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Vazquez-Naya, J. M.

Publications and source records attributed to Vazquez-Naya, J. M..

2 recordsLinked to original sources

Viral Sentry AI (VirSentAI) - Automated Zoonotic Surveillance & Drug Repurposing Agent

Zoonotic viruses capable of jumping from animal reservoirs into human populations represent a persistent and unpredictable menace to global health. To confront this challenge, we developed Viral Sentry AI (VirSentAI), an autonomous agent designed to close the gap between viral emergence and therapeutic response. Unlike static analysis tools, VirSentAI operates as a continuous sentinel, automatically scanning public databases (e.g., NCBI) for new viral genomes and executing a three-stage agentic surveillance workflow, with distinct, specialized AI architectures for generated text, macromolecule sequences, and drug chemical data. First, the system is using gemma-2-9b, utilizing this Large Language Model to parse unstructured submission records and extract critical meta-information that provides context to the raw data. In the second stage, the system employs a novel deep-learning topology, virsentai-v2-hyena-dna-16k, a fine-tuned HyenaDNA model capable of processing complete viral genomes up to 160,000 bases. This architecture captures subtle, long-range genomic dependencies to predict human infectivity with high precision. Upon flagging a high-risk pathogen, the agent autonomously triggers a downstream therapeutic module as the stage three. It extracts NCBI viral protein sequences and utilizes a PLAPT (Protein-Ligand Affinity Prediction Transformer) model to calculate affinity interactions against the ChEMBL-curated set of approved therapeutics, instantly identifying candidates for drug repurposing. In rigorous cross-validation on a curated dataset of 31,728 complete viral genomes, the surveillance module demonstrated robust discriminatory power, achieving an AUROC of 0.95 in classifying human host potential. By integrating state-of-the-art genomic modeling with automated lead compound screening, VirSentAI offers a proactive, end-to-end solution for pandemic preparedness. The platform is freely accessible at https://muntisa.github.io/virsentai, with source code available at https://github.com/muntisa/virsentai.

bioinformatics↗

Prediction of breast cancer proteins using molecular descriptors and artificial neural networks: a focus on cancer immunotherapy proteins, metastasis driver proteins, and RNA-binding proteins

BackgroundBreast cancer (BC) is a heterogeneous disease characterized by an intricate interplay between different biological aspects such as ethnicity, genomic alterations, gene expression deregulation, hormone disruption, signaling pathway alterations and environmental determinants. Due to the complexity of BC, the prediction of proteins involved in this disease is a trending topic in drug design. MethodsThis work is proposing accurate prediction classifier for BC proteins using six sets of protein sequence descriptors and 13 machine learning methods. After using a univariate feature selection for the mix of five descriptor families, the best classifier was obtained using multilayer perceptron method (artificial neural network) and 300 features. ResultsThe performance of the model is demonstrated by the area under the receiver operating characteristics (AUROC) of 0.980 {+/-} 0.0037 and accuracy of 0.936 {+/-} 0.0056 (3-fold cross-validation). Regarding the prediction of 4504 cancer-associated proteins using this model, the best ranked cancer immunotherapy proteins related to BC were RPS27, SUPT4H1, CLPSL2, POLR2K, RPL38, AKT3, CDK3, RPS20, RASL11A and UBTD1; the best ranked metastasis driver proteins related to BC were S100A9, DDA1, TXN, PRNP, RPS27, S100A14, S100A7, MAPK1, AGR3 and NDUFA13; and the best ranked RNA-binding proteins related to BC were S100A9, TXN, RPS27L, RPS27, RPS27A, RPL38, MRPL54, PPAN, RPS20 and CSRP1. ConclusionsThis powerful model predicts several BC-related proteins which should be deeply studied to find new biomarkers and better therapeutic targets. The script and the results are available as a free repository at https://github.com/muntisa/neural-networks-for-breast-cancer-proteins.

bioinformatics↗