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At least 19 recordsLinked to original sources
PubMed2006

Similarity networks of protein binding sites.

An increasing attention has been dedicated to the characterization of complex networks within the protein world. This work is reporting how we uncovered networked structures that reflected the structural similarities among protein binding sites. First, a 211 binding sites dataset has been compiled by removing the redundant proteins in the Protein Ligand Database (PLD) (http://www-mitchell.ch.cam.ac.uk/pld/). Using a clique detection algorithm we have performed all-against-all binding site comparisons among the 211 available ones. Within the set of nodes representing each binding site an edge was added whenever a pair of binding sites had a similarity higher than a threshold value. The generated similarity networks revealed that many nodes had few links and only few were highly connected, but due to the limited data available it was not possible to definitively prove a scale-free architecture. Within the same dataset, the binding site similarity networks were compared with the networks of sequence and fold similarity networks. In the protein world, indications were found that structure is better conserved than sequence, but on its own, sequence was better conserved than the subset of functional residues forming the binding site. Because a binding site is strongly linked with protein function, the identification of protein binding site similarity networks could accelerate the functional annotation of newly identified genes. In view of this we have discussed several potential applications of binding site similarity networks, such as the construction of novel binding site classification databases, as well as the implications for protein molecular design in general and computational chemogenomics in particular.

Animals↗
PubMed2006

Integrating multi-attribute similarity networks for robust representation of the protein space.

MOTIVATION: A global view of the protein space is essential for functional and evolutionary analysis of proteins. In order to achieve this, a similarity network can be built using pairwise relationships among proteins. However, existing similarity networks employ a single similarity measure and therefore their utility depends highly on the quality of the selected measure. A more robust representation of the protein space can be realized if multiple sources of information are used. RESULTS: We propose a novel approach for analyzing multi-attribute similarity networks by combining random walks on graphs with Bayesian theory. A multi-attribute network is created by combining sequence and structure based similarity measures. For each attribute of the similarity network, one can compute a measure of affinity from a given protein to every other protein in the network using random walks. This process makes use of the implicit clustering information of the similarity network, and we show that it is superior to naive, local ranking methods. We then combine the computed affinities using a Bayesian framework. In particular, when we train a Bayesian model for automated classification of a novel protein, we achieve high classification accuracy and outperform single attribute networks. In addition, we demonstrate the effectiveness of our technique by comparison with a competing kernel-based information integration approach.

Algorithms↗
PubMed2025

miss-SNF: a multimodal patient similarity network integration approach to handle completely missing data sources.

MOTIVATION: Precision medicine leverages patient-specific multimodal data to improve prevention, diagnosis, prognosis, and treatment of diseases. Advancing precision medicine requires the non-trivial integration of complex, heterogeneous, and potentially high-dimensional data sources, such as multi-omics and clinical data. In the literature, several approaches have been proposed to manage missing data, but are usually limited to the recovery of subsets of features for a subset of patients. A largely overlooked problem is the integration of multiple sources of data when one or more of them are completely missing for a subset of patients, a relatively common condition in clinical practice. RESULTS: We propose miss-Similarity Network Fusion (miss-SNF), a novel general-purpose data integration approach designed to manage completely missing data in the context of patient similarity networks. miss-SNF integrates incomplete unimodal patient similarity networks by leveraging a non-linear message-passing strategy borrowed from the SNF algorithm. miss-SNF is able to recover missing patient similarities and is "task agnostic", in the sense that can integrate partial data for both unsupervised and supervised prediction tasks. Experimental analyses on nine cancer datasets from The Cancer Genome Atlas (TCGA) demonstrate that miss-SNF achieves state-of-the-art results in recovering similarities and in identifying patients subgroups enriched in clinically relevant variables and having differential survival. Moreover, amputation experiments show that miss-SNF supervised prediction of cancer clinical outcomes and Alzheimer's disease diagnosis with completely missing data achieves results comparable to those obtained when all the data are available. AVAILABILITY AND IMPLEMENTATION: miss-SNF code, implemented in R, is available at https://github.com/AnacletoLAB/missSNF.

Humans↗
PubMed2004

Protein ranking: from local to global structure in the protein similarity network.

Biologists regularly search databases of DNA or protein sequences for evolutionary or functional relationships to a given query sequence. We describe a ranking algorithm that exploits the entire network structure of similarity relationships among proteins in a sequence database by performing a diffusion operation on a precomputed, weighted network. The resulting ranking algorithm, evaluated by using a human-curated database of protein structures, is efficient and provides significantly better rankings than a local network search algorithm such as psi-blast.

