Utilização de aprendizado de máquina para classificação de bactérias através de proteínas ribossomais - (:unkn) unknown (Universidade Estadual de Ponta Grossa, 2017)
Identification of microorganisms in health and agriculture areas is essential to understand the composition and development of the environment. New techniques are seeking to identify these microorganisms with more accuracy, speed and at a lower cost. Nowadays, a technique that is increasingly studied and used is the identification of microorganisms through mass spectra, generated by mass spectrometry. The mass spectra are able to generate a recognition profile from a microorganism, using the referring peaks to the most abundant molecular masses recorded in the spectrum. By analyzing the peaks, it is possible to designate a pattern, such as a fingerprint, to recognize a microorganism; this technique is known as the Peptide Mass Fingerprint (PMF). Another way to identify a mass spectrum is through the peaks that are expected to appear in the spectrum, which model this work used. To predict the expected peaks in the spectrum, the estimated molecular weights of ribosomal proteins were calculated. These proteins are responsible for the cellular functioning itself, so-called housekeeping. Besides they being abundant in the prokaryotic content, they are highly conserved, not altering their physiology to different environments or cell stage. The estimated weights formed a presumed database, containing all the information obtained from the NCBI’s repository. This presumed database was generalized at the specie level and later submitted to a machine learning algorithm. With this, it was possible to obtain a microorganism’s classificatory model based on ribosomal proteins values. Using the generated model by the machine learning, a software called Ribopeaks was developed to classify the microorganisms at the specie level with an accuracy of 94.83%, considering the related species. It was also observed the results at genus level, which obtained 98.69% of assertiveness. Values of biological ribosomal molecular masses from the literature were also tested in the acquihired model, obtaining a total assertiveness of 84.48% at the specie level, and 90.51% at the genus level.
Keywords: Espectrometria de massa; Proteínas ribossomais; Pesos moleculares estimados; Aprendizado de máquina; Mass spectrometry; Ribosomal proteins; Estimated molecular weights; Machine learning; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO