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At least 55 records · Page 3Linked to original sources
PubMed1993

Artificial neural network analysis of pyrolysis mass spectrometric data in the identification of Streptomyces strains.

Sixteen representatives of three morphologically distinct groups of streptomycetes were recovered from soil using selective isolation procedures. Duplicated batches of the test strains were examined by Curie-point pyrolysis mass spectrometry and the first data set used for conventional multivariate statistical analyses and as a training set for an artificial neural network. The second set of data was used for 'operational fingerprinting' and for testing the artificial neural network. All of the test strains were correctly identified using the artificial neural network whereas only fifteen of the sixteen strains were assigned to the correct group using the conventional operational fingerprinting procedure. Artificial neural network analysis of pyrolysis mass spectrometric data provides a rapid, cost-effective and reproducible way of identifying and typing large numbers of microorganisms.

Bacterial Typing Techniques↗
PubMed1991

Use of neural network analysis to classify electroencephalographic patterns against depth of midazolam sedation in intensive care unit patients.

The electroencephalographic (EEG) analog signal is complex and cannot easily be described by univariate variables. Clear visual changes in the EEG power spectrum can be present with little or no change in univariate variable values. A method that could produce a single value based on the total data available in the EEG power spectrum would be very useful in monitoring EEG changes. Neural network analysis is a technique that can take multiple inputs and produce a single output value using complicated processing patterns that require training to establish. We examined the usefulness of a series of neural network models to classify 63 EEG patterns against sedation level in 26 mechanically ventilated patients requiring midazolam for long-term sedation. During a stable period of sedation, a 4- to 60-minute period of EEG data was obtained concurrently with a sedation level from 1 (follows commands) to 7 (no or gag response to suctioning of the endotracheal tube). The EEG power spectrum was divided into equal frequency bands, and the log absolute powers in each of these bands were used as inputs for a series of neural network models. The output target was the sedation level associated with each set of EEG data. Networks were trained on a subset of EEG power/sedation score data pairs, and the ability to classify the remaining data pairs was tested. Using a t-test comparison with a random set of sedation levels, we found that trained neural network models classified EEG patterns against sedation level successfully (p less than 0.001).(ABSTRACT TRUNCATED AT 250 WORDS)

Adolescent↗
PubMed2026

Crosstalk mediators implicated in the Stevens-Johnson Syndrome through gene regulatory network analysis.

Stevens-Johnson syndrome (SJS) is a rare and severe mucocutaneous disorder often triggered by medications or infections. Our previous research identified that four key genes, Ikzf1, Ptger3, Mavs, and Tlr3 are involved in SJS susceptibility and the conjunctival epithelial innate immune response, demonstrating their role in regulating interferon-stimulated genes. However, the interplay among these regulatory factors remains unclear. This study aimed to elucidate the crosstalk mechanisms between the pathways regulated by these four genes in conjunctival epithelial cells. We constructed a comprehensive gene regulatory network using transcriptomic data from murine conjunctival epithelial cells under 16 distinct conditions, including polyI:C stimulation across wild-type, knockout, and transgenic backgrounds for the key genes. A targeted network analysis systematically identified numerous candidate genes mediating the crosstalk between the regulatory pathways initiated by Ikzf1, Ptger3, Mavs, and Tlr3. The identified candidates suggest the involvement of diverse signaling pathways previously unlinked to SJS pathology. Our findings suggest that the pathogenesis of SJS may arise not from the dysfunction of isolated genes but from the disruption of a balance maintained by intricate pathway crosstalk.

Animals↗
PubMed2026

Functional and Nutritional Potential of Chickpea Protein Hydrolysates: A Systematic Review and Plant-protein Network Analysis.

Chickpea is a protein-rich legume increasingly explored as a substrate for functional plant-based ingredients. Chickpea protein hydrolysates (CPHs) and chickpea-derived peptides (CPs), obtained through enzymatic hydrolysis or simulated gastrointestinal digestion, may provide technological and biological properties while supporting the valorization of chickpea fractions and by-products. This review integrates a network analysis of title-abstract terms from 5,728 unique Scopus and PubMed records on plant protein hydrolysates with a systematic review of 72 studies focused on CPH production, peptide characterization, bioactivity, and translational gaps. The evidence indicates that CPHs and CPs show promising antioxidant, antihypertensive, antidiabetic, anti-inflammatory, lipid-lowering, immunomodulatory, antimicrobial, and anticancer-related activities, mainly supported by biochemical assays, cell models, and animal studies. However, heterogeneous hydrolysis protocols, incomplete peptide characterization, inconsistent bioactivity methods, limited scale-up evidence, and the absence of human intervention trials restrict translation. Future studies should prioritize standardized protocols, mechanistic validation, bioavailability, sensory and regulatory assessment, food-matrix validation, and clinical trials.

