Corinne Hrnicek
Dr. Chapman
Phoenix College Stem Train Scholarship
3 October 2023
Project Background
Research question: Can teachable machine learning be trained to identify bacteria accurately? Hypothesis: If we train teachable machine learning, it will accurately identify bacteria.
What is currently known about the topic is that there is a “well-known system, Mycin, which can diagnose bacterial infections. The input to Mycin is the set of symptoms experienced by a patient. Based on a set of rules triggered by each symptom, Mycin suggests the type of bacteria that could cause the problem.” (Lerner). “The researchers plan to use their platform to study more bacteria and media types, using the information to build a training data library of various bacterial types in additional media to reduce the collection and detection times for new samples.” (New)
It is important to expand on this current knowledge because “Expert systems cannot pass the Turing test because of their limited domain of expertise.”(Lerner) We can do more research and improve accuracy. Another reason this research is important is that “This approach eliminates the need for time-consuming culture-based colony isolation and resource-intensive molecular approaches for bacterial identification.”(Ma). It is important to work on this specifically because “ relatively few studies have been conducted to optimize BPA removal using microalgae-bacteria consortia and artificial intelligence to predict degradation rate, and even fewer research have been conducted on the kinetic models in the degradation of BPA. (Fu)
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 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.
“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. “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
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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
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