Epicenter location by analysis of interictal spikes: a case study for the use of artificial neural networks in biomedical engineering

Identification of Marker Genes for Cancer Based on Microarrays Using a Computational Biology Approach

Rapid advances in gene expression microarray technology have enabled to discover molecular markers used for cancer diagnosis, prognosis, and prediction. One computational challenge with using microarray data analysis to create cancer classifiers is how to effectively deal with microarray data which are composed of high-dimensional attributes (p) and low-dimensional instances (n). Gene selection and classifier construction are two key issues concerned with this topics. In this article, we reviewed major methods for computational identification of cancer marker genes. We concluded that simple methods should be preferred to complicated ones for their interpretability and applicability.

The use of neural networks in evaluation of the direction and dynamics of changes in lipid parameters in kidney transplant patients on the Mediterranean diet

OBJECTIVE

The objective of the study was to assess whether neural networks can be a tool useful in the evaluation of the effect of the Mediterranean diet (MD) on the direction and dynamics of selected parameters.

DESIGN

Randomized, prospective study.

SETTING

Outpatient Clinic of the Department of Nephrology, Transplantology, and Internal Medicine.

PATIENTS AND INTERVENTION

The study group consisted of 21 patients after kidney transplantation whose diet complied with the MD; the control group included 16 patients (also after transplantation) on a low-fat diet, isocaloric with the study diet.

MAIN OUTCOME MEASURES

Anthropometry, plasma lipids, chromatography of triacylglycerols and fatty acids, and activity of superoxide dismutase and catalase were measured in both groups. Statistical analysis was done with the SNN (Statistica Neural Networks) StatSoft software package.

RESULTS

The advantage of neural networks is the possibility of the dynamic presentation of a process taking place in a biological system. In the MD group in the first months of use of the diet, the cholesterol level was reduced only in the group of young and middle-aged patients. This tendency was not observed among elderly patients, among whom a small reduction of the total cholesterol level was noted only at the end of the observation period. In control group at the beginning of the observation, the plasma total cholesterol level was proportional to the patient's age. After 6 months, the total cholesterol increased in young patients and redacted in the group of elderly patients.

CONCLUSIONS

We concluded that the MD diet would be ideal for posttransplantation patients without serious pathologic dyslipidemia. In the case of patients with substantial dyslipidemia, appropriate pharmacologic treatment lowering proatherosclerotic lipid levels should be used in combination with the MD. Artificial neural networks (ANNs) were a useful tool in modeling biological parameters, showing dynamics of the studied interactions in a very detailed way. ANN is the most suitable method for investigations with many variables, interconnected nonlinearly; therefore, this method allows for a more general approach to biological problems. However, it should be noted that considerable data sets are required to obtain a satisfactory fit to the data. Moreover, to ensure the predictive power of this method for new cases, the representative database is indispensable. In spite of these demands, ANN is a prospective tool for reliable, quick assessments and predictions.

Application of artificial neural network modelling to identify severely ill patients whose aminoglycoside concentrations are likely to fall below therapeutic concentrations

OBJECTIVE

Identification of ICU patients whose concentrations are likely to fall below therapeutic concentrations using artificial neural network (ANN) modelling and individual patient physiologic data.

METHOD

Data on indicators of disease severity and some physiologic data were collected from 89 ICU patients who received arbekacin (ABK) and 61 who received amikacin (AMK). Three-layer ANN modelling and multivariate logistic regression analysis were used to predict the plasma concentrations of the aminoglycosides (ABK and AMK) in the severely ill patients.

RESULTS

Predictive performance analysis showed that the sensitivity and specificity of ANN modelling was superior to multivariate logistic regression analysis. For accurate modelling, a predictable range should be inferred from the data structure before the analysis. Restriction of the predictable region, based on the data structure, increased predictive performance.

CONCLUSION

ANN analysis was superior to multivariate logistic regression analysis in predicting which patients would have plasma concentrations lower than the minimum therapeutic concentration. To improve predictive performance, the predictable range should be inferred from the data structure before prediction. When applying ANN modelling in clinical settings, the predictive performance and predictable region should be investigated in detail to avoid the risk of harm to severely ill patients.

Exchange of unsaturated fatty acids between adipose tissue and atherosclerotic plaque studied with artificial neural networks

The linoleic C18:2 (n-6) and linolenic C18:3 (n-3) are recognized as essential components of the diet. Free radical peroxidation of essential fatty acids (EFAs) present in lipoproteins produces oxidized low-density lipoproteins which play a critical role in the development of atherosclerosis. The accumulation of EFAs in the vascular wall and correlations between their content in the adipose tissue and atherosclerotic plaque have been confirmed. The present study was undertaken to determine the usefulness of a neural network for studying the exchange between tissues of linoleic, alpha-linolenic, and arachidonic acids-three fatty acids with a well-understood metabolism. Atheromatous plaques, adipose tissue, and serum were obtained from 31 patients who underwent surgery due to atherosclerotic stenosis of the abdominal aorta, iliac or femoral arteries. Fatty acids were extracted and separated as methyl esters using gas chromatography. Statistical analysis was done with STATISTICA neural networks package. Several correlations reported previously were corroborated and factors modifying the content of individual EFAs in adipose tissue and atherosclerotic plaque were revealed. Artificial neural networks (ANNs) were used to determine factors modifying the content of linoleic, alpha-linolenic, and arachidonic acids in human atheromatous plaques. The mechanism of exchange of some fatty acids between the adipose tissue, atheromatous plaque, and plasma is discussed. The results provide evidence for an effective mechanism of tissue uptake and turnover of linoleic acid. Reduced plasma levels of this acid are compensated by release from adipose tissue and atheromatous plaque. While alpha-linolenic acid is continuously taken up by the plaque, adipose tissue absorbs this acid to a certain level only. The dynamics of exchange of arachidonic acid between adipose tissue and atheromatous plaque reflects a minor role for adipose tissue in determining plaque content of this acid, suggesting that "de novo" synthesis is the chief source of arachidonic acid in plaques.