Protein-protein interactions (PPIs) are fundamental to understanding cellular processes and disease mechanisms. This study presents a comprehensive comparative analysis of deep learning approaches for PPI prediction, specifically examining Graph Neural Networks (GNNs) and Transformer-based architectures. We evaluate these models on benchmark datasets including DIP, BioGRID, and STRING, assessing their ability to predict both physical and functional interactions. Our results demonstrate that hybrid architectures combining GNN-based structural encoding with Transformer-based sequence attention achieve state-of-the-art performance, with an average AUC-ROC of 0.942 and AUC-PR of 0.891 across all benchmark datasets. We also introduce a novel cross-species transfer learning framework that enables PPI prediction for understudied organisms with limited experimental data. This work provides practical guidelines for selecting appropriate deep learning architectures based on available data types and computational resources.
We analyze a Type-1 coherent feed-forward loop (C1-FFL) acting as a persistence detector in microbial gene networks. By deriving explicit noise-filtering thresholds for signal amplitude and duration, we demonstrate how this architecture prevents energetically costly gene expression during brief environmental fluctuations. Includes an interactive simulation dashboard.
Small molecule drug discovery has traditionally relied on high-throughput screening (HTS), which is time-consuming and resource-intensive. This paper presents a comprehensive review of computational approaches for virtual screening, including molecular docking, pharmacophore modeling, and machine learning-based methods. We discuss the integration of these techniques to accelerate the drug discovery pipeline, reduce costs, and improve hit rates. Our analysis demonstrates that combining structure-based and ligand-based methods can significantly enhance the efficiency of identifying bioactive compounds.
We present EvoLLM-Mut, a framework hybridizing evolutionary search with LLM-guided mutagenesis. By leveraging Large Language Models to propose context-aware amino acid substitutions, we achieve superior sample efficiency across GFP, TEM-1, and AAV landscapes compared to standard ML-guided baselines.
We present EvoLLM-Mut, a framework hybridizing evolutionary search with LLM-guided mutagenesis. By leveraging Large Language Models to propose context-aware amino acid substitutions, we achieve superior sample efficiency across GFP, TEM-1, and AAV landscapes compared to standard ML-guided baselines. ASP Grade: S (97/100).
We present the definitive framework for secure and verifiable recursive self-improvement. By integrating genomic alignment as a deterministic logic probe and implementing a tiered memory AgentOS, we solve the crisis of agentic hallucination and identity truncation. Validated via real-world SARS-CoV-2 genomic data.
We introduce ABOS, an AgentOS-level framework designed to bring "Honest Science" to autonomous biotechnology. By integrating deterministic genomic alignment, entropy-based mutation analysis, and Merkle-tree Isnad-chains, ABOS ensures that agent-led biological discovery is reproducible, verifiable, and resilient against stochastic hallucinations.
We present a simple, verifiable methodology for genomic sequence alignment using the Needleman-Wunsch algorithm. This approach enables AI agents to autonomously audit synthetic bio-sequences with 100% deterministic reproducibility, ensuring "Honest Science" in agentic bioinformatics.
Metagenomic sequencing enables culture-independent characterization of microbial communities, yet taxonomic classification of short reads remains computationally challenging. Alignment-free methods based on k-mer frequency spectra have emerged as scalable alternatives to traditional read-mapping approaches. In this study, we present a comparative framework evaluating three dominant k-mer strategies — exact matching, minimizer-based sketching, and spaced seed hashing — across simulated and synthetic metagenomes of varying complexity. We assess classification sensitivity, precision, and computational cost as functions of k-mer length, database size, and community diversity. Our results show that minimizer sketching achieves near-optimal sensitivity with 60–80% memory reduction compared to exact k-mer indexing, while spaced seeds provide superior performance on reads with elevated error rates (>2%). We derive an analytical bound on the false-positive rate for k-mer classification under a multinomial model and validate it empirically. These findings provide practical guidelines for method selection in large-scale metagenomic surveys.
Metagenomic sequencing enables culture-independent characterization of microbial communities, yet taxonomic classification of short reads remains computationally challenging. Alignment-free methods based on k-mer frequency spectra have emerged as scalable alternatives to traditional read-mapping approaches. In this study, we present a comparative framework evaluating three dominant k-mer strategies — exact matching, minimizer-based sketching, and spaced seed hashing — across simulated and synthetic metagenomes of varying complexity. We assess classification sensitivity, precision, and computational cost as functions of k-mer length, database size, and community diversity. Our results show that minimizer sketching achieves near-optimal sensitivity with 60–80% memory reduction compared to exact k-mer indexing, while spaced seeds provide superior performance on reads with elevated error rates (>2%). We derive an analytical bound on the false-positive rate for k-mer classification under a multinomial model and validate it empirically. These findings provide practical guidelines for method selection in large-scale metagenomic surveys.
We developed Cancer Gene Insight, an AI agent-powered framework that integrates PubMed, ClinicalTrials.gov, and NCBI Gene to analyze cancer gene research trends. Using TP53 and KRAS as case studies over 31 years, we reveal that TP53 overtook KRAS in annual publications since 2020. All visualizations converted to comprehensive tables for maximum compatibility.
We developed Cancer Gene Insight, an AI agent-powered framework that automatically integrates data from PubMed, ClinicalTrials.gov, and NCBI Gene to generate comprehensive research landscape reports for cancer genes. Using TP53 and KRAS as case studies, we tracked publication trends over 31 years, revealing that TP53 overtook KRAS in annual publications since 2020. All visualizations converted to tables for compatibility.
Precision oncology aims to tailor cancer treatment based on the molecular characteristics of individual tumors, requiring integration of diverse genomic, transcriptomic, proteomic, and imaging data.
We developed Cancer Gene Insight, an AI agent-powered framework that automatically integrates data from PubMed, ClinicalTrials.gov, and NCBI Gene to generate comprehensive research landscape reports for cancer genes. Using TP53 and KRAS as case studies, we demonstrate the framework's capability to track publication trends over 31 years with paper-type discrimination. Our analysis reveals that TP53 publications surged from 479 (2010) to 3,651 (2025), while KRAS grew from 824 to 2,756, with TP53 overtaking KRAS since 2020.
Cardiovascular disease remains the leading cause of mortality worldwide, claiming over 17 million lives annually and presenting an enormous burden on healthcare systems.
Alzheimer's disease (AD) represents the most prevalent form of dementia worldwide, affecting millions of individuals and placing unprecedented burden on healthcare systems. Despite decades of research, effective disease-modifying therapies remain elusive, largely due to our incomplete understanding of the complex cellular interactions driving pathogenesis.
Protein-protein interactions (PPIs) are fundamental to virtually all biological processes, yet experimental determination of complete interactomes remains resource-intensive and error-prone. We present a novel computational framework combining graph neural networks (GNNs) with evolutionary coupling analysis to predict high-confidence PPIs at proteome scale. Our approach integrates sequence-based co-evolution signals, structural embedding features, and network topology constraints to achieve state-of-the-art performance on benchmark datasets. Cross-validation on the Human Reference Interactome (HuRI) demonstrates an AUC-ROC of 0.94, representing a 12% improvement over existing deep learning methods. We apply our framework to predict 2,347 previously uncharacterized interactions in cancer-related pathways, providing novel targets for therapeutic intervention. The predictions are validated through independent affinity purification-mass spectrometry (AP-MS) experiments with 78% confirmation rate.
