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PubMed1993

Application of multivariate, fuzzy set and neural network analysis in quantitative cytological examinations.

Multivariate statistical methods have been used in several studies to increase the diagnostic reliability of TV image analyser systems. In recent years some algorithms for decision support (fuzzy logic) and for pattern recognition (neural nets), both non-linear, were developed. This paper reports on preliminary results obtained with these methods in quantitative cytology and compares them to the traditional classifiers. A total of 21 normal, 15 dysplastic and 23 malignant, gastric imprint smears were Feulgen stained and analysed on a Leitz Miamed DNA cytophotometer system. Mean DNA content, the 2c deviation index (2cDI), 5c exceeding rate (5cER), G1, S, G2 phase fraction ratios, cell nucleus area and form factor were determined. Diagnostic accuracy of the discriminant analysis was 96% for the malignant cases, 87% for dysplasias and 81% for normal cases. Cluster analysis gave no significant result. Our diagnostic system utilizing fuzzy logic has made the diagnostic borders adjustable and reliable. The back-propagation neural net correctly classified the normal and malignant cases (100%) and all but one of the dysplasias (98%). The non-linear mathematical methods improved the reliability of the diagnostic system. These new algorithms gave results comparable to traditional classifiers. The application of these methods to clinical samples is encouraging.

Algorithms↗

PubMed1993

Advanced ovarian cancer. Neural network analysis to predict treatment outcome.

BACKGROUND: Quantitative methods for the analysis of prognostic information are important in order to use this knowledge optimally. The neural network is a new quantitative method where the fundamental building blocks are units which can be likened to neurons, and weighted connections which can be likened to synapses. The more the hidden units, the more complex the patterns that can be learnt. MATERIALS AND METHODS: Data from two Dutch studies in ovarian cancer were used to compare the previously reported survival rates predicted by the Cox's prognostic index with the prediction obtained by a neural network. RESULTS: Both the Cox's analysis and the neural network agreed on residual tumour size, stage, and performance status as being important for survival. The neural network identified additional predictive factors such as place of diagnosis and age. As the Cox's prognostic index has not been tested to predict survival on an independent data set a comparison with the results obtained in the neural network test set could not be performed. CONCLUSIONS: Neural networks perform at least as well as Cox's method for the prediction of survival, and prognostic factors can easily be identified. The analysis not only revealed the predictive power of some characteristics, but also the non-predictive power of the others.

Antineoplastic Combined Chemotherapy Protocols↗

PubMed1995

Resolution of batch variations in pyrolysis mass spectrometry of bacteria by the use of artificial neural network analysis.

A simple, but stringent, three group model of bacterial interstrain identity (two cultures of the same strain of Escherichia coli) and difference (a culture of a serologically distinct strain) was used in multiple serial weekly subcultures for five weeks to demonstrate the effect of both growth-related (phenotypic) and machine-related variation on pyrolysis mass spectra. An aliquot of serum from a single sample was included in each pyrolysis batch to distinguish machine drift from culture drift. Conventional principal component (PC) canonical variate (CV) analysis was successful within each pyrolysis batch but the variations between batches precluded the use of data from more than one batch in successful PCCV analysis. In contrast, artificial neural networks (ANNs) trained with data from one batch could be successfully used to identify groups in data from non-contemporaneous pyrolysis batches. Although the ANN method will require validation in more complex settings than this simple model, it is a promising approach to the problem of batch constraint in pyrolysis mass spectrometry.

Bacterial Typing Techniques↗

PubMed1998

Automated quantification of human brain metabolites by artificial neural network analysis from in vivo single-voxel 1H NMR spectra.

A real-time automated way of quantifying metabolites from in vivo NMR spectra using an artificial neural network (ANN) analysis is presented. The spectral training and test sets for ANN containing peaks at the chemical shift ranges resembling long echo time proton NMR spectra from human brain were simulated. The performance of the ANN constructed was compared with an established lineshape fitting (LF) analysis using both simulated and experimental spectral data as inputs. The correspondence between the ANN and LF analyses showed correlation coefficients of order of 0.915-0.997 for spectra with large variations in both signal-to-noise and peak areas. Water suppressed 1H NMR spectra from 24 healthy subjects were collected and choline-containing compounds (Cho), total creatine (Cr), and N-acetyl aspartate (NAA) were quantified with both methods. The ANN quantified these spectra with an accuracy similar to LF analysis (correlation coefficients of 0.915-0.951). These results show that LF and ANN are equally good quantifiers; however, the ANN analyses are more easily automated than LF analyses.

