Turning chaotic sample group clusterization into organized ones by feature selection: Application on photodiagnosis of Brucella abortus serological test

de Rezende, Bruno Silva; Franca, Thiago; de Paula, Maykko Antônyo Bravo; Cleveland, Herbert Patric Kellermann; Cena, Cícero; do Nascimento Ramos, Carlos Alberto

doi:10.1016/j.jphotobiol.2023.112781

Cited by 3 publications

(1 citation statement)

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“…In summary, both groups form the same cluster, which hinders the group classification with PC1 and PC2, and the loading shows that the data variance is concentrated in the main bands observed in each spectral rangeas expected. Previous studies have shown the potential use of high-order PCs to improve clustering and group separation. , We used these high-order PCs based on the prediction model performance in the leave-one-out cross-validation (LOOCV) and validation tests. Figure shows the score plot and loading for the three sample sets in the 1800–800 cm –1 range.…”

Section: Resultsmentioning

confidence: 99%

Diagnosis of Cutaneous Leishmaniasis Using FTIR Spectroscopy and Machine Learning: An Animal Model Study

Pacher,

Franca,

Lacerda

et al. 2024

ACS Infect. Dis.

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Cutaneous leishmaniasis (CL) is a polymorphic and spectral skin disease caused by Leishmania spp. protozoan parasites. CL is difficult to diagnose because conventional methods are time-consuming, expensive, and low-sensitive. Fourier transform infrared spectroscopy (FTIR) with machine learning (ML) algorithms has been explored as an alternative to achieve fast and accurate results for many disease diagnoses. Besides the high accuracy demonstrated in numerous studies, the spectral variations between infected and noninfected groups are too subtle to be noticed. Since variability in sample set characteristics (such as sex, age, and diet) often leads to significant data variance and limits the comprehensive understanding of spectral characteristics and immune responses, we investigate a novel methodology for diagnosing CL in an animal model study. Blood serum, skin lesions, and draining popliteal lymph node samples were collected from Leishmania (Leishmania) amazonensis-infected BALB/C mice under experimental conditions. The FTIR method and ML algorithms accurately differentiated between infected (CL group) and noninfected (control group) samples. The best overall accuracy (∼72%) was obtained in an external validation test using principal component analysis and support vector machine algorithms in the 1800−700 cm −1 range for blood serum samples. The accuracy achieved in analyzing skin lesions and popliteal lymph node samples was satisfactory; however, notable disparities emerged in the validation tests compared to results obtained from blood samples. This discrepancy is likely attributed to the elevated sample variability resulting from molecular compositional differences. According to the findings, the successful functioning of prediction models is mainly related to data analysis rather than the differences in the molecular composition of the samples.

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Section: Resultsmentioning

confidence: 99%