arXiv Machine Learning
Jun 5
Environment-Robust Representation Learning with Empirical Bayes
arXiv:2606. 05365v1 Announce Type: cross Abstract: We consider multi-environment prediction problems.
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.
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arXiv Machine Learning
Jul 3
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.
arXiv Machine Learning
Jun 19
Multi-Task Bayesian In-Context Learning
arXiv:2606. 20538v1 Announce Type: new Abstract: Bayesian predictive inference provides a principled framework for uncertainty quantification, data efficiency, and robust generalization.
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
Jun 3
Parallel Adaptive Multi-Objective Evolutionary Learning of Discretized Bayesian Network Classifiers for Clinical Data
arXiv:2605. 29058v2 Announce Type: replace Abstract: Bayesian Networks (BNs) are of interest from an explainable AI viewpoint, offering transparent probabilistic models for decision support.
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.
Hugging Face Trending Papers
Aug 31
Coarse composition suffices: tabular in-context learning for multi-activity antimicrobial peptide profiling
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...
arXiv Machine Learning
Jul 2
Deep Multitask Learning for Mixed-Type Outcomes with Shared Sparsity
arXiv:2607. 00995v1 Announce Type: cross Abstract: Most existing multitask learning approaches are limited by their reliance on task-specific loss functions tailored to the scale and type of each outcome.
arXiv Machine Learning
Jun 9
SDM-Q: Cost-Aware Staged Decision-Making for Multi-Omics Classification with Deep Q-Learning
arXiv:2605. 31014v2 Announce Type: replace Abstract: Multi-omics data provide complementary molecular characterizations of disease phenotypes and play an important role in disease diagnosis and subtype classification in precision medicine.
arXiv Machine Learning
Sep 1
Coarse composition suffices: tabular in-context learning for multi-activity antimicrobial peptide profiling
The study demonstrates that a simple, sequence-only approach using 330 interpretable descriptors and the TabPFN tabular foundation model can outperform complex multimodal deep learning methods for multi-label antimicrobial peptide activity prediction. On the ESCAPE benchmark (82,359 peptides, five labels), a label‑powerset TabPFN model achieved a mean average precision of 77.8%, surpassing the previous best of 72.1%. The approach also shows that predicted structure is unnecessary, that a small set of global physicochemical scalars can recover most performance, and that modeling label dependence benefits rare activities and informs assay prioritization.
arXiv Machine Learning
Jul 30
BayesAME: Bayesian Active Model Evaluation
arXiv:2607. 27023v1 Announce Type: new Abstract: Evaluating large generative models across benchmarks is time-consuming and computationally expensive.
arXiv Machine Learning
Jun 2
Bayesian meta-learning for modeling Alzheimer's disease progression
arXiv:2606. 02228v1 Announce Type: cross Abstract: Predicting whether an individual with Alzheimer's disease will experience mild or severe disease progression is essential for personalized treatment.