Aspartic Acid↗

PubMed1994

Rapid identification of species within the Mycobacterium tuberculosis complex by artificial neural network analysis of pyrolysis mass spectra.

An artificial neural network (ANN) was trained to distinguish between Mycobacterium tuberculosis and M. bovis with averaged pyrolysis mass spectra from duplicate subcultures of four strains of each of these species, each pyrolysed in triplicate. Once trained, the ANN was interrogated with spectrum data from the original organisms (the "training set") and from 26 other mycobacterial isolates (the "challenge set") of the M. tuberculosis complex (MTBC). Eight strains of M. bovis and 13 of M. tuberculosis, whether sensitive or variously resistant to antituberculosis drugs, were identified in agreement with conventional identification. Four strains of "M. africanum" were identified as M. bovis. Of two atypical M. tuberculosis strains from South India, one was identified as M. tuberculosis and the other as M. bovis. Six strains of BCG proved heterogeneous; two gave equivocal identifications, three were identified as M. bovis and one was identified as M. tuberculosis.

Humans↗

PubMed1993

Rapid identification of streptomycetes by artificial neural network analysis of pyrolysis mass spectra.

An artificial neural network was trained to distinguish between three putatively novel species of Streptomyces using normalised, scaled prolysis mass spectra from three representative strains of each of the taxa, each sampled in triplicate. Once trained, the artificial neural network was challenged with spectral data from the original organisms, the 'training set', from additional members of the putative novel taxa and from over a hundred strains representing six other actinomycete genera. All of the streptomycetes were correctly identified but many of the other actinomycetes were mis-identified. A modified network topology was developed to recognise the mass spectral patterns of the non-streptomycete strains. The resultant neural network correctly identified the streptomycetes, whereas all of the remaining actinomycetes were recognised as unknown organisms. The improved artificial neural network provides a rapid, reliable and cost-effective method of identifying members of the three target streptomycete taxa.

Actinomycetales↗

PubMed1992

Neuronal network analysis of serum electrophoresis.

AIMS: To advise a system of neuronal networks which can classify the densitometric patterns of serum electrophoresis. METHODS: Digitised data containing 83 normal and 132 pathological serum protein electrophoresis patterns were presented to four neuronal networks containing 1900 neurons. Network 1 evaluates the integrated values of the albumin, alpha 1, alpha 2, beta and gamma fractions together with total protein (Biuret method). Networks 2, 3, and 4 analyse the shape of the albumin, beta and gamma fractions. To increase the sensitivity for the detection of monoclonal gammopathies a Fourier transformation was applied to the beta and gamma fractions. RESULTS: After a learning period of 20 minutes (back-propagation learning algorithm) the system was tested with a set of electrophoresis patterns comprising 446 routinely collected samples. It differentiated between physiological and pathological curves with a sensitivity of 97.5% and a specificity of 98.8%, with 86% correct diagnoses. All monoclonal gammopathies were recognised by the Fourier detector. CONCLUSIONS: Neuronal networks could be useful for certain medical uses. Unlike rule based systems, neuronal networks do not have to be programmed but have the capacity to "learn" quickly.

Blood Protein Electrophoresis↗

PubMedDate not supplied

Neural network analysis of follow-up data in primary breast cancer.

This paper reports on the performance of a recently developed neural network environment incorporating likelihood-based optimization and complexity reduction techniques in the analysis of breast cancer follow-up data with the goal of building up a clinical decision support system. The inputs to the neural network include classical factors such as grading, age, tumor size, estrogen and progesterone receptor measurements, as well as tumor biological markers such as PAI-1 and uPA. The network learns the structural relationship between these factors and the follow-up data. Examples of neural models for relapse-free survival are presented, which are based on data from 784 breast cancer patients who received their primary therapy at the Department of Obstetrics and Gynecology, Technische Universität München, Germany. The performance of the neural analysis as quantified by various indicators (likelihood, Kaplan-Meier curves, log-rank tests) was very high. For example, dividing the patients into two equally sized groups based on the neural score (i.e., cutoff = median score) leads to an estimated difference in relapse-free survival of 40% or better (80% vs. 40%) after 10 years in Kaplan-Meier analysis. Evidence for factor interactions as well as for time-varying impacts is presented. The neural network weights included in the models are significant at the 5% level. The use of neural network analysis and scoring in combination with strong tumor biological factors such as uPA and PAI-1 appears to result in a very effective risk group discrimination. Considerable additional comparison of data from different patient series will be required to establish the generalization capability more firmly. Nonetheless, the improvement of risk group discrimination represents an important step toward the use of neural networks for decision support in a clinical framework and in making the most of biological markers.

