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At least 703 records · Page 39Linked to original sources
PubMed2006

Tradeoffs of different types of species occurrence data for use in systematic conservation planning.

Data on the occurrence of species are widely used to inform the design of reserve networks. These data contain commission errors (when a species is mistakenly thought to be present) and omission errors (when a species is mistakenly thought to be absent), and the rates of the two types of error are inversely related. Point locality data can minimize commission errors, but those obtained from museum collections are generally sparse, suffer from substantial spatial bias and contain large omission errors. Geographic ranges generate large commission errors because they assume homogenous species distributions. Predicted distribution data make explicit inferences on species occurrence and their commission and omission errors depend on model structure, on the omission of variables that determine species distribution and on data resolution. Omission errors lead to identifying networks of areas for conservation action that are smaller than required and centred on known species occurrences, thus affecting the comprehensiveness, representativeness and efficiency of selected areas. Commission errors lead to selecting areas not relevant to conservation, thus affecting the representativeness and adequacy of reserve networks. Conservation plans should include an estimation of commission and omission errors in underlying species data and explicitly use this information to influence conservation planning outcomes.

Animals↗
PubMed2005

Identifying cooperative transcriptional regulations using protein-protein interactions.

Cooperative transcriptional activations among multiple transcription factors (TFs) are important to understand the mechanisms of complex transcriptional regulations in eukaryotes. Previous studies have attempted to find cooperative TFs based on gene expression data with gene expression profiles as a measure of similarity of gene regulations. In this paper, we use protein-protein interaction data to infer synergistic binding of cooperative TFs. Our fundamental idea is based on the assumption that genes contributing to a similar biological process are regulated under the same control mechanism. First, the protein-protein interaction networks are used to calculate the similarity of biological processes among genes. Second, we integrate this similarity and the chromatin immuno-precipitation data to identify cooperative TFs. Our computational experiments in yeast show that predictions made by our method have successfully identified eight pairs of cooperative TFs that have literature evidences but could not be identified by the previous method. Further, 12 new possible pairs have been inferred and we have examined the biological relevances for them. However, since a typical problem using protein-protein interaction data is that many false-positive data are contained, we propose a method combining various biological data to increase the prediction accuracy.

Cell Cycle↗
PubMed2006

Support vector machine learning from heterogeneous data: an empirical analysis using protein sequence and structure.

MOTIVATION: Drawing inferences from large, heterogeneous sets of biological data requires a theoretical framework that is capable of representing, e.g. DNA and protein sequences, protein structures, microarray expression data, various types of interaction networks, etc. Recently, a class of algorithms known as kernel methods has emerged as a powerful framework for combining diverse types of data. The support vector machine (SVM) algorithm is the most popular kernel method, due to its theoretical underpinnings and strong empirical performance on a wide variety of classification tasks. Furthermore, several recently described extensions allow the SVM to assign relative weights to various datasets, depending upon their utilities in performing a given classification task. RESULTS: In this work, we empirically investigate the performance of the SVM on the task of inferring gene functional annotations from a combination of protein sequence and structure data. Our results suggest that the SVM is quite robust to noise in the input datasets. Consequently, in the presence of only two types of data, an SVM trained from an unweighted combination of datasets performs as well or better than a more sophisticated algorithm that assigns weights to individual data types. Indeed, for this simple case, we can demonstrate empirically that no solution is significantly better than the naive, unweighted average of the two datasets. On the other hand, when multiple noisy datasets are included in the experiment, then the naive approach fares worse than the weighted approach. Our results suggest that for many applications, a naive unweighted sum of kernels may be sufficient. AVAILABILITY: http://noble.gs.washington.edu/proj/seqstruct

Algorithms↗
PubMed2006

A dynamic model for action understanding and goal-directed imitation.

The understanding of other individuals' actions is a fundamental cognitive skill for all species living in social groups. Recent neurophysiological evidence suggests that an observer may achieve the understanding by mapping visual information onto his own motor repertoire to reproduce the action effect. However, due to differences in embodiment, environmental constraints or motor skills, this mapping very often cannot be direct. In this paper, we present a dynamic network model which represents in its layers the functionality of neurons in different interconnected brain areas known to be involved in action observation/execution tasks. The model aims at substantiating the idea that action understanding is a continuous process which combines sensory evidence, prior task knowledge and a goal-directed matching of action observation and action execution. The model is tested in variations of an imitation task in which an observer with dissimilar embodiment tries to reproduce the perceived or inferred end-state of a grasping-placing sequence. We also propose and test a biologically plausible learning scheme which allows establishing during practice a goal-directed organization of the distributed network. The modeling results are discussed with respect to recent experimental findings in action observation/execution studies.

Action Potentials↗
PubMed2003

Whole-proteome interaction mining.

