tasks, BioM-JEPA embeddings retained expression, pathway and neighbourhood information.
  arXiv  Detail & Related papers  (2026-08-06T11:58:29Z)
• BioMiner: A Multi-modal System for Automated Mining of Protein-Ligand Bioactivity Data from Literature [53.894504720119805]
  We introduce BioMiner, a multi-modal extraction framework for bioactivity data extraction.<n>Within BioMiner, bioactivity semantics are inferred through direct reasoning, while chemical structures are resolved via a chemical-structure-grounded visual semantic reasoning paradigm.<n>For rigorous evaluation and method development, we establish a benchmark comprising 16,457 bioactivity entries curated from 500 publications.
  arXiv  Detail & Related papers  (2026-04-23T10:11:56Z)
• An Empirical Analysis of Fine-Tuning Large Language Models on Bioinformatics Literature: PRSGPT and BioStarsGPT [0.0]
  We present a reproducible pipeline for fine-tuning large language models (LLMs) on specialized bioinformatics data.<n>We fine-tuned three LLMs and benchmarked them on over 14 lexical and semantic metrics.<n>Qwen2.5-7B emerged as the best performer, with BLEU-4 and ROUGE-1 improvements of 82% and 70% for PRSGPT and BioStarsGPT, respectively.
  arXiv  Detail & Related papers  (2025-12-29T19:09:12Z)
• Learning Cell-Aware Hierarchical Multi-Modal Representations for Robust Molecular Modeling [74.25438319700929]
  We propose CHMR (Cell-aware Hierarchical Multi-modal Representations), a robust framework that models local-global dependencies between molecules and cellular responses.<n> evaluated on nine public benchmarks spanning 728 tasks, CHMR outperforms state-of-the-art baselines.<n>Results demonstrate the advantage of hierarchy-aware, multimodal learning for reliable and biologically grounded molecular representations.
  arXiv  Detail & Related papers  (2025-11-26T07:15:00Z)
• Automated Analysis of Learning Outcomes and Exam Questions Based on Bloom's Taxonomy [0.0]
  This paper explores the automatic classification of exam questions and learning outcomes according to Bloom's taxonomy.<n>A small dataset of 600 sentences labeled with six cognitive categories was processed using traditional machine learning (ML) models.
  arXiv  Detail & Related papers  (2025-11-14T02:31:12Z)
• Enhancing Biomedical Named Entity Recognition using GLiNER-BioMed with Targeted Dictionary-Based Post-processing for BioASQ 2025 task 6 [0.0]
  This study evaluates the GLiNER-BioMed model on a BioASQ dataset.<n>We introduce a targeted dictionary-based post-processing strategy to address common misclassifications.<n>This work highlights the potential of dictionary-based refinement for pre-trained BioNER models but underscores the critical challenge of overfitting to development data.
  arXiv  Detail & Related papers  (2025-10-03T18:35:04Z)
• BioScore: A Foundational Scoring Function For Diverse Biomolecular Complexes [11.395906604051156]
  Current structure-based scoring functions often lack generalizability across diverse biomolecular systems.<n>We present BioScore, a foundational scoring function that addresses key challenges -- data sparsity, cross-system representation, and task compatibility.<n>BioScore supports a wide range of tasks, including affinity prediction, conformation ranking, and structure-based virtual screening.
  arXiv  Detail & Related papers  (2025-07-15T00:41:58Z)
• Advancing Tabular Stroke Modelling Through a Novel Hybrid Architecture and Feature-Selection Synergy [0.9999629695552196]
  The present work develops and validates a data-driven and interpretable machine-learning framework designed to predict strokes.<n>Ten routinely gathered demographic, lifestyle, and clinical variables were sourced from a public cohort of 4,981 records.<n>The proposed model achieved an accuracy rate of 97.2% and an F1-score of 97.15%, indicating a significant enhancement compared to the leading individual model.
  arXiv  Detail & Related papers  (2025-05-18T21:46:45Z)
• Augmenting Biomedical Named Entity Recognition with General-domain Resources [47.24727904076347]
  Training a neural network-based biomedical named entity recognition (BioNER) model usually requires extensive and costly human annotations.<n>We propose GERBERA, a simple-yet-effective method that utilized general-domain NER datasets for training.<n>We systematically evaluated GERBERA on five datasets of eight entity types, collectively consisting of 81,410 instances.
  arXiv  Detail & Related papers  (2024-06-15T15:28:02Z)
• Breast Ultrasound Tumor Classification Using a Hybrid Multitask CNN-Transformer Network [63.845552349914186]
  Capturing global contextual information plays a critical role in breast ultrasound (BUS) image classification. Vision Transformers have an improved capability of capturing global contextual information but may distort the local image patterns due to the tokenization operations. In this study, we proposed a hybrid multitask deep neural network called Hybrid-MT-ESTAN, designed to perform BUS tumor classification and segmentation.
  arXiv  Detail & Related papers  (2023-08-04T01:19:32Z)
• Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations [68.32093648671496]
  We introduce GODE, which accounts for the dual-level structure inherent in molecules.<n> Molecules possess an intrinsic graph structure and simultaneously function as nodes within a broader molecular knowledge graph.<n>By pre-training two GNNs on different graph structures, GODE effectively fuses molecular structures with their corresponding knowledge graph substructures.
  arXiv  Detail & Related papers  (2023-06-02T15:49:45Z)
• Cancer Gene Profiling through Unsupervised Discovery [49.28556294619424]
  We introduce a novel, automatic and unsupervised framework to discover low-dimensional gene biomarkers. Our method is based on the LP-Stability algorithm, a high dimensional center-based unsupervised clustering algorithm. Our signature reports promising results on distinguishing immune inflammatory and immune desert tumors.
  arXiv  Detail & Related papers  (2021-02-11T09:04:45Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)

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.