Adult↗

PubMed1997

Neural networks analysis of astrocytic gliomas from MRI appearances.

A three-layered backpropagation neural network was developed to differentiate malignant from benign brain tumors in a group of patients with astrocytic gliomas. The MRI findings of 43 patients were reviewed before biopsy by three neuroradiologists independently. This provided a database made up of 129 patients' records each of which comprised 13 parameters derived from pre- and post-contrast MR images. The network's generalizing ability was then tested to predict the outcome of biopsy in 36 new cases and its performance compared to that of radiologist using ROC analysis. The output of the network with and without radiologists' impression yielded a better diagnostic performance with relative ROC areas of 0.94 and 0.91, respectively; compared to 0.84 obtained by radiologist. These results demonstrate that the neural network can effectively differentiate malignant from benign brain tumors.

Algorithms↗

PubMed1993

Neural network analysis of ventilation-perfusion lung scans.

A neural network model was constructed to interpret ventilation-perfusion (V/Q) lung scans. This model was trained with data from 100 consecutive V/Q scans with pulmonary angiographic correlation. The network was constructed from 28 input parameters that described various standard V/Q findings, which were fed into a single hidden layer that contained 10-20 nodes. The network output indicated the percentage probability of pulmonary embolism for each set of findings on V/Q scans. This network was then used to classify 28 new scans; the resultant classifications were compared with the rankings of an experienced observer who read the scans without knowledge of the correlative angiographic data. The network with 15 hidden nodes outperformed the experienced observer in prediction of the likelihood of pulmonary embolism in the 28-case test set (P = .039). The neural network has several advantages over current algorithms for interpretation of V/Q scans, including the ability to synthesize many variables into a single conclusion and to learn, or modify itself, at exposure to additional data.

Adult↗

PubMed1997

Classification of mild Alzheimer's disease by artificial neural network analysis of SPET data.

An evaluation of the performance of artificial neural networks (ANNs) for the classification of probable Alzheimer's disease (pAD) patients was undertaken using data extracted from four regions of interest constructed on single photon emission tomographic (SPET) cerebral perfusion images. Two studies using feed-forward neural networks (FFNNs) were undertaken. The first was to determine if it would be possible to classify pAD patients and normal subjects in a mixed group, comprising 29 patients diagnosed as having pAD varying in severity from mild, established dementia to moderate dementia and 10 healthy control subjects. The second was to determine if the networks generated in the first study could prospectively classify 15 additional patients with very mild or mild cognitive impairment. The results were compared to those obtained using the same data and discriminant analysis. The relative performances of the two analysis techniques were assessed on the basis of the area under receiver operating characteristics (ROC) curves. The FFNN successfully classified all datasets in the first study, achieving an area under the ROC curve of 1.00, whereas discriminant analysis achieved 0.94. When tested on data from the second group, the areas under the ROC curves varied between 0.86 and 1.00 for the FFNN, whereas that for discriminant analysis was 0.99. We conclude that FFNNs can accurately classify pAD patients with mild to moderate dementia using data obtained from SPET cerebral perfusion images.

Aged↗

PubMed2002

Classification and molecular organization of satellites elucidated by phylogenetic network analysis - examples from Triturus salamanders and Palorus beetles.

A phylogenetic network of 244 satellite DNA sequences across five species of aquatic salamanders (genus Triturus) revealed four types of satellite DNAs in a 'p'-shaped 1-2*-3-4-2* arrangement. Analysis of dimer and trimer DNA sequences revealed a prevalence of homosequential (e.g. 1-1, 2-2) and particular (1-4 and 2-3) heterosequential repeat motifs. Genetic diversity across types and species phylogeny indicated that type 1 and type 4 are derived from types 2 and 3. Support was also found for alternating motifs in Palorus flour beetle tandem repeats. The results were statistically significant, whether or not the underlying satellite DNA phylogenies were robust under bootstrap analysis.

Animals↗

PubMed2026

Protamine gene expression is associated with sperm motility in rams: An integrative experimental and gene network analysis.

