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Neural networks in the analysis of episodic growth hormone release.

Pulsatile secretion of growth hormone (GH) has been observed in healthy controls as well as acromegalic patients. In healthy adults, highly regulated secretory pulses of GH occur 4-8 times within 24 h. This episodic pattern of secretion seems to be related to the optimal induction of physiological effects at the peripheral level. In contrast to normal subjects, acromegalic patients demonstrate an irregular pattern of excessive GH release. This pattern of secretion is responsible for many systemic effects, such as the stimulation of connective tissue growth, cardiovascular and cerebrovascular disease, diabetes mellitus and arthritis. Standard methods for the analysis of pulsatile patterns of hormone secretion did not consistently separate the temporal dynamics of GH release in healthy controls and acromegalic patients under various study conditions. Using the cutting edge technology of artificial neural networks for time series prediction, we were able to achieve significant separation of both groups under various conditions by means of the predictability of their GH secretory dynamics. Improving the predictive results by using a more refined system of multiple neural networks acting in parallel (adaptive mixtures of local experts), we found that this system performed a self-organized segmentation of hormone pulsatility. It separated phases of secretory bursts and quiescence without any prior knowledge of the form of a GH pulse or a model of secretion. Comparing the predictive results for the GH dynamics with those for computer-stimulated stochastic processes, we were able to define the irregular pattern of GH secretion in acromegaly as a random autonomous process. The introduction of neural networks to the analysis of dynamic endocrine systems might help to expand the existing analytical approaches beyond counting frequency and amplitude of hormone pulses.

Acromegaly↗
PubMed2004

Deriving sufficient conditions for global asymptotic stability of delayed neural networks via nonsmooth analysis.

In this paper, we obtain new sufficient conditions ensuring existence, uniqueness, and global asymptotic stability (GAS) of the equilibrium point for a general class of delayed neural networks (DNNs) via nonsmooth analysis, which makes full use of the Lipschitz property of functions defining DNNs. Based on this new tool of nonsmooth analysis, we first obtain a couple of general results concerning the existence and uniqueness of the equilibrium point. Then those results are applied to show that existence assumptions on the equilibrium point in some existing sufficient conditions ensuring GAS are actually unnecessary; and some strong assumptions such as the boundedness of activation functions in some other existing sufficient conditions can be actually dropped. Finally, we derive some new sufficient conditions which are easy to check. Comparison with some related existing results is conducted and advantages are illustrated with examples. Throughout our paper, spectral properties of the matrix (A + Atau) play an important role, which is a distinguished feature from previous studies. Here, A and Atau are, respectively, the feedback and the delayed feedback matrix defining the neural network under consideration.

Neural Networks, Computer↗
PubMed1994

Polygenic trait analysis by neural network learning.

AI techniques have been applied to the domain of DNA sequence analysis in predicting or identifying certain specialized regions, in recognizing genes, and in understanding the evolutionary relationships between sequences. This paper focuses on a kind of genetic pattern recognition, namely, the problem of identifying the gene combinations (patterns) causally related to a given trait determined by multiple genes (a so-called polygenic trait). A novel approach is presented which combines neural-network and knowledge-based techniques. The neural network is trained to predict the trait and then the knowledge embedded in the network is decoded into symbolic patterns. This hybrid approach is evaluated in the domain of identifying genes of insulin dependent diabetes mellitus. The consistency between the results with this approach and those reported in genetic literature supports the viability of this approach.

Artificial Intelligence↗
PubMed2026

Comparative Efficacy of Non-opioid Analgesic Drugs for Chronic Cancer Pain: A Bayesian Network Meta-analysis.

