EMERSON SOARES BERNARDES

EMERSON SOARES BERNARDES

(Fonte: Lattes)

Resumo

Bachelor's at Farmácia from Universidade Federal de Ouro Preto (1998) and doctorate at Applied Imunology from Universidade de São Paulo (2004). Has experience in Medicine, acting on the following subjects: galectina-3, carboidratos, trypanosoma cruzi, carcinogênesis and macrophage. (Text obtained from the Currículo Lattes on October 8th 2021)

Possui graduação em Farmácia pela Universidade Federal de Ouro Preto (1998), mestrado e doutorado em Imunologia Básica e Aplicada pela Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo (2004), com período de Doutorado Sanduíche pela Universidade da California, Davis, USA. Realizou pós-doutoramento durante o período de 2004 a 2008 pela Faculdade de Medicina da USP-Ribeirão Preto. Trabalhou como pesquisador contratado pelo Instituto de Patologia e Imunologia Molecular da Universidade do Porto - IPATIMUP em Portugal no período de 2008 a 2011. Retornou ao Brasil como pesquisador visitante na Faculdade de Medicina da USP - São Paulo (2011-2012) e foi posteriormente contratado como pesquisador no Instituto do Câncer do Estado de São Paulo (2012-2013). Coordenou um projeto Jovem Pesquisador financiado pela FAPESP (2012-2016 - Desenvolvimento e Produção de Radiofármacos Emissores de Pósitrons com Aplicações Diagnósticas em Oncologia) e está integrado como pesquisador Colaborador no Instituto de Pesquisas Energéticas e Nucleares (IPEN). Tem atuado na área da Glicobiologia, com ênfase na participação de proteínas ligantes de carboidratos em processos inflamatórios e no Câncer. Atualmente é professor do Programa de Pós-Graduação do IPEN-USP Tecnologia Nuclear - Aplicações, tem experiência na área de Radiofarmácia, com ênfase no desenvolvimento de Radiofármacos inéditos para diagnóstico e terapia em Oncologia. (Texto extraído do Currículo Lattes em 08 out. 2021)