Protamine 1 (PRM1) and protamine 2 (PRM2) are essential regulators of sperm chromatin condensation and genome integrity, and their dysregulation has been associated with impaired male fertility. However, their role in rams remains insufficiently characterized. This study investigated the relationship between protamine gene expression and semen quality in rams and explored their potential upstream regulatory mechanisms using gene regulatory network (GRN) analysis. Fifteen ejaculates from five rams were analyzed. Based on total sperm motility using computer-assisted sperm analysis (CASA), ejaculates were classified into a high-motility group (n = 8) and a low-motility group (n = 7). PRM1 and PRM2 expression levels were quantified by RT-qPCR. Following normality confirmation (p > 0.05), parametric tests were applied using the ejaculate as the biological experimental unit. Samples with reduced motility showed significantly lower expression of both protamines (p < 0.01). Moreover, progressive sperm motility was strongly correlated with both PRM1 (r = 0.71, p = 0.019) and PRM2 (r = 0.69, p = 0.03) transcript levels. Cross-species GRN inference using scGeneRAI and a reference human spermatogenesis dataset identified several hypothesis-generating candidate transcription factors, including HMGB4, HMGB1, H2AFZ, NKX6-1, and SMC3, consistently supported across multiple bootstrap resampling runs. These findings demonstrate a strong association between protamine expression and sperm motility in rams. While the identified candidate regulators provide a valuable framework for future species-specific validation, they also highlight promising candidate molecular biomarkers of male fertility in livestock.

Gene regulatory networks↗

PubMed1997

Artificial neural network analysis of noisy visual field data in glaucoma.

This paper reports on the application of an artificial neural network to the clinical analysis of ophthalmological data. In particular a 2-dimensional Kohonen self-organising feature map (SOM) is used to analyse visual field data from glaucoma patients. Importantly, the paper addresses the problem of how the SOM can be utilised to accommodate the noise within the data. This is a particularly important problem within longitudinal assessment, where detecting significant change is the crux of the problem in clinical diagnosis. Data from 737 glaucomatous visual field records (Humphrey Visual Field Analyzer, program 24-2) are used to train a SOM with 25 nodes organised on a square grid. The SOM clusters the data organising the output map such that fields with early and advanced loss are at extreme positions, with a continuum of change in place and extent of loss represented by the intervening nodes. For each SOM node 100 variants, generated by a computer simulation modelling the variability that might be expected in a glaucomatous eye, are also classified by the network to establish the extent of noise upon classification. Field change is then measured with respect to classification of a subsequent field, outside the area defined by the original field and its variants. The significant contribution of this paper is that the spatial analysis of the field data, which is provided by the SOM, has been augmented with noise analysis enhancing the visual representation of longitudinal data and enabling quantification of significant class change.

Diagnosis, Computer-Assisted↗

PubMed1984

Information trains. The technique and its uses in spike train and network analysis, with examples taken from the nucleus parabrachialis medialis during sleep-waking states.

We describe an analytical procedure for assessing functional interactions between neuronal spike trains based on the outcome of cross-correlation procedures. Subsets of a reference cell spike train in a two-train recording are extracted, based on their time-locked relationship to spikes in the dependent train. Such timing relationships comprise the significant primary structures in the cross-correlogram. Different subsets can be extracted for different primary structures in the same correlogram (i.e. a subset responsible for an interaction effect, a subset responsible for a shared input effect, etc.). These new spike trains represent an information transfer process across synapses. These 'information trains' may be compared and correlated to different cells of the network across different functional conditions such as sleep-waking states, and may also be subjected to conventional spike train analysis techniques such as rate histogram, auto-correlation and cross-correlation procedures. We illustrate the information train procedures with a network analysis of a set of cells recorded in the nucleus parabrachialis medialis during different sleep-waking states.

Action Potentials↗

PubMed1996

Automated classification of human brain tumours by neural network analysis using in vivo 1H magnetic resonance spectroscopic metabolite phenotypes.

We present a novel method to integrate in vivo nuclear magnetic resonance spectroscopy (MRS) information into the clinical diagnosis of brain tumours. Water-suppressed 1H MRS data were collected from 33 patients with brain tumours and 28 healthy controls in vivo. The data were treated in the time domain for removal of residual water and a region from the frequency domain (from 3.4 to 0.3 p.p.m.) together with the unsuppressed water signal were used as inputs for artificial neural network (ANN) analysis. The ANN distinguished tumour and normal tissue in each case and was able to classify benign and malignant gliomas as well as other brain tumours to match histology in a clinically useful manner with an accuracy of 82%. Thus the present data indicate existence of tumour tissue-specific metabolite phenotypes that can be detected by in vivo 1H MRS. We believe that a user-independent ANN analysis may provide an alternative method for tumour classification in clinical practice.

Astrocytoma↗

PubMedDate not supplied

Application of quantitative artificial neural network analysis to 2D NMR spectra of hydrocarbon mixtures.

