This chapter presents a machine-learning model, called ProteoRift, capable of predicting multiple properties of the peptide directly from the spectra, which helps to apply more targeted filters in the database search and provides up to 12× speedup as compared to mass-only filtering in peptide identification.
Bilal Shabbir, U. Tariq, Fahad Saeed· Methods in molecular biology· 0 citations
Virtual screening ranks candidate molecules against a protein target. Sequence-based deep learning avoids docking’s structural requirements, but pair-based models need one forward pass per protein-molecule pair and scale poorly to large libraries. Dual-encoder contrastive models remove that bottleneck, yet standard CLI...
Gabriel Bianchin de Oliveira, Fahad Saeed· bioRxiv· 0 citations
These findings suggest that adapting pre-trained ViTs with domain-specific CNN front-ends and strategic fine-tuning offers a robust, generalizable, and resource-efficient path forward for clinical seizure prediction systems.
Umair Mohammad, Paras Parani, Fahad Saeed· bioRxiv· 0 citations
Hypertension is the leading preventable risk factor for cardiovascular disease, yet continuous blood pressure (BP) monitoring is still largely inaccessible outside of clinical settings. Photoplethysmography (PPG), collected via consumer wearables, offers a promising non-invasive method for continuous cuffless BP monito...
N. Valencia, J. T. Rodriguez, M. A. Rahman et al.· International Conference on...· 0 citations
Over eight diverse protein foundational models trained on 550,120 SwissProt proteins with AlphaFold structures, enriched embeddings improved zero-shot remote homology retrieval, increasing Precision@10 and MRR by up to 0.13 and 0.11, respectively.
Gabriel Bianchin de Oliveira, Fahad Saeed· bioRxiv· 0 citations
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