Cicer↗
PubMed1995

Neural network analysis of quantitative histological factors to predict pathological stage in clinical stage I nonseminomatous testicular cancer.

A great deal of controversy exists in staging clinical stage I (CSI) nonseminomatous testicular germ cell tumors (NSGCT) because of the difficulty of distinguishing true stage I patients from those with occult retroperitoneal or distant metastases. The goal of this study was to quantitate primary tumor histologic factors and to apply these in a neural network computer analysis to determine if more accurate staging could be achieved. All available primary tumor histological slides from 93 CSI NSGCT patients were analyzed for vascular invasion (VI), lymphatic invasion (LI), tunical invasion (TI) and quantitative determination of percentage of the primary tumor composed of embryonal carcinoma (%EMB), yolk sac carcinoma (%YS), teratoma (%TER) and seminoma (%SEM). These patients had undergone retroperitoneal lymphadenectomy or follow-up such that final stage included 55 pathologic stage I and 38 stage II or higher lesions. Two investigators were provided identical datasets for neural network analysis; one experienced researcher used custom Kohonen and back propagation programs and one less experienced researcher used a commercially available program. For each experiment, a subset of data was used for training, and subsets were blindly used to test the accuracy of the networks. In the custom back propagation network, 86 of 93 patients were correctly staged for an overall accuracy of 92% (sensitivity 88%, specificity 96%). Using Neural Ware commercial software 74 of 93 (79.6%) were accurately staged when all 7 input variables were used; however, accuracy improved from 84.9 to 87.1% when 2, 4 and 5 of the variables were used. Quantitative histologic assessment of the primary tumor and neural network processing of data may provide clinically useful information in the CSI NSGCT population; however, the expertise of the network researcher appears to be important, and commercial software in general use may not be superior to standard regression analysis. Prospective testing of expert methodology should be instituted to confirm its utility.

Germinoma↗
PubMed1998

A hybrid neural network analysis of subtle brain volume differences in children surviving brain tumors.

In the treatment of children with brain tumors, balancing the efficacy of treatment against commonly observed side effects is difficult because of a lack of quantitative measures of brain damage that can be correlated with the intensity of treatment. We quantitatively assessed volumes of brain parenchyma on magnetic resonance (MR) images using a hybrid combination of the Kohonen self-organizing map for segmentation and a multilayer backpropagation neural network for tissue classification. Initially, we analyzed the relationship between volumetric differences and radiologists' grading of atrophy in 80 subjects. This investigation revealed that brain parenchyma and white matter volumes significantly decreased as atrophy increased, whereas gray matter volumes had no relationship with atrophy. Next, we compared 37 medulloblastoma patients treated with surgery, irradiation, and chemotherapy to 19 patients treated with surgery and irradiation alone. This study demonstrated that, in these patients, chemotherapy had no significant effect on brain parenchyma, white matter, or gray matter volumes. We then investigated volumetric differences due to cranial irradiation in 15 medulloblastoma patients treated with surgery and radiation therapy, and compared these with a group of 15 age-matched patients with low-grade astrocytoma treated with surgery alone. With a minimum follow-up of one year after irradiation, all radiation-treated patients demonstrated significantly reduced white matter volumes, whereas gray matter volumes were relatively unchanged compared with those of age-matched patients treated with surgery alone. These results indicate that reductions in cerebral white matter: 1) are correlated significantly with atrophy; 2) are not related to chemotherapy; and 3) are correlated significantly with irradiation. This hybrid neural network analysis of subtle brain volume differences with magnetic resonance may constitute a direct measure of treatment-induced brain damage.

Adolescent↗
PubMed2026

Genetic dissection of cardiac iron regulation using transcriptome network analysis and systems genetics in BXD mice.

