arXiv Machine Learning
Jun 30
Friend or Foe
arXiv:2509. 00123v2 Announce Type: replace-cross Abstract: A fundamental challenge in microbial ecology is determining whether bacteria compete or cooperate in different environmental conditions.
Structured Gaussian Processes for Uncertainty-Aware Classification of High-Dimensional, Small-Sampled Omics Data
arXiv:2607. 02103v1 Announce Type: cross Abstract: Classifying heterogeneous omics data remains a fundamental challenge in computational biology, particularly in high-dimensional, small-sample settings where nonlinear interactions dominate and class imbalance further complicates reliable prediction of minority phenotypes.
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arXiv Machine Learning
Sep 23
Hierarchical Sparse Bayesian Multitask Learning for Disease Prediction in Pooled Microbiome Studies
This paper introduces a hierarchical Bayesian multitask learning model that assumes a shared sparsity structure across different binary classification tasks. The authors develop a variational inference algorithm for efficient posterior approximation and evaluate the method on synthetic data and pooled microbiome studies. Results show superior support recovery in synthetic experiments and robust, well‑calibrated predictions with informative taxa selection in microbiome classification.
arXiv AI
Jun 9
Knowledge-Inclusive Adaptive Physics-Informed Neural Network for Microbial Interaction Modelling
arXiv:2606. 07686v1 Announce Type: cross Abstract: Physics-Informed Neural Network (PINN) is a way of including knowledge in the form of equations in Machine Learning methods.
arXiv AI
Aug 10
bioMoR: Biology-Guided Mixture-of-Recursions for Effective Genomic Learning
arXiv:2608. 06727v1 Announce Type: new Abstract: Transformer models for high-dimensional omics analysis process thousands of genes or pathways, although only a subset requires deep computation.
Hugging Face Trending Papers
Aug 7
bioMoR: Biology-Guided Mixture-of-Recursions for Effective Genomic Learning
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.
arXiv AI
3d ago
TaxDistill: Improving Metagenomic Taxonomic Annotation via Distilled Genomic Foundation Models
arXiv:2605.28868v2 Announce Type: replace-cross Abstract: Metagenomic taxonomic annotation is essential for interpreting complex microbial communities, yet reliable annotation remains challenging und...
arXiv Machine Learning
Jun 25
Are Tabular Foundation Models Robust to Realistic Query Distribution Shifts in Microbiome Data?
arXiv:2606. 24995v1 Announce Type: new Abstract: Tabular foundation models (TFMs) achieve strong performance on microbiome abundance data, yet their robustness under realistic distribution shift remains poorly characterized.
arXiv AI
Aug 11
Biologically Informed Representation Learning for Robust Cross-Center Generalization of MALDI-TOF Mass Spectrometry
arXiv:2608. 08182v1 Announce Type: cross Abstract: Machine learning models for MALDI-TOF mass spectrometry have shown considerable promise for clinical microbiology tasks such as microbial identification and antimicrobial resistance prediction.
arXiv Machine Learning
Aug 28
MODIS: Multi-Omics Data Integration for Small and unpaired datasets
MODIS is a semi‑supervised framework for integrating multi‑omics data that are often unpaired, partially labeled, and scarce, such as in rare disease studies. It trains on a large reference database and a small target dataset simultaneously, using diagonal integration and class‑label alignment to handle class imbalance. The architecture combines variational auto‑encoders, a class classifier, and an adversarially trained modality classifier, with a regularized relativistic GAN loss for stable training, and demonstrates high accuracy on synthetic data and the TCGA cancer dataset.
arXiv AI
Aug 18
ProteoKnight: Convolution-based Phage Virion Protein Classification and Uncertainty Analysis
arXiv:2508. 07345v2 Announce Type: replace-cross Abstract: \textbf{Introduction:} Accurate prediction of Phage Virion Proteins (PVP) is essential for genomic studies due to their crucial role as structural elements in bacteriophages.
arXiv Machine Learning
Jul 8
Canopy: A Heterograph Foundation Model for Metabolic Engineering
arXiv:2607. 06224v1 Announce Type: new Abstract: Designing microbial strains that produce high-value chemicals at commercially viable titers remains a central challenge in metabolic engineering.
arXiv AI
Sep 10
The Accuracy Paradox: Empirical Diagnostic of Default Decision Thresholds in Multi-Label Enzyme Commission Prediction [With Code]
The study evaluates the use of default decision thresholds (t=0.50) in multi‑label enzyme commission (EC) number prediction across 14,096 compounds and six EC classes. It finds a high mean accuracy of 77.16% but low macro F1 (0.3976) and macro recall (0.3872), indicating severe class‑imbalance issues: majority classes are over‑predicted while minority classes, especially EC6, have zero recall despite reasonable ROC‑AUC. The authors recommend target‑specific threshold tuning and conformal calibration as post‑processing safeguards to expose and correct these hidden errors.