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<h4>Summary</h4>This study presents dAMN, a genome-scale neural-mechanistic hybrid model that combines neural networks with dynamic flux balance analysis to predict bacterial growth dynamics across diverse nutrient environments. Using a residual network architecture, dAMN predicts reaction …
Read PDF<h4>Motivation</h4>Drug combinations are crucial for overcoming resistance in cancer therapy. Although deep learning has achieved strong performance in synergy prediction, existing models often treat cell-specific features and paired drugs as a static background and fail …
Read PDFThe imperative to re-analyze existing public sequencing data is central to modern biology, driven by new hypotheses and advanced analytical methods. However, this effort is critically hampered by the profound heterogeneity of repository data, particularly …
Read PDF<h4>Background</h4>Hydatid cyst disease is caused by the parasite Echinococcus and poses significant health concerns worldwide. Due to the lack of early symptoms and limited diagnostic tools, researchers aim to design a more specific and sensitive …
Read PDF<h4>Motivation</h4>Glycans are highly diverse biological sequences, but their functional understanding has lagged behind proteins and nucleic acids. Many glycans remain ambiguously annotated, limiting computational analyses. Existing computational approaches are primarily graph-based, capturing local structural features …
Read PDF<h4>Motivation</h4>Multiple sequence alignment (MSA) remains a core problem in bioinformatics, yet most widely used alignment methods still rely on static amino acid substitution matrices that cannot adapt to sequence-specific context.<h4>Results</h4>BABAPPAlign is a progressive MSA engine …
Read PDF<h4>Motivation</h4>Approximate string matching (ASM) is the problem of finding all occurrences of a pattern in a text while allowing up to k errors. Many modern methods use seed-chain-extend, which is fast in practice, but does …
Read PDF[This corrects the article DOI: 10.3389/fbinf.2026.1764743.].
Read PDF<h4>Motivation</h4>Named entity recognition (NER) is a fundamental component of structured knowledge extraction, yet its effectiveness in emerging domains remains by the scarcity of high-quality, domain-specific annotated corpora. Although data augmentation and distant supervision have been …
Read PDF<h4>Premise</h4>Hybrid capture sequencing (Hyb-Seq) is a widely used approach in phylogenomics, providing efficient access to targeted genomic regions. However, deriving high-quality phylogenetic trees from raw sequencing reads requires extensive bioinformatics processing, which increases complexity, the …
Read PDF<h4>Introduction</h4>Selecting appropriate bioinformatics tools is critical for accurate and reproducible analysis, particularly in support of genomic surveillance and molecular biomarker monitoring. The importance of these analyses is underscored by the need for effective public health …
Read PDFLiquid-liquid phase separation (LLPS) has emerged as a fundamental mechanism underlying the formation and regulation of membraneless cellular compartments and is increasingly implicated in diverse physiological processes and diseases. Alongside rapid experimental and high-throughput advances, …
Read PDF<h4>Motivation</h4>Reliable predictions of protein-protein binding affinities are essential for molecular biology and therapeutic discovery. However, most computational methods rely on three-dimensional structural models, which are often unavailable for many complexes.<h4>Results</h4>We introduce BindPred, a structure-agnostic input …
Read PDF[This corrects the article DOI: 10.3389/fbinf.2025.1738737.].
Read PDFMetabolic reprogramming is central to cancer biology, enabling tumor cells to sustain rapid proliferation, resist stress, and adapt to therapy. However, these alterations are highly heterogeneous across cancer types, and current treatments rarely exploit subtype-specific …
Read PDF<h4>Motivation</h4>Illumina Infinium BeadChip technology for DNA methylation analysis continues to expand, with the latest EPICv2 arrays targeting about a million loci. As data volumes from this technology continue to grow, there is increasing demand for …
Read PDF<h4>Motivation</h4>Advances in high-throughput chromatin conformation capture have provided insight into the three-dimensional structure and organization of chromatin. While bulk Hi-C experiments capture spatio-temporally averaged chromatin interactions across millions of cells, single-cell Hi-C experiments report on …
Read PDF<h4>Motivation</h4>Identifying synergistic drug combinations holds promise for more effective treatment strategies. Recent deep learning methods such as Transformers and Graph Neural Networks have shown improved predictive performance, but most of them integrate drug and cell …
Read PDF<h4>Motivation</h4>Genome-scale metabolic models lack explicit regulatory mechanisms, limiting their predictive accuracy for genetic interventions. Current methods for computing genetic Minimal Cut Sets either ignore regulatory networks entirely or use simplified acyclic representations that cannot capture …
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