Cardiac iron homeostasis is essential for myocardial energy metabolism and contractile function, yet the genetic and molecular mechanisms governing iron levels within the heart remain poorly understood. We used a systems genetics approach to dissect the transcriptional regulation of cardiac iron homeostasis. Myocardial iron level varies substantially across BXD strains (40-112 μg/g) and is under heritable genetic control (H2 = 0.38). Elevated cardiac iron is associated with reduced ventricular mass, increased ventricular ectopy, and prolonged atrioventricular conduction in the BXD population. Weighted gene co-expression network analysis of the BXD heart transcriptome identified a co-expression module that was significantly and negatively correlated with cardiac iron levels in both young and old BXD mice and enriched for pathways related to metabolic regulation, cyclic AMP (cAMP) signaling, circadian entrainment, and cardiovascular physiology. The module showed substantial overlap with a curated cardiac iron gene set, and cross-species enrichment analysis confirmed its conservation in human cardiomyopathy differentially expressed genes (enrichment ratio = 1.49; false discovery rate [FDR] = 0.0342). Quantitative trait locus (QTL) mapping of the first principal component of the overlapping module iron genes (n = 38), corroborated by individual gene mapping, identified trans-eQTL hotspots on multiple chromosomes, implicating Fcho2, Gcc2, and Rmdn1 as candidate upstream regulators operating through sequential steps of intracellular iron trafficking. Together, these findings establish a systems-level map of cardiac iron gene regulation, identify candidate genetic regulators, and provide a molecular framework linking disruption of iron-related transcriptional networks to structural and electrical cardiac dysfunction with implications for iron-related heart diseases.

BXD mouse population↗
PubMed1996

Network analysis of human Y microsatellite haplotypes.

To investigate the utility of Y chromosome microsatellites for studying human male-lineage evolution, we typed samples from three populations for five tetranucleotide repeats and an Alu insertion polymorphism. We found very high levels of haplotype diversity and evidence that most mutations involve the gain or loss of only one repeat unit, implying that any given microsatellite haplotype may have arisen independently on two or more Y-chromosome lineages. Together, these factors suggest that interpretation of small sample sizes (< 30) will be problematic. By typing a large sample of individuals (n = 174) from one population, East Anglia, we were able to construct a haplotype network. The network exhibits a well-connected core structure of commoner haplotypes. Computer simulations based on this network estimate the convergence time for African and Caucasian groups may be between 1.4 and 1.8 times as long as the convergence of the East Anglian population. Based on our comparison between large and small sample sizes, we suggest that large sample sizes are necessary in order to interpret Y-microsatellite haplotypes, and that a network analysis of the type we describe may prove informative in future studies.

Base Sequence↗
PubMed1995

Quantification of biomedical NMR data using artificial neural network analysis: lipoprotein lipid profiles from 1H NMR data of human plasma.

Artificial neural network (ANN) analysis is a new technique in NMR spectroscopy. It is very often considered only as an efficient "black-box' tool for data classification, but we emphasize here that ANN analysis is also powerful for data quantification. The possibility of finding out the biochemical rationale controlling the ANN outputs is presented and discussed. Furthermore, the characteristics of ANN analysis, as applied to plasma lipoprotein lipid quantification, are compared to those of sophisticated lineshape fitting (LF) analysis. The performance of LF in this particular application is shown to be less satisfactory when compared to neural networks. The lipoprotein lipid quantification represents a regular clinical need and serves as a good example of an NMR spectroscopic case of extreme signal overlap. The ANN analysis enables quantification of lipids in very low, intermediate, low and high density lipoprotein (VLDL, IDL, LDL and HDL, respectively) fractions directly from a 1H NMR spectrum of a plasma sample in < 1 h. The ANN extension presented is believed to increase the value of the 1H NMR based lipoprotein quantification to the point that it could be the method of choice in some advanced research settings. Furthermore, the excellent quantification performance of the ANN analysis, demonstrated in this study, serves as an indication of the broad potential of neural networks in biomedical NMR.

Humans↗
PubMed1996

Neural network analysis of the EMG interference pattern.

This paper investigates the performance of artificial neural networks for analysing and classifying EMG signals from healthy subjects and patients with myopathic and neuropathic disorders. EMG interference patterns (IP) were recorded under maximum voluntary contraction from the right biceps of a total of 50 subjects. Parameters were obtained from the signals using recognized quantification techniques including turns analysis, small segments analysis and frequency analysis. Supervised networks examined were an improved backpropagation network (IBPN), a radial basis network (RBN), and a learning vector quantization network (INQ). Supervised networks using different combinations of parameters from turns analysis and small segments analysis gave diagnostic yields of 60-80%. Combinations using frequency analysis parameters produced similar results. The performance of unsupervised Self-Organising Feature Maps (SOFM) was generally lower than that of the supervised networks. Including personal data (sex and age) did not improve the overall performance.

Biomedical Engineering↗
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↗