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Feature importance in machine learning models: A fuzzy information fusion approach (2022)
Journal Article
Rengasamy, D., Mase, J. M., Kumar, A., Rothwell, B., Torres, M. T., Alexander, M. R., …Figueredo, G. P. (2022). Feature importance in machine learning models: A fuzzy information fusion approach. Neurocomputing, 511, 163-174. https://doi.org/10.1016/j.neucom.2022.09.053

With the widespread use of machine learning to support decision-making, it is increasingly important to verify and understand the reasons why a particular output is produced. Although post-training feature importance approaches assist this interpreta... Read More about Feature importance in machine learning models: A fuzzy information fusion approach.

Clinical laboratory parameters and comorbidities associated with severity of coronavirus disease 2019 (COVID-19) in Kurdistan Region of Iraq (2022)
Journal Article
Ali, H. N., Ali, K. M., Rostam, H. M., Ali, A. M., Tawfeeq, H. M., Fatah, M. H., & Figueredo, G. P. (2022). Clinical laboratory parameters and comorbidities associated with severity of coronavirus disease 2019 (COVID-19) in Kurdistan Region of Iraq. Practical Laboratory Medicine, 31, Article e00294. https://doi.org/10.1016/j.plabm.2022.e00294

Background: The pandemic coronavirus disease (COVID-19) dramatically spread worldwide. Considering several laboratory parameters and comorbidities may facilitate the assessment of disease severity. Early recognition of disease progression associated... Read More about Clinical laboratory parameters and comorbidities associated with severity of coronavirus disease 2019 (COVID-19) in Kurdistan Region of Iraq.

Cluster analyses of the TCGA and a TMA dataset using the coexpression of HSP27 and CRYAB improves alignment with clinical-pathological parameters of breast cancer and suggests different epichaperome influences for each sHSP (2022)
Journal Article
Quinlan, P. R., Figeuredo, G., Mongan, N., Jordan, L. B., Bray, S. E., Sreseli, R., …Quinlan, R. A. (2022). Cluster analyses of the TCGA and a TMA dataset using the coexpression of HSP27 and CRYAB improves alignment with clinical-pathological parameters of breast cancer and suggests different epichaperome influences for each sHSP. Cell Stress and Chaperones, 27(2), 177-188. https://doi.org/10.1007/s12192-022-01258-0

Our cluster analysis of the Cancer Genome Atlas for co-expression of HSP27 and CRYAB in breast cancer patients identified three patient groups based on their expression level combination (high HSP27 + low CRYAB; low HSP27 + high CRYAB; similar HSP27 ... Read More about Cluster analyses of the TCGA and a TMA dataset using the coexpression of HSP27 and CRYAB improves alignment with clinical-pathological parameters of breast cancer and suggests different epichaperome influences for each sHSP.