PURPOSE: While opioids remain the primary pharmacological intervention for cancer pain management, their clinical utility is frequently compromised by dose-limiting toxicities. This study aimed to determine the comparative efficacy, opioid-sparing potential, and clinical hierarchy of non-opioid adjuvant drug classes. The study was structured around the PICO framework to evaluate the pharmacological strategies currently utilized in multimodal clinical oncology. METHODS: A systematic search of electronic databases (PubMed, Embase, Cochrane) was conducted for randomized controlled trials (RCTs) published between 2000 and 2025. The primary outcome was global analgesic efficacy (standardized mean difference [SMD]), while secondary outcomes included the opioid-sparing effect, defined as the percentage reduction in morphine equivalent daily dose (MEDD) and the incidence of treatment-emergent adverse events (Harms). A Bayesian network meta-analysis (NMA) was performed to rank treatments using SUCRA values. The methodological quality was assessed using the Cochrane Risk of Bias (RoB 2.0) tool. RESULTS: Twenty-three RCTs (n = 1845) met the inclusion criteria. Nonsteroidal anti-inflammatory drugs (NSAIDs) (-1.10) and anticonvulsants (-1.06) demonstrated the most robust analgesic effects. The SUCRA ranking confirmed a clear hierarchy, with the combination of anticonvulsants and antidepressants showing the highest probability of efficacy. A significant opioid-sparing effect was observed for gabapentinoids and ketamine, facilitating MEDD reduction. While serious adverse events were rare, minor harms (somnolence, dizziness) were more frequent in the most effective classes. CONCLUSION: Our NMA provides a robust evidence base for a "Clinical Tier" system, ranking adjuvants by their balance of efficacy and safety. These findings support the early integration of Tier I agents (anticonvulsants and NSAIDs) to optimize pain control and reduce opioid-related toxicities in chronic cancer pain management.

Humans↗
PubMed2004

Structural analysis of expanding metabolic networks.

Methods are developed for structural analysis of metabolic networks expanding in size. Expansion proceeds in consecutive generations in which new reactions are attached to the network produced in the previous stage. Different rules are applied resulting in various modes of expansion. Expansion is performed on the set of glycolytic reactions as well as on a very large set of reactions taken from the KEGG database. It is shown that reactions and compounds strongly differ in the generation in which they are attached to the network allowing conclusions for the temporal order of the acquisition during network evolution. The expansion provides efficient tools for detecting new structural characteristics such as substrate-product relationships over long distances.

Databases, Factual↗
PubMed2004

An extended transcriptional regulatory network of Escherichia coli and analysis of its hierarchical structure and network motifs.

Recent studies of genome-wide transcriptional regulatory network (TRN) revealed several intriguing structural and dynamic features of gene expression at a system level. Unfortunately, the network under study is often far from complete. A critical question is thus how much the network is incomplete and to what extent this would affect the results of analysis. Here we compare the Escherichia coli TRN built by Shen-Orr et al. (Nature Genet., 31, 64-68) with two TRNs reconstructed from RegulonDB and Ecocyc respectively and present an extended E.coli TRN by integrating information from these databases and literature. The scale of the extended TRN is about twice as large as the previous ones. The new network preserves the multi-layer hierarchical structure which we recently reported but has more layers. More global regulators are inferred. While the feed forward loop (FFL) is confirmed to be highly representative in the network, the distribution of the different types of FFLs is different from that based on the incomplete network. In contrast to the notion of motif aggregation and formation of homologous motif clusters, we found that most FFLs interact and form a giant motif cluster. Furthermore, we show that only a small portion of the genes is solely regulated by only one FFL. Many genes are regulated by two or more interacting FFLs or other more complicated network motifs together with transcriptional factors not belonging to any network motifs, thereby forming complex regulatory circuits. Overall, the extended TRN represents a more solid basis for structural and functional analysis of genome-wide gene regulation in E.coli.

Databases, Genetic↗
PubMed2006

High resolution analysis and phylogenetic network construction using complete mtDNA sequences in sardinian genetic isolates.

