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Prof. Oliveira Osvaldo

更新时间:2018/3/28 16:23:15
Resume: Osvaldo N. Oliveira Jr. is a professor at the Sao Carlos Institute of Physics, University of Sao Paulo, Brazil. He has led research into the fabrication of novel materials in the form of ultrathin films for a variety of applications, including electronic tongues and biosensors for the diagnosis of cancer. He has pioneered the combined use of methods from distinct fields of science, with the merge of methods of statistical physics and computer science to process text, and use of information visualization to enhance the performance of sensing and biosensing. This work is associated with the merge of nanotechnology with Big Data Analytics and machine learning. Prof. Oliveira is the current president of the Brazilian Materials Research Society, and eHassociate editor of ACS Applied Materials & Interfaces. In 2006 he was awarded the Scopus Prize, given to 16 Brazilian researchers considered the most productive in terms of papers published and citations.

Machine learning for data analysis and biosensors to detect cancer biomarkers
Nanomaterials have been used in conjunction with biomolecules in a variety of biosensors used in clinical diagnosis, where synergy is sought in the combination of distinct materials in the same sensing unit. In this lecture an overview will be presented of film nanoarchitectures for immunosensors and genosensors employed to detect cancer biomarkers. The data obtained with electrochemical methods and impedance spectroscopy are analyzed with computational methods, including multidimensional projections and genetic algorithms for feature selection. With such methods it is possible to detect biomarkers for different types of cancer with high sensitivity and selectivity, even in blood or tumor samples of patients. Also emphasized is the need of sophisticated data analysis for developing computer-assisted diagnosis systems, in which machine learning may be employed to deal with data of different natures. The prospects of a revolution in clinical analysis owing to the merge of nanotechnology, Big Data and machine learning will be discussed.