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Related Articles from SNS
When Are Multimodal Predictions Biologically Supported? A Diagnostic Evaluation Framework
arXiv:2605.31504v1 Announce Type: new Abstract: Multimodal models in oncology can produce accurate predictions, but accurate prediction does not reveal whether the model has learned biology that is shared across modalities, biology confined to one modality, or spurious correlations that reflect confounders rather than genuine biology. We introduce DECAT, a model-agnostic post-hoc evaluation framework that classifies multimodal representations into four diagnostic scenarios for a given task...
Exact Stiefel Optimization for Probabilistic PLS: Closed-Form Updates, Error Bounds, and Calibrated Uncertainty
Announce Type: replace-cross Abstract: Probabilistic partial least squares (PPLS) is a central likelihood-based model for two-view learning when one needs both interpretable latent factors and calibrated uncertainty. Building on the identifiable parameterization of Bouhaddani et al.\ (2018), existing fitting pipelines still face two practical bottlenecks: noise--signal coupling under joint EM/ECM updates and nontrivial handling of orthogonality constraints. Following the fixed-noise...
Graph Mamba Survival Analysis Based on Topology-Aware ordering
arXiv:2606.02602v1 Announce Type: new Abstract: In computational pathology, Whole Slide Images (WSIs) survival analysis is crucial for patient prognosis assessment, but it faces multiple technical challenges. Although the Transformer captures long-range dependencies through its self-attention mechanism, its $O(N^2)$ time complexity causes a severe computational bottleneck in large-scale WSIs graph structures. The Mamba model breaks through the Transformer's computational bottleneck with...
Loss of tissue specificity and recurrent pan-cancer activation define a conserved oncogenic microRNA class
MicroRNAs (miRNAs) act as crucial post-transcriptional regulators of large gene networks, and their aberrant expression drives key oncogenic processes such as epithelial-mesenchymal transition (EMT), angiogenesis, immune evasion, and metastasis. Oncogenic miRNAs that lose tissue specificity during malignant transformation represent promising therapeutic targets, as their restricted expression in healthy organs could minimize off-target effects. To identify these candidates, this study...
Genomic-Adjusted Radiation Dose from Bulk RNA Sequencing for Personalized Radiotherapy
Radiotherapy is delivered to more than half of all patients with cancer yet is prescribed using uniform physical doses despite well-established interpatient variability in biological response. The genomic-adjusted radiation dose (GARD), derived from the radiosensitivity index (RSI), integrates tumor transcriptomics with radiation dose to estimate patient-specific treatment effect, and has been clinically validated as a predictor of radiotherapy benefit across diverse disease sites, including...
Do Foundation Models See Biology? Evaluating Attention Coherence with Spatial Transcriptomics in Glioblastoma
Announce Type: new Abstract: Whether attention maps from pathology foundation models capture genuine biology remains unknown, yet this question is critical for clinical trust and regulatory approval. We propose a spatial transcriptomics-based framework for orthogonal, hypothesis-free evaluation of attention and apply it to five pathology foundation models (CONCH v1.5, UNI v2, Virchow2, GigaPath, H-Optimus-1) and a ResNet50 baseline. Using attention-based multiple instance learning, we train...
Multivariate integration of histological images and gene expression data: a comparative review
Integrating histological images with gene expression data offers a promising approach for linking tissue morphologies to molecular signatures and improving disease subtyping. However, such integration remains challenging due to the high dimensionality of these datasets, cross-modal heterogeneity, and limited interpretability. Multivariate methods such as Sparse Canonical Correlation Analysis (Sparse CCA), Joint Nonnegative Matrix Factorisation (Joint NMF), and Angle-based Joint and...
BBOmix: A Tabular Benchmark for Hyperparameter Optimization of Unsupervised Biological Representation Learning
Announce Type: new Abstract: The rapid advancement of high-throughput sequencing has led to large, high-dimensional omics datasets. Deep unsupervised learning architectures, particularly Autoencoders (AEs), are increasingly used for dimensionality reduction and representation learning in this domain. However, AEs are highly sensitive to architectural choices and hyperparameters, and unsupervised optimization typically relies on reconstruction loss, which may be a poor proxy for downstream...
ArrowFlow: Hierarchical Machine Learning in the Space of Permutations
Announce Type: replace Abstract: We introduce ArrowFlow, a machine learning architecture that operates entirely in the space of permutations. Its computational units are ranking filters, learned orderings that compare inputs via Spearman's footrule distance and update through permutation-matrix accumulation, a non-gradient rule rooted in displacement evidence. Layers compose hierarchically: each layer's output ranking becomes the next layer's input, enabling deep ordinal representation...
Symb-xMIL: Symbolic Explanations for Multiple Instance Learning in Digital Pathology
Announce Type: new Abstract: Explanations of multiple instance learning (MIL) models are widely used for validation and discovery in digital histopathology. Existing methods primarily rely on heatmaps that highlight influential regions but do not explain how evidence from different tissue regions is combined to produce a prediction. This limits interpretability, especially when decisions depend on interactions between tissue features.