The algorithm is designed to identify patterns in the treatment dataset.

<p>This process involves two key functions. First, from lines 1 to 8, the algorithm identifies amino acids that meet the occurrence threshold, starting with patterns of length 1. It then iteratively extends these potential patterns by adding amino acids to previously identified patterns of max...

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Bibliographic Details
Main Author:	Belén Otero-Carrasco (21262360) (author)
Other Authors:	Paloma Tejera Nevado (15060625) (author), Rafael Artiñano Muñoz (21262363) (author), Gema Díaz Ferreiro (21262366) (author), Aurora Pérez Pérez (21262369) (author), Juan Pedro Caraça-Valente Hernández (21262372) (author), Alejandro Rodríguez-González (18550155) (author)
Published:	2025
Subjects:	Biochemistry Medicine Genetics Molecular Biology Pharmacology Biotechnology Cancer Infectious Diseases Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified providing novel insights fundamental biomolecules composed effective therapeutic strategies biomedical data facilitate novel computational framework related mortality worldwide xlink "> proteins lung cancer treatment lung cancer cases suggest potential opportunities significant sequence patterns computational approach designed various biological functions lung cancer drug lung cancer proteins related computational analyses biological roles target proteins proteins targeted proteins linked proteins associated thorough understanding specific patterns regulatory processes protein structures protein functionality primary focus living organisms leading cause finding patterns establishing connections drugs used drug repurposing developing targeted closely linked amino acids
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The algorithm is designed to identify patterns in the treatment dataset.

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