Evolving Neural Networks To
Flow Cytometric Data Interpretation
Author(s)
G. C. Pereira, A. Bonomo & N. F. F. Ebecken
Abstract
Flow cytometry is a method largely employed in research and medical practice
as a diagnostic exam on grave diseases, such as infections by HIV viruses and
mainly leukemia. This paper aims to introduce a new methodological approach.
Artificial neural networks based on supervised training are applied to pattern
recognition of their optical blood cell population coming from patients.
Nonlinear system identification via artificial neural networks consists of
adjusting the network in such a way that it approximately describes the inputoutput
mapping of the system. In its complete form, network induction involves
both parametric and structural learning. The two tasks can be seen as
optimization problems and, as such, they may present multiple local minima,
non-differentiability and discontinuity. The idea of applying Genetic Algorithms
to the problem of complete network acquisition comes naturally.
The preliminary results demonstrate that this approach is promising and can
soon contribute to protocols related to this disease.
Keywords: flow cytometry; neural network; diagnostic.
1 Introduction
Flow cytometers are equipments which use light dispersion and quantify
different cellular populations (Van Dam and Tjalma [28]). Depending of the
arrangement it can obtain two or thirteen simultaneous parameters for each cell
or organism. Many diseases depend on the right \“classification” from flow
cytometers to give an accurate diagnosis and subsequent treatment. Patients
infected, for instance by HIV viruses need their CD4+ cells quantified to suitable
Keywords
flow cytometry; neural network; diagnostic.
Related Book
Other papers in this volume
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