Research proposal
Corinne Hrnicek
Dr. Chapman
Phoenix College Stem Train Scholarship
12 September 2023
Research Proposal
This topic was selected because it is significant in medicine, biology, and technology. Learning more about this topic and researching to identify bacteria faster is essential. An example of how teachable machine learning can identify bacteria in a medical setting is stated in the article. “Gram-negative bacteria (ESBL-GNB) accounting for individual- and group-level confounding using machine-learning methods. Patients hospitalized between September 2010 and June 2013 at six medical and six surgical wards in Italy, Serbia and Romania were screened for ESBL-GNB”(Tacconelli). That is why it is important to research this topic.
The research will be conducted by observing bacteria, taking pictures, and experimenting with teachable machine learning. “In the last decade, NIR spectroscopy has been applied to identify bacteria, fungi, and viruses, combined with Machine Learning algorithms and Multivariate Analysis techniques”(Farias,). In a similar way, the bacteria will be observed when experimenting with a teachable machine.
Variable table
“The clustering algorithms…applied to the dataset) and their performance was evaluated by means of accuracy”(Duran)
I anticipate finding that teachable machine learning can be trained to Identify bacteria.
Research question: Can teachable machine learning be trained to identify bacteria accurately? “We have an important biological question. The whole study aims to answer this biological question via developing a specific classification model for AMP prediction”(Söylemez).
My hypothesis is that yes, if we train teachable machine learning then it will accurately identify bacteria. “Ranking and selection of probiotic potential bacteria for harnessing as antibacterial agents in plant tissue cultures were performed using supervised machine learning models.”(Sadeghi). Another source also supports the hypothesis that “a machine learning-based method can perform fast estimation of the concentration of indoor airborne culturable bacteria. “(Liu)
5.
References
Durán, C., Ciucci, S., Palladini, A., Ijaz, U. Z., Zippo, A. G., Sterbini, F. P., Masucci, L., Cammarota, G.,
Ianiro, G., Spuul, P., Schroeder, M., Grill, S. W., Parsons, B. N., Pritchard, D. M., Posteraro, B., Sanguinetti, M., Gasbarrini, G., Gasbarrini, A., & Cannistraci, C. V. (2021). Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome. Nature Communications, 12(1), 1926–1926. https://doi.org/10.1038/s41467-021-22135-x
Farias, L. R., Panero, J. D., Riss, J. S., Correa, A. P., Vital, M. J., & Panero, F. D. (2023). Rapid and
Green Classification Method of Bacteria Using Machine Learning and NIR Spectroscopy. Sensors (Basel, Switzerland), 23(17), 7336. https://doi.org/10.3390/s23177336
Liu, Z., Li, H., & Cao, G. (2017). Quick Estimation Model for Indoor Airborne Culturable Bacteria
Concentration: An Application of Machine Learning. International Journal of Environmental Research and Public Health, 14(8), 857. https://doi.org/10.3390/ijerph14080857
Sadeghi, M., Panahi, B., Mazlumi, A., Hejazi, M. A., Komi, D. E., & Nami, Y. (2022). Screening of
potential probiotic lactic acid bacteria with antimicrobial properties and selection of superior bacteria for application as biocontrol using machine learning models. Food Science & Technology, 162, 113471. https://doi.org/10.1016/j.lwt.2022.113471
Söylemez, Ü. G., Yousef, M., Kesmen, Z., Büyükkiraz, M. E., & Bakir-Gungor, B. (2022). Prediction of
Linear Cationic Antimicrobial Peptides Active against Gram-Negative and Gram-Positive Bacteria Based on Machine Learning Models. Applied Sciences, 12(7), 3631. https://doi.org/10.3390/app12073631
Tacconelli, E., Górska, A., De Angelis, G., Lammens, C., Restuccia, G., Schrenzel, J., Huson, D. H.,
Carević, B., Preoţescu, L., Carmeli, Y., Kazma, M., Spanu, T., Carrara, E., Malhotra-Kumar, S., & Gladstone, B. P. (2020). Estimating the association between antibiotic exposure and colonization with extended-spectrum β-lactamase-producing Gram-negative bacteria using machine learning methods: a multicentre, prospective cohort study. Clinical Microbiology and Infection, 26(1), 87–94. https://doi.org/10.1016/j.cmi.2019.05.013