VeloHARMO achieves superior translation velocity harmonization across organisms using deep learning and genetic algorithms, outperforming existing methods in prediction accuracy.
Briefings in Bioinformatics
Oxford University Press · Genomics & Bioinformatics · ISSN 1467-5463, 1477-4054
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DeepIR-Pred accurately identifies insulin receptor proteins with over 10-fold improved robustness and generalization using deep recurrent learning and biologically informed features.
Gene-Chronos accurately infers developmental time across diverse species, outperforming existing methods with a 20% improvement in robustness and generalization.
Masked-multi-layer perceptron improves robustness of genetic risk scores, achieving a Spearman correlation of 0.951 with noisy data compared to 0.669 for traditional methods.
A targeted RNA sequencing panel with 48 cMHs achieves near-perfect discrimination power (up to 0.99999999) for five body fluids in East Asian populations.
ContiTE achieves a 12.09% increase in Pearson correlation and 18.22% boost in R² for cross-tissue mRNA translation efficiency prediction using a continuous manifold MoE framework.
FlexBIP, a modular framework for biomolecular interaction prediction, outperforms 25 state-of-the-art models across 15 datasets, including cold-start scenarios.
PGpath generates population-adapted linear reference genomes from pangenome subpaths, improving read mapping and variant detection accuracy in large-scale genomic analysis.
Dynamic perturbation analysis uncovers extensive modular rewiring in gastric cancer progression despite limited transcriptional changes, revealing key driver programs and regulators.
Ribo-seq rRNA reads reveal consistent signatures of ribosome heterogeneity and collisions across multiple studies.
Correlation analysis of 6752 blood metabolites reveals that structurally related clusters have up to 0.6 correlation in abundance across human studies.
Joint modeling approaches improve power and resolution for shared causal variants in multi-ancestry fine-mapping studies.
Incorporating linear and circular transcript information with filtering improves circRNA differential expression analysis, with edgeR's filterByExpr() providing the most reliable results across datasets.
DeepU-PRS enhances SNP heritability modeling by incorporating nonlinear and uncertainty-aware methods, improving prediction accuracy by approximately 10% in UK Biobank traits.
Integrating knowledge graph neural networks with multi-omics data enhances synthetic lethality prediction accuracy in cancer research.
A national bioinformatics competency framework in Thailand addresses workforce gaps by defining core skills and aligning education with labor market demands.
KAT-Net achieves superior joint lysine acylation site prediction by integrating sequence and structural data with hyperbolic routing, outperforming single-task models in benchmark tests.
Advances in machine learning have transformed genetic variant effect prediction from hand-crafted features to foundation models that learn directly from biological sequences.
Topology-guided dual-masked contrastive learning achieves accurate cell-type annotation with minimal labels in single-cell multi-omics.
Deconformer accurately estimates cfRNA contributions across 60 human cell types with lower computational cost than existing methods.
ToxMamba achieves up to 18.3% higher F1 scores and 46.9% better Matthews Correlation Coefficient than existing methods in toxic peptide prediction.
Cell2space accurately reconstructs multi-scale tissue architecture from scRNA-seq and spatial transcriptomics data, achieving superior domain and neighborhood inference.
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