For mitochondrial phylogenetic analysis, the best result comes from complete sequences. We therefore decided to sequence the entire mitochondrial DNA (mtDNA) (coding and D-loop regions) of 63 individuals selected in 3 small Ogliastra villages, an isolated area of eastern Sardinia: Talana, Urzulei, and Perdasdefogu. We studied at least one individual for each of the most frequent maternal genealogical lineages belonging to haplogroups H, V, J, K, T, U, and X. We found in our 63 samples, 172 and 69 sequence changes in the coding and in the D-loop region, respectively. Thirteen out of 172 sequence changes in the coding region are novel. It is our hypothesis that some of them are characteristic of the Ogliastra region and/or Sardinia. We reconstructed the phylogenetic network of the 63 complete mtDNA sequences for the 3 villages. We also drew a network including a large number of European sequences and calculated various indices of genetic diversity in Ogliastra. It appears that these small populations remained extremely isolated and genetically differentiated compared with other European populations. We also identified in our samples a never previously described subhaplogroup, U5b3, which seems peculiar to the Ogliastra region.

DNA, Mitochondrial↗
PubMed2006

Construction of phylogenetic trees by kernel-based comparative analysis of metabolic networks.

BACKGROUND: To infer the tree of life requires knowledge of the common characteristics of each species descended from a common ancestor as the measuring criteria and a method to calculate the distance between the resulting values of each measure. Conventional phylogenetic analysis based on genomic sequences provides information about the genetic relationships between different organisms. In contrast, comparative analysis of metabolic pathways in different organisms can yield insights into their functional relationships under different physiological conditions. However, evaluating the similarities or differences between metabolic networks is a computationally challenging problem, and systematic methods of doing this are desirable. Here we introduce a graph-kernel method for computing the similarity between metabolic networks in polynomial time, and use it to profile metabolic pathways and to construct phylogenetic trees. RESULTS: To compare the structures of metabolic networks in organisms, we adopted the exponential graph kernel, which is a kernel-based approach with a labeled graph that includes a label matrix and an adjacency matrix. To construct the phylogenetic trees, we used an unweighted pair-group method with arithmetic mean, i.e., a hierarchical clustering algorithm. We applied the kernel-based network profiling method in a comparative analysis of nine carbohydrate metabolic networks from 81 biological species encompassing Archaea, Eukaryota, and Eubacteria. The resulting phylogenetic hierarchies generally support the tripartite scheme of three domains rather than the two domains of prokaryotes and eukaryotes. CONCLUSION: By combining the kernel machines with metabolic information, the method infers the context of biosphere development that covers physiological events required for adaptation by genetic reconstruction. The results show that one may obtain a global view of the tree of life by comparing the metabolic pathway structures using meta-level information rather than sequence information. This method may yield further information about biological evolution, such as the history of horizontal transfer of each gene, by studying the detailed structure of the phylogenetic tree constructed by the kernel-based method.

Archaeal Proteins↗
PubMed2006

Combining wavelet analysis and Bayesian networks for the classification of auditory brainstem response.

The auditory brainstem response (ABR) has become a routine clinical tool for hearing and neurological assessment. In order to pick out the ABR from the background EEG activity that obscures it, stimulus-synchronized averaging of many repeated trials is necessary, typically requiring up to 2000 repetitions. This number of repetitions can be very difficult, time consuming and uncomfortable for some subjects. In this study, a method combining wavelet analysis and Bayesian networks is introduced to reduce the required number of repetitions, which could offer a great advantage in the clinical situation. 314 ABRs with 64 repetitions and 155 ABRs with 128 repetitions recorded from eight subjects are used here. A wavelet transform is applied to each of the ABRs, and the important features of the ABRs are extracted by thresholding and matching the wavelet coefficients. The significant wavelet coefficients that represent the extracted features of the ABRs are then used as the variables to build the Bayesian network for classification of the ABRs. In order to estimate the performance of this approach, stratified ten-fold cross-validation is used.

Algorithms↗
PubMed2006

GenePro: a Cytoscape plug-in for advanced visualization and analysis of interaction networks.