Algorithms↗
PubMed2003

Similar network activated by young and old adults during the acquisition of a motor sequence.

In this functional MRI (fMRI) study, we investigated ageing effects on motor skill learning. We applied an adapted version of the serial reaction time (SRT) task to extensive groups of young (N=26) and elderly (N=40) subjects. Since indications have been provided for age-related shrinkage of brain regions assumed to be critical to motor skill learning, we tested the hypothesis that age effects on implicit sequence learning are larger on a neurofunctional level than on a behavioural level. The SRT task consisted of two identical scan sessions, in which subjects had to manually trail an asterisk appearing serially in one of four spatial positions by means of button-pressing. Reliable response time reductions were already found in the first session for both the young and the elderly groups, when comparing a fixed sequence condition to a random sequence, but the learning effect was greater for the young subjects. In the second session, though, both groups showed a similar degree of learning. This indicates that implicit sequence learning is still intact in elderly adults, but that the rate of learning is somewhat slower. Reliable learning-related changes in brain activity were also observed. A similar network of brain regions was recruited by both groups during the fixed compared to the random sequence, involving several regions that have been previously associated with implicit sequence learning, including bilateral parietal, and frontal regions, the supplementary motor area (SMA), cerebellum and the basal ganglia. The direct group comparison did not reveal any differences in brain activity. In addition, we did not observe any significant differences in activity when comparing the different sessions either, neither for the young nor for the elderly subjects. Hence, we did not find indications for an age-related functional reorganisation of neural networks involved in motor sequence learning. In view of earlier reports of pronounced ageing effects on the performance on declarative memory tasks, our finding of age-related sparing of processes that sustain motor skill learning, provides further support for the proposition of different memory systems relying on different brain substrates.

Adult↗
PubMed2005

A graph spectral analysis of the structural similarity network of protein chains.

We present a simple method for the analysis of large networks based on their graph spectral properties. One of the advantages of this method is that it uses a single numerical computation to identify subclusters in a connected graph, which can significantly simplify the complexity involved in analyzing large graphs. This is illustrated using a network of protein chains constructed on the basis of their structural similarities. The large-scale network properties and the cluster and subcluster organization of the protein chain network are presented. We summarize the results of structural and functional analyses of the nodes present in these clusters and elucidate the implications of structural similarity in the protein chain universe.

Cluster Analysis↗
PubMed2026

ECLIPSE: exploring the dark proteome of ESKAPE pathogens through the sequence similarity network of the Protein Universe Atlas.

MOTIVATION: The accelerating crisis of antimicrobial resistance among the critical so-called ESKAPE pathogens demands the urgent identification of novel molecular targets. However, a substantial fraction of ESKAPE proteomes remains functionally uncharacterized, with many genes annotated as encoding hypothetical proteins. These protein sequences often lack significant similarity to known protein families when conventional homology-based annotation methods are used and thus remain "dark". This limits our ability to explore their roles in pathogenicity, and it is thus crucial to bridge this substantial gap in pathogen biology by developing new strategies to illuminate these "dark" regions of the ESKAPE pan-proteome. RESULTS: We introduce ECLIPSE (ESKAPE Connectome Linkage and Inference for Proteome Sequence Exploration), a network-based computational framework that systematically identifies and prioritizes functionally dark protein families in ESKAPE pan-proteomes. ECLIPSE embeds target ESKAPE pathogen proteomes within the global sequence similarity network of the Protein Universe Atlas. It detects connected components composed entirely of unannotated proteins, called the "dark proteome." As a case study, we applied ECLIPSE to a pan-proteome of 3 460 657 protein sequences from 635 strains of Pseudomonas aeruginosa (PA). ECLIPSE identified 120 985 proteins (4%) residing in completely dark connected components. Furthermore, we have performed a taxonomic diversity analysis using normalized Shannon indices to characterize each dark component by its enrichment in ESKAPE pathogens. The analysis utilized the evenness (E) value (see Methods 2.1), which distinguishes Pseudomonas-specific (target-specific) from ESKAPE-enriched dark components. We then developed the Dark Proteome Prioritization Score (DPPS), a composite multidimensional scoring framework (see Methods 2.5). It ranks these dark components by biological relevance across four orthogonal axes: (i) functional darkness, (ii) P. aeruginosa proportion in the Atlas, (iii) AMR-clade taxonomic restriction, and (iv) conservation across the 635 P. aeruginosa strains. This framework outputs a robust four-tier scoring system; the prioritized Tier I components were validated by weight sensitivity analysis and remained stable across 500 Monte Carlo weight perturbations. Structural characterization of one of the top-ranked ESKAPE-enriched dark components revealed that it belongs to the beta-barrel fold DUF1302 (PF06980) family, for which no experimentally solved three-dimensional structure exists in the PDB. The genomic context analysis indicates that it is co-localized with a LuxR-type transcriptional regulator. Collectively, ECLIPSE identifies evolutionarily conserved, structurally defined, and functionally dark proteins enriched across ESKAPE pathogens; these dark proteins can further be utilized as alternative antimicrobial targets for experimental characterization. AVAILABILITY AND IMPLEMENTATION: The source code and dataset are available for free at: Github: https://github.com/surabhilata/ECLIPSE.git, Zenodo: DOI: 10.5281/zenodo.21064323.