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Recognition of breast cancer subtypes using FTIR hyperspectral data
2024 - FAROOQ, SAJID; DEL-VALLE, MATHEUS; SANTOS, SOFIA N. dos; BERNARDES, EMERSON S.; ZEZELL, DENISE M.
Fourier -transform infrared spectroscopy (FTIR) is a powerful, non-destructive, highly sensitive and a promising analytical technique to provide spectrochemical signatures of biological samples, where markers like carbohydrates, proteins, and phosphate groups of DNA can be recognized in biological micro -environment. However, method of measurements of large cells need an excessive time to achieve high quality images, making its clinical use difficult due to speed of data -acquisition and lack of optimized computational procedures. To address such challenges, Machine Learning (ML) based technologies can assist to assess an accurate prognostication of breast cancer (BC) subtypes with high performance. Here, we applied FTIR spectroscopy to identify breast cancer subtypes in order to differentiate between luminal (BT474) and nonluminal (SKBR3) molecular subtypes. For this reason, we tested multivariate classification technique to extract feature information employing three -dimension (3D) -discriminant analysis approach based on 3D -principle component analysis -linear discriminant analysis (3D-PCA-LDA) and 3D -principal component analysis -quadratic discriminant analysis (3D-PCA-QDA), showing an improvement in sensitivity (98%), specificity (94%) and accuracy (98%) parameters compared to conventional unfolded methods. Our results evidence that 3D-PCALDA and 3D-PCA-QDA are potential tools for discriminant analysis of hyperspectral dataset to obtain superior classification assessment.
Rapid identification of breast cancer subtypes using micro-FTIR and machine learning methods
2023 - FAROOQ, SAJID; DEL-VALLE, MATHEUS; SANTOS, MOISES O. dos; SANTOS, SOFIA N. dos; BERNARDES, EMERSON S.
Breast cancer (BC) molecular subtypes diagnosis involves improving clinical uptake by Fourier transform infrared (FTIR) spectroscopic imaging, which is a non-destructive and powerful technique, enabling label free extraction of biochemical information towards prognostic stratification and evaluation of cell functionality. However, methods of measurements of samples demand a long time to achieve high quality images, making its clinical use impractical because of the data acquisition speed, poor signal to noise ratio, and deficiency of optimized computational framework procedures. To address those challenges, machine learning (ML) tools can facilitate obtaining an accurate classification of BC subtypes with high actionability and accuracy. Here, we propose a ML-algorithmbased method to distinguish computationally BC cell lines. The method is developed by coupling the K-neighbors classifier (KNN) with neighborhood components analysis (NCA), and hence, the NCA-KNN method enables to identify BC subtypes without increasing model size as well as adding additional computational parameters. By incorporating FTIR imaging data, we show that classification accuracy, specificity, and sensitivity improve, respectively, 97.5%, 96.3%, and 98.2%, even at very low co-added scans and short acquisition times. Moreover, a clear distinctive accuracy (up to 9 %) difference of our proposed method (NCA-KNN) was obtained in comparison with the second best supervised support vector machine model. Our results suggest a key diagnostic NCA-KNN method for BC subtypes classification that may translate to advancement of its consolidation in subtype-associated therapeutics.
Breast cancer subtypes diagnostic via high performance supervised machine learning
2022 - FAROOQ, SAJID; DEL-VALLE, MATHEUS; SANTOS, MOISES O. dos; NASCIMENTO, SOFIA; BERNARDES, EMERSON S.; ZEZELL, DENISE M.
Aim: Breast cancer molecular subtypes are being used to improve clinical decision. The Fourier transform infrared (FTIR) spectroscopic imaging, which is a powerful and non-destructive technique, allows performing a non-perturbative and labelling free extraction of biochemical information towards diagnosis and evaluation for cell functionality. However, methods of measurements of large areas of cells demand a long time to achieve high quality images, making its clinical use impractical because of speed of data acquisition and dearth of optimized computational procedures. In order to cope with these challenges, Machine learning (ML) technologies can facilitate to obtain accurate prognosis of Breast Cancer (BC) subtypes with high action ability and accuracy. Methods: Here we propose a ML algorithm based method to distinguish computationally BC cell lines. The method is developed by coupling K neighbors Classifier (KNN) with Neighborhood Component Analysis (NCA) and NCA-KNN methods enables to identify BC subtypes without increasing model size as well additional parameters. Results: By incorporating FTIR imaging data, we show that using NCA-KNN method, the classification accuracies, specificities and sensitivities improve up to 97%, even at very low co-added scan (S_4). Moreover, a clear distinctive accuracy difference of our proposed method was obtained in comparison with other ML supervised models. Conclusion: For confirming our model results performance, the cross validation (k fold = 10) and receiver operation characteristics (ROC) curve were used and found in great agreement, suggest a potential diagnostic method for BC subtypes, even with small co-added scan < 8 at low spectral resolution (4 cm-1).
Identifying breast cancer cell lines using high performance machine learning methods
2022 - FAROOQ, SAJID; DEL-VALLE, MATHEUS; SANTOS, SOFIA; BERNARDES, EMERSON S.; ZEZELL, DENISE M.
We present a computational framework based on machine learning classifiers K-Nearest Neighbors and Neighborhood Component analysis for breast cancer (BC) subtypes prognostic. Our results has up to 97% accuracy for prognostic stratification of BC subtypes.
Superior Machine Learning Method for breast cancer cell lines identification
2022 - FAROOQ, SAJID; CARAMEL-JUVINO, AMANDA; DEL-VALLE, MATHEUS; SANTOS, SOFIA; BERNARDES, EMERSON S.; ZEZELL, DENISE M.
We propose an artificial intelligence platform based on machine learning (ML) algorithm using Neighborhood Component analysis and K-Nearest Neighbors for breast cancer cell lines recognition. Our model presents up to 97% accuracy for identification of breast cancer cell lines.