Understanding relationships between the structure and composition of molecular mixtures and their chemical properties is a main industrial aim. One central field of research is oil chemistry where the key question is how the molecular characteristics of composite hydrocarbon mixtures can be associated with the macroscopic properties of the oil products. Apparently these relationships are complex and often nonlinear and therefore call for advanced spectroscopic techniques. An informative and an increasingly used approach is two-dimensional nuclear magnetic resonance (2D NMR) spectroscopy. In the case of composite hydrocarbons the application of 2D NMR methodologies in a quantitative manner pose many technical difficulties, and, in any case, the resulting spectra contain many overlapping resonances that challenge the analytical work. Here, we present a general methodology, based on quantitative artificial neural network (ANN) analysis, to resolve overlapping information in 2D NMR spectra and to simultaneously assess the relative importance of multiple spectral variables on the sample properties. The results in a set of 2D NMR spectra of oil samples illustrate, first, that use of ANN analysis for quantitative purposes is feasible also in 2D and, second, that this methodology offers an intrinsic opportunity to assess the complex and nonlinear relationships between the molecular composition and sample properties. The presented ANN methodology is not limited to the analysis of NMR spectra but can also be applied in a manner similar to other (multidimensional) spectroscopic data.

Journal Article↗

PubMed2026

Decoding protein signatures and protein interactions in oral potentially malignant disorders: a systematic review and network analysis.

BACKGROUND: Proteomic profiling offers thorough insights into protein structure and function, as well as it acts as an essential approach for analyzing molecular changes at the tissue level. However, because of the proteome's diversity and dynamic nature, biomarker discovery remains challenging. By combining proteomics with bioinformatics, the level of understanding in relation to molecular interactions and disease processes can be improved. Through an integrative approach, few limitations can be addressed, thereby promoting proteomic profiling for the discovery of new therapeutic targets and novel biomarkers for a variety of disorders. AIM: To identify differentially expressed protein markers and their key molecular pathways associated with Oral Potentially Malignant Disorders. METHODS: Systematic Review was conducted following the PRISMA guidelines and the protocol registered in the International Prospective Register of Systematic Reviews (PROSPERO) with the registration ID number CRD42024557545. A comprehensive literature review was performed using electronic databases, yielding 12,797, studies from which 15 eligible articles were selected. The Newcastle-Ottawa Scale was used to assess the risk of bias. Vote counting was performed to identify proteins reported in more than one study. A bipartite network was constructed using Cytoscape to identify shared and disease-specific protein markers. Lesion-wise protein-protein interaction networks were generated using STRING and analysed in Cytoscape to identify highly interconnected hub proteins, and pathway enrichment analysis for these hubs was performed using Reactome. RESULTS: A total of fifteen studies (Leukoplakia (LK) - n = 1, Proliferative Verrucous Leukoplakia (PVL) - n = 2, Oral Submucous Fibrosis (OSMF) - n = 7, and Oral Lichen Planus (OLP) - n = 5) were included. The Newcastle-Ottawa Scale was used to evaluate methodological quality and the quality of studies included in this systematic review was high for 4 articles and moderate in the remaining 11. The most commonly employed technique was mass spectrometry. A total of 318 candidate proteins (LK - 14, PVL - 82, OSMF - 172, and OLP - 50) were identified across the oral potentially malignant disorders. Key markers identified through vote counting included ERO1A, NUCB1, RHOA, and IL36A for PVL; LUM, KRT1, KRT9, ALB, and VIM for OSMF; and ALB, LYZ, HP, HBB, and AMY1A for OLP. The bipartite network showed that OSMF and OLP shared the highest number of proteins, indicating the strongest overlap among lesions. Network analysis further highlighted distinct hub proteins for each lesion: for LK- AMY1A, AMY1B and APOA1; for PVL- CFL1, RHOA and CDC42; for OSMF- HSP90AA1, ENO1 and SERPINA1; and for OLP- HP, B2M, and ORM1. Lesion-specific pathway enrichment revealed that LK was associated with epithelial differentiation, PVL with oncogenic signaling, OSMF with stress-driven fibrosis, and OLP with immune-mediated inflammation. CONCLUSIONS: Proteomic expression offers insights into disease pathogenesis by identifying important molecular changes across OPMDs. However, the majority of biomarkers are still in the exploratory stage due to the considerable variation in lesion types, sample sources, proteomic techniques, and reporting systems. In order to create reliable and clinically applicable biomarkers, future studies should concentrate on combining multi-omics techniques with large-scale, standardized cohorts.

Humans↗