Biology subjects

Znabu, B. F.

Publications and source records attributed to Znabu, B. F..

3 recordsLinked to original sources

bioRxiv2026

A Reproducible MFASS Benchmark of Splice-Disruption Predictors Reveals a Shared Exon-Interior Blind Spot

We benchmark four published splicing variant-effect predictors against a multiplexed experimental splicing assay. On 27,733 single-nucleotide variants in and around human exons from MFASS with measured exon-inclusion outcomes, Pangolin is the strongest predictor of splice-disrupting variants (AUROC 0.888, average precision 0.421), ahead of SpliceAI (0.819, 0.321) and SpliceTransformer (0.786, 0.317), with MMSplice fourth (0.758, 0.256); all four exceed the older SPANR model (0.748, 0.228). The ranking reproduces the relative performance reported by the Pangolin authors, a correctness check on the pipeline. A calibrated consensus of the three deep-learning sequence-window predictors, evaluated on an exon-grouped held-out split, does not meaningfully improve over Pangolin alone. Stratifying by distance to the splice site exposes a shared blind spot: all five tools detect disruptions within a few bases of the splice site well, but recall declines sharply in the exon interior, and 19% of disrupting variants are missed by every tool; these shared misses are enriched among variants away from splice sites, and are predominantly exon-interior. MMSplice, the one model built for modular exonic and intronic effects rather than splice-site recognition, shows the same distance-dependent decline, so the blind spot is not an artifact of splice-site-centric architectures. Every number is computed against fixed experimental ground truth and is reproducible from the public dataset and the released code.

bioinformatics↗

bioRxiv2026

NativeReady: an open benchmark and sequence-based triage model for native mass spectrometry suitability

Native mass spectrometry is a central analytical method for characterizing intact proteins, antibody-drug conjugates, and non-covalent assemblies, and it is increasingly the deciding measurement in biotherapeutic development pipelines. A single screening attempt requires days of expression, purification, and buffer exchange into ammonium acetate, followed by 30 to 60 minutes of optimization on a Q-Exactive UHMR or comparable instrument. To our knowledge, no published sequence-based predictor currently estimates native MS suitability before experimental screening. We curated 634 unique proteins with documented native MS outcomes, drawn from a 232-protein hand-curated base set, 358 entries recovered from RCSB PDB by full-text searching for native MS terminology, and 44 evidence-based extractions from supplementary tables across 80 EuropePMC papers. We trained four model variants on this benchmark: a 36-feature BioPython physicochemical baseline, an ESM-2 linear probe, an ESM-2 PCA-256 random forest, and a combined model that concatenates ESM-2 PCA components with BioPython features. All variants were evaluated under cluster-aware 5-fold cross-validation (GroupKFold over ESM-2 embedding-similarity clusters) with isotonic calibration, and standard stratified 5-fold cross-validation is reported as a sensitivity analysis. Under cluster-aware 5-fold cross-validation (GroupKFold over ESM-2 embedding-similarity clusters, our defense against homology leakage), the combined model achieved an AUC of 0.869 plus or minus 0.036, robust against the original stratified-CV value (0.873) and the BioPython baseline (0.852). The ESM-2-only variants showed AUC drops of 0.024 to 0.046 between stratified and cluster-aware splits, indicating that some of the apparent ESM-2 contribution under standard CV reflects homology leakage. Negative recall was 9.4 percent under cluster-aware splitting versus 26.0 percent under stratified, confirming that the models apparent failure-detection capability was substantially inflated by within-fold homology. We report both numbers and treat the cluster-aware values as the primary results. We release the curated dataset, the trained model, and an interactive web tool at nativeready.netlify.app. In its current form, NativeReady should be interpreted primarily as a positive-suitability triage tool; failure prediction remains limited by the scarcity of experimentally documented negative cases. We propose a user-contribution mechanism to accumulate real failure data over time. To our knowledge, no published sequence-based predictor currently estimates native MS suitability before experimental screening, and NativeReady is the first open benchmark and triage model specifically designed for this task.

bioinformatics↗

bioRxiv2026

Interpretable Deep Learning-Based Multi-Omics Integrationfor Prognosis in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality worldwide, yet existing prognostic models incompletely capture its molecular heterogeneity. We developed an interpretable, attention-based multi-branch deep learning framework for multi-omics survival prediction in HCC. Using 358 TCGA LIHC patients with matched mRNA expression, miRNA expression, and DNA methylation data, we first reproduced the Chaudhary et al. autoencoder-based survival model as a baseline (C-index = 0.561, log-rank p = 3.10 x 10-2). We then designed a multi-branch architecture with omics-specific encoders, multi-head attention fusion, and Cox partial likelihood training, optimized via Bayesian hyperparameter search (100 Optuna trials). In 5-fold stratified cross-validation with nested feature selection (no data leakage), our attention model achieved a mean C-index of 0.683 {+/-} 0.039, outperforming the autoencoder baseline (0.561) and clinical-only model (0.637), and performing similarly to an AUTOSurv-like benchmark (0.697). Branch dropout enabled single-omics inference; external validation on the real GSE14520 cohort (n=221, mRNA) achieved a C-index of 0.637 (p = 0.004), comparable to Chaudhary et al.s reported 0.67 on the same data. Integrated gradients and attention weights highlighted features with prior links to HCC biology, including cell cycle genes (CCNA2, PLK1) and a Wnt pathway component (FZD7), along with candidate biomarkers stable across all cross-validation folds (PZP, SGCB, CD300LG, ZNF831 for mRNA; 12 miRNAs; 6 CpG sites). Differential expression analysis between model-defined risk groups identified 381 significant genes (Bonferroni p < 0.05), though this analysis is partly circular. Multivariable Cox regression indicated that the model-derived risk score adds prognostic value beyond clinical variables, with consistent performance across clinical subgroups, though clinical integration metrics were evaluated on training data. This framework provides a transparent, biologically grounded approach to multi-omics prognostication in HCC.

bioinformatics↗