Proteome↗
PubMed2004

Similar network activity from disparate circuit parameters.

It is often assumed that cellular and synaptic properties need to be regulated to specific values to allow a neuronal network to function properly. To determine how tightly neuronal properties and synaptic strengths need to be tuned to produce a given network output, we simulated more than 20 million versions of a three-cell model of the pyloric network of the crustacean stomatogastric ganglion using different combinations of synapse strengths and neuron properties. We found that virtually indistinguishable network activity can arise from widely disparate sets of underlying mechanisms, suggesting that there could be considerable animal-to-animal variability in many of the parameters that control network activity, and that many different combinations of synaptic strengths and intrinsic membrane properties can be consistent with appropriate network performance.

Action Potentials↗
PubMed1993

Contrasting approaches aim at similar network goals.

At any node on the network implementation timeline, whether in the first planning stages or after the bottom-line benefits are stacking up, networking is a compelling idea. So compelling, with flexibility, cost-cutting results and strengthened community resources, that it extends its influence into the future of the organizations it serves. Two institutions, University Hospital in Denver and the Medical Center of Delaware in Wilmington, Del., illustrate networking's tempting benefits. University Hospital disabled its mainframe, switching to an Ungermann-Bass Access One smart hub in January of this year. Their systems are added to the network one-at-a-time, with plans for complete integration once everything is working online. In contrast, the Medical Center of Delaware shifted to networking in 1987, following a five-year implementation plan. The completed, integrated network is now a strategic tool for containing costs an MCD.

Colorado↗
PubMed2006

Vertex similarity in networks.

We consider methods for quantifying the similarity of vertices in networks. We propose a measure of similarity based on the concept that two vertices are similar if their immediate neighbors in the network are themselves similar. This leads to a self-consistent matrix formulation of similarity that can be evaluated iteratively using only a knowledge of the adjacency matrix of the network. We test our similarity measure on computer-generated networks for which the expected results are known, and on a number of real-world networks.

Animals↗
PubMed1992

Toward normative expert systems: Part II. Probability-based representations for efficient knowledge acquisition and inference.

We address practical issues concerning the construction and use of decision-theoretic or normative expert systems for diagnosis. In particular, we examine Pathfinder, a normative expert system that assists surgical pathologists with the diagnosis of lymph-node diseases, and discuss the representation of dependencies among pieces of evidence within this system. We describe the belief network, a graphical representation of probabilistic dependencies. We see how Pathfinder uses a belief network to construct differential diagnosis efficiently, even when there are dependencies among pieces of evidence. In addition, we introduce an extension of the belief-network representation called a similarity network, a tool for constructing large and complex belief networks. The representation allows a user to construct independent belief networks for subsets of a given domain. A valid belief network for the entire domain can then be constructed from the individual belief networks. We also introduce the partition, a graphical representation that facilitates the assessment of probabilities associated with a belief network. We show that the similarity-network and partition representations made practical the construction of Pathfinder.

Algorithms↗
PubMed2005

Identifying remote protein homologs by network propagation.

Perhaps the most widely used applications of bioinformatics are tools such as psi-blast for searching sequence databases. We describe a recently developed protein database search algorithm called rankprop. rankprop relies upon a precomputed network of pairwise protein similarities. The algorithm performs a diffusion operation from a specified query protein across the protein similarity network. The resulting activation scores, assigned to each database protein, encode information about the global structure of the protein similarity network. This type of algorithm has a rich history in associationist psychology, artificial intelligence and web search. We describe the rankprop algorithm and its relatives, and we provide evidence that the algorithm successfully improves upon the rankings produced by psi-blast.