MOTIVATION: Analyzing the networks of interactions between genes and proteins has become a central theme in systems biology. Versatile software tools for interactively displaying and analyzing these networks are therefore very much in demand. The public-domain open software environment Cytoscape has been developed with the goal of facilitating the design and development of such software tools by the scientific community. RESULTS: We present GenePro, a plugin to Cytoscape featuring a set of versatile tools that greatly facilitates the visualization and analysis of protein networks derived from high-throughput interactions data and the validation of various methods for parsing these networks into meaningful functional modules. AVAILABILITY: The GenePro plugin is available at the website http://genepro.ccb.sickkids.ca.

Algorithms↗
PubMed2004

The circular topology of rhythm in asynchronous random Boolean networks.

The analysis of previously evolved rhythmic asynchronous random Boolean networks [Biosystems 59 (2001) 185] reveals common topological characteristics indicating that rhythm originates from a circular functional structure. The rhythm generating core of the network has the form of a closed ring which operates as a synchronisation substrate by supporting a travelling wave of state change; the size of the ring corresponds well with the period of oscillation. The remaining nodes in the network are either stationary or follow the activity of the ring without feeding back into it so as to form a coherent whole. Rings are typically formed early on in the evolutionary search process. Alternatively, long chains of nodes are favoured before they close upon themselves to stabilize. Analysis of asynchronous networks with de-correlated (non-rhythmic, non-stationary) attractors reveals no such common topological characteristics. These results have been obtained using statistical analysis and a specifically developed bottom-up pruning algorithm. This algorithm works from local interactions to global configuration by eliminating redundant links. The suitability of the algorithm has been confirmed by both numerical and single lesion analysis. The ring topology solution for the generation of rhythm implies that it will be harder to evolve rhythmic networks for big sizes and small periods and for bigger number of connections per node. These trends are confirmed empirically. Finally, the identified mechanisms are utilised to handcraft rhythmic networks of different periods showing that a low number of connections suffices for a large variety of rhythms. Random asynchronous update forces the evolved solutions to be highly robust maintaining their performance in the presence of intrinsic noise. The biological implications of such robust designs for molecular clocks are discussed.

Algorithms↗
PubMed2005

An algorithmic framework for genome-wide modeling and analysis of translation networks.

The sequencing of genomes of several organisms and advances in high throughput technologies for transcriptome and proteome analysis has allowed detailed mechanistic studies of transcription and translation using mathematical frameworks that allow integration of both sequence-specific and kinetic properties of these fundamental cellular processes. To understand how perturbations in mRNA levels affect the synthesis of individual proteins within a large protein synthesis network, we consider here a genome-scale codon-wide model of the translation machinery with explicit description of the processes of initiation, elongation, and termination. The mechanistic codon-wide description of the translation process and the large number of mRNAs competing for resources, such as ribosomes, requires the use of novel efficient algorithmic approaches. We have developed such an efficient algorithmic framework for genome-scale models of protein synthesis. The mathematical and computational framework was applied to the analysis of the sensitivity of a translation network to perturbation in the rate constants and in the mRNA levels in the system. Our studies suggest that the highest specific protein synthesis rate (protein synthesis rate per mRNA molecule) is achieved when translation is elongation-limited. We find that the mRNA species with the highest number of actively translating ribosomes exerts maximum control on the synthesis of every protein, and the response of protein synthesis rates to mRNA expression variation is a function of the strength of initiation of translation at different mRNA species. Such quantitative understanding of the sensitivity of protein synthesis to the variation of mRNA expression can provide insights into cellular robustness mechanisms and guide the design of protein production systems.

Algorithms↗
PubMed2004

Generalized neural networks for spectral analysis: dynamics and Liapunov functions.

This paper analyzes local and global behavior of several dynamical systems which generalize some artificial neural network (ANN) semilinear models originally designed for principal component analysis (PCA) in the characterization of random vectors. These systems implicitly performed the spectral analysis of correlation (i.e. symmetric positive definite) matrices. Here, the proposed generalizations cover both nonsymmetric matrices as well as fully nonlinear models. Local stability analysis is performed via linearization and global behavior is analyzed by constructing several Liapunov functions.

