Application of probabilistic neural network in the clinical diagnosis of cancers based on clinical chemistry data

As a recently developed and powerful classification tool, probabilistic neural network was used to distinguish cancer patients from healthy persons according to the levels of nucleosides in human urine. Two datasets (containing 32 and 50 patterns, respectively) were investigated and the total consistency rate obtained was 100% for dataset 1 and 94% for dataset 2. To evaluate the performance of probabilistic neural network, linear discriminant analysis and learning vector quantization network, were also applied to the classification problem. The results showed that the predictive ability of the probabilistic neural network is stronger than the others in this study. Moreover, the recognition rate for dataset 2 can achieve to 100% if combining, these three methods together, which indicated the promising potential of clinical diagnosis by combining different methods. (C) 2002 Elsevier Science B.V. All rights reserved.

DNA diagnosis by capillary electrophoresis and microfabricated electrophoretic devices

DNA diagnosis is experiencing an impressive progression towards the development of novel technology to identity various clinically relevant categories of genetic changes and to meet the exponential growth of genomics. The introduction of capillary electrophoresis has dramatically accelerated the completion of the first draft of the human DNA sequence in the Human Genome Project, and thus, has become the method of choice for analysis of various genetic variants. The recent development of microfabricated electrophoretic devices has led to the possibility of integrating multiple sample handling with the actual measurement steps required for automation of molecular diagnostics. This review highlights the most recent progress in capillary electrophoresis and electrophoretic microdevices for DNA-based diagnostics, including the important areas of genotyping for point mutation, single nucleotide polymorphisms, short tandem repeats and organism identification. The application of these techniques for infectious and genetic disease diagnosis, as well as forensic identification purpose, are covered. The promising development and the challenges for techinical problems are also discussed.

Artificial neural network classification based on high-performance liquid chromatography of urinary and serum nucleosides for the clinical diagnosis of cancer

Nucleosides in human urine and serum have frequently been studied as a possible biomedical marker for cancer, acquired immune deficiency syndrome (AIDS) and the whole-body turnover of RNAs. Fifteen normal and modified nucleosides were determined in 69 urine and 42 serum samples using high-performance liquid chromatography (HPLC). Artificial neural networks have been used as a powerful pattern recognition tool to distinguish cancer patients from healthy persons. The recognition rate for the training set reached 100%. In the validating set, 95.8 and 92.9% of people were correctly classified into cancer patients and healthy persons when urine and serum were used as the sample for measuring the nucleosides. The results show that the artificial neural network technique is better than principal component analysis for the classification of healthy persons and cancer patients based on nucleoside data. (C) 2002 Elsevier Science B.V. All rights reserved.