Algorithms↗
PubMed1996

Self-similar Neural Networks Based on a Kohonen Learning Rule.

One of the most striking features about the perceptual machinery of mammals is its regularity of structure. This is particularly evident in the mammalian visual system, as the work pioneered by Hubel and Wiesel has demonstrated. The likely source of this regularity is the visual stimulus, which does not change randomly from instant to instant, but is affected primarily by motions of both the animal and objects in the environment. These motions induce structured changes in the visual stimulus, which might well be expected to have a significant effect in shaping the structure of the visual machinery, whether through individual plasticity or longer-term genetic changes.The work reported in this paper is an investigation of the structures that may evolve in a simple artificial neural network driven not by random changes of input pattern, but directly by transformations which are themselves related to transformations of the input signal through an analysis of motion-prediction error. Results are presented which demonstrate that such networks can evolve a remarkable degree of regularity which reflects the underlying symmetry group of the transformations, both in one and two dimensions. An appropriate and visually plausible choice of transformation group can lead to the development of foveal structures in two-dimensional networks. We also present some preliminary results on parametrised function spaces which support the general conclusion that global structure bearing a considerable resemblance to that found in the mammalian visual system can evolve as the result of a simple learning rule in networks driven by transformations similar to those typically encountered in vision. Copyright 1996 Elsevier Science Ltd

Journal Article↗
PubMed2003

A neural network-based similarity index for clustering DNA microarray data.

A common approach to the analysis of gene expression data is to define clusters of genes that have similar expression. A critical step in cluster analysis is the determination of similarity between the expression levels of two genes. We introduce a neural network-based similarity index as a non-linear similarity index and compare the results with other proximity measures for Saccharomyces cerevisiae gene expression data. We show that the clusters obtained using Euclidean distance, correlation coefficients, and mutual information were not significantly different. The clusters formed with the neural network-based index were more in agreement with those defined by functional categories and common regulatory motifs.

Algorithms↗
PubMed1996

Comparison of structurally different allosteric modulators of muscarinic receptors by self-organizing neural networks.

Similarities in the molecular structure and surface properties of the allosteric modulators of muscarinic receptors, alcuronium, gallamine, tubocurarine, and the hexamethonium compound W84, a well-known pharmacological tool, are explored. The analysis of the molecular electrostatic potential (MEP) as well as of the shape of the molecular surface is performed by self-organizing neural networks. A distorted sandwich conformation of W84 is suggested to be the active form. The importance of the MEP for binding of these compounds could be established.

Alcuronium↗
PubMed1989

A calsequestrin-like protein in the endoplasmic reticulum of the sea urchin: localization and dynamics in the egg and first cell cycle embryo.

Using an antiserum produced against a purified calsequestrin-like (CSL) protein from a microsomal fraction of sea urchin eggs, we performed light and electron microscopic immunocytochemical localizations on sea urchin eggs and embryos in the first cell cycle. The sea urchin CSL protein has been found to bind Ca++ similarly to calsequestrin, the well-characterized Ca++ storage protein in the sarcoplasmic reticulum of muscle cells. In semi-thin frozen sections of unfertilized eggs, immunofluorescent staining revealed a tubuloreticular network throughout the cytoplasm. Staining of isolated egg cortices with the CSL protein antiserum showed the presence of a submembranous polygonal, tubular network similar to ER network patterns seen in other cells and in egg cortices treated with the membrane staining dye DiIC16[3]. In frozen sections of embryos during interphase of the first cell cycle, a cytoplasmic network similar to that of the unfertilized egg was present. During mitosis, we observed a dramatic concentration of the antibody staining within the asters of the mitotic apparatus where ER is known to aggregate. Electron microscopic localization on unfertilized eggs using peroxidase-labeled secondary antibody demonstrated the presence of the CSL protein within the luminal compartment of ER-like tubules. Finally, in frozen sections of centrifugally stratified eggs, the immunofluorescent staining concentrated in the clear zone: a layer highly enriched in ER and thought to be the site of calcium release upon fertilization. This localization of a CSL protein within the ER of the egg provides evidence for the ability of this organelle to serve a Ca++ storage role in the regulation of intracellular Ca++ in nonmuscle cells in general, and in the regulation of fertilization and cell division in sea urchin eggs in particular.

Animals↗