Algorithms↗
PubMed2004

Modeling genetic networks from clonal analysis.

In this report a systematic approach is used to determine the approximate genetic network and robust dependencies underlying differentiation. The data considered is in the form of a binary matrix and represent the expression of the nine genes across the 99 colonies. The report is divided into two parts: the first part identifies significant pair-wise dependencies from the given binary matrix using linear correlation and mutual information. A new method is proposed to determine statistically significant dependencies estimated using the mutual information measure. In the second, a Bayesian approach is used to obtain an approximate description (equivalence class) of network structures. The robustness of linear correlation, mutual information and the equivalence class of networks is investigated with perturbation and decreasing colony number. Perturbation of the data was achieved by generating bootstrap realizations. The results are refined with biological knowledge. It was found that certain dependencies in the network are immune to perturbation and decreasing colony number and may represent robust features, inherent in the differentiation program of osteoblast progenitor cells. The methods to be discussed are generic in nature and not restricted to the experimental paradigm addressed in this study.

Animals↗
PubMed1992

Protecting the privacy of patient information in clinical networks: regulatory effectiveness analysis.

Patient privacy is one of the major issues in the development of modern clinical information system networks. Such networks will have to demonstrate an appropriate concern for privacy as a precondition of operation. Regulatory effectiveness analysis is a novel technique for measuring compliance with a technological regulatory system. By examining the public policies, legal structures, and technical tools involved in the regulatory system, it is possible to discover discontinuities that may result in noncompliance with the regulatory system.

Civil Rights↗
PubMed1998

Rapid determination of bacterial abundance, biovolume, morphology, and growth by neural network-based image analysis

Annual bacterial plankton dynamics at several depths and locations in the Baltic Sea were studied by image analysis. Individual bacteria were classified by using an artificial neural network which also effectively identified nonbacterial objects. Cell counts and frequencies of dividing cells were determined, and the data obtained agreed well with visual observations and previously published values. Cell volumes were measured accurately by comparison with bead standards. The survey included 690 images from a total of 138 samples. Each image contained approximately 200 bacteria. The images were analyzed automatically at a rate of 100 images per h. Bacterial abundance exhibited coherent patterns with time and depth, and there were distinct subsurface peaks in the summer months. Four distinct morphological classes were resolved by the image analyzer, and the dynamics of each could be visualized. The bacterial growth rates estimated from frequencies of dividing cells were different from the bacterial growth rates estimated by the thymidine incorporation method. With minor modifications, the image analysis technique described here can be used to analyze other planktonic classes.

Journal Article↗
PubMed2005

A brain-like neural network for periodicity analysis.

This paper introduces a brain-like neural model for sound processing. The periodicity analyzing network (PAN) is a bio-inspired neural network of spiking neurons. The PAN consists of complex models of neurons, which can be used for understanding the dynamics of individual neurons and neuronal networks. On a technical level, the PAN is able to compute the ratio of modulation and carrier frequency of harmonic sound signals. The PAN model may, therefore, be used in audio signal processing applications, such as sound source separation, periodicity analysis, and the cocktail party problem.

Algorithms↗
PubMed2004

New methods for joint analysis of biological networks and expression data.

SUMMARY: Biological networks, such as protein interaction, regulatory or metabolic networks, derived from public databases, biological experiments or text mining can be useful for the analysis of high-throughput experimental data. We present two algorithms embedded in the ToPNet application that show promising performance in analyzing expression data in the context of such networks. First, the Significant Area Search algorithm detects subnetworks consisting of significantly regulated genes. These subnetworks often provide hints on which biological processes are affected in the measured conditions. Second, Pathway Queries allow detection of networks including molecules that are not necessarily significantly regulated, such as transcription factors or signaling proteins. Moreover, using these queries, the user can formulate biological hypotheses and check their validity with respect to experimental data. All resulting networks and pathways can be explored further using the interactive analysis tools provided by ToPNet program.

Algorithms↗