MOTIVATION: A major post-genomic scientific and technological pursuit is to describe the functions performed by the proteins encoded by the genome. One strategy is to first identify the protein-protein interactions in a proteome, then determine pathways and overall structure relating these interactions, and finally to statistically infer functional roles of individual proteins. Although huge amounts of genomic data are at hand, current experimental protein interaction assays must overcome technical problems to scale-up for high-throughput analysis. In the meantime, bioinformatics approaches may help bridge the information gap required for inference of protein function. In this paper, a previously described data mining approach to prediction of protein-protein interactions (Bock and Gough, 2001, Bioinformatics, 17, 455-460) is extended to interaction mining on a proteome-wide scale. An algorithm (the phylogenetic bootstrap) is introduced, which suggests traversal of a phenogram, interleaving rounds of computation and experiment, to develop a knowledge base of protein interactions in genetically-similar organisms. RESULTS: The interaction mining approach was demonstrated by building a learning system based on 1,039 experimentally validated protein-protein interactions in the human gastric bacterium Helicobacter pylori. An estimate of the generalization performance of the classifier was derived from 10-fold cross-validation, which indicated expected upper bounds on precision of 80% and sensitivity of 69% when applied to related organisms. One such organism is the enteric pathogen Campylobacter jejuni, in which comprehensive machine learning prediction of all possible pairwise protein-protein interactions was performed. The resulting network of interactions shares an average protein connectivity characteristic in common with previous investigations reported in the literature, offering strong evidence supporting the biological feasibility of the hypothesized map. For inferences about complete proteomes in which the number of pairwise non-interactions is expected to be much larger than the number of actual interactions, we anticipate that the sensitivity will remain the same but precision may decrease. We present specific biological examples of two subnetworks of protein-protein interactions in C. jejuni resulting from the application of this approach, including elements of a two-component signal transduction systems for thermoregulation, and a ferritin uptake network.

Algorithms↗
PubMed1992

Computer-assisted three-dimensional reconstruction and simulations of vestibular macular neural connectivities.

The macular neuroepithelium is morphologically organized as a weighted neural network for parallel distributed processing of information. The network is continuous across the striola, where some type II hair cells synapse with calyces containing type I cells with tufts of opposite directional polarities. Whether other hair cell to calyx appositions that lack synapses interact because of intercellular potassium accumulation remains an open question. A functionally important inference of macular organization is that just as arrays of hair cells communicate an entire piece of information to a nerve fiber, so do macular subarrays of nerve fibers (not single units) carry the whole coded message to the brain stem. Moreover, the size of the network subarray can expand or become more limited depending upon the strength and/or duration of the input. It is the functioning of the network and its subarrays that must be understood if we are to learn how maculas carry out their work and adapt to new environments. Simulations of functioning maculas, or subparts, based on precise morphology and known physiology are useful tools to gain insights into macular information processing. The current simulations of afferent collateral electrical activity are a prelude to development of a 3-D model. The simulations demonstrate a relationship between geometry and function, with the diameter of the stem apparently being a major determinant of electrical activity transmitted to the base in the case of collaterals with short stems. Thus, while changes in synaptic number and/or size may be an important adaptive mechanism in an altered g environment, changes in diameter of the stem is another means of altering outflow. Research on the effects of microgravity should be extremely useful in examining the validity of this and other concepts of neural adaptation, since maculas are biological linear accelerometers ideally suited to the task. Maculas are also extremely interesting to study in detail because of the richness of connectivities and submicroscopic organization they present. Many of their features are common with more complex parts of the brain. It seems possible that knowledge of the three-dimensional geometric relationships operative in a functioning macula will contribute much to the understanding of the dynamics underlying more complex behavior. Computerized approaches greatly facilitate this task and provide an objective method of analysis. It is likely that, in the end, simple rules will be found to govern optimal neural architectural organization, even at higher cognitive levels. The architecture only appears complex because we do not yet grasp its meaning.(ABSTRACT TRUNCATED AT 400 WORDS)

Acoustic Maculae↗
PubMed2026

Multitarget interactions of bisphenol A in polycystic ovary syndrome: evidence from integrated network toxicology, mendelian randomization, and molecular docking.

OBJECTIVE: To study the potential pathogenic mechanisms of bisphenol A (BPA) in polycystic ovary syndrome (PCOS) using an integrative computational strategy. DESIGN: Integrative computational study combining network toxicology, Mendelian randomization (MR), and molecular docking. SUBJECTS: For MR analysis, genetic data were sourced from large European-ancestry cohorts, including plasma protein quantitative trait loci data and genome-wide association study summary statistics for PCOS (3,045 cases and 267,780 controls). EXPOSURE: In silico exposure to BPA for target prediction; genetically predicted plasma protein levels for causal inference. MAIN OUTCOME MEASURES: Identification of overlapping targets between BPA and PCOS; functional enrichment pathways; causal effects of prioritized proteins on PCOS risk (odds ratios with 95% confidence intervals); binding affinities between BPA and core targets (kcal/mol). RESULTS: Network toxicology identified 310 overlapping targets between BPA and PCOS. Enrichment analyses revealed significant involvement in endocrine signaling, inflammatory pathways (eg, IL-17), and cellular processes. MR demonstrated that genetically elevated levels of RET, CXCL8, HTR6, MMP1, MMP9, NTRK1, and TNNI2 were significantly associated with increased PCOS risk, whereas higher PSAP and SHBG levels were protective. Molecular docking confirmed stable binding between BPA and all nine key targets, with strongest affinity for SHBG (-8.4 kcal/mol), followed by NTRK1, TNNI2, and RET. CONCLUSION: This integrative investigation suggests that BPA may contribute to PCOS pathogenesis through multitarget interactions involving inflammatory mediators, endocrine regulators, and tissue remodeling proteins. The findings provide prioritized targets and mechanistic insights for future experimental validation and environmental risk assessment.

Female↗
PubMed2026

GRNContext: an interactive web platform for contextualized gene regulatory networks visualization across human cancers.

SUMMARY: While current Gene Regulatory Network (GRN) databases provide comprehensive reference maps of potential interactions between transcription factors and target genes, they do not specify which regulatory interactions are active within specific biological contexts. This limitation is particularly critical in cancer, where transcriptional programs are inherently tissue-specific. To address this gap, we developed GRNContext, an interactive web platform designed for the visualization, exploration, and comparative analysis of gene regulatory networks contextualized across 33 cancer types from The Cancer Genome Atlas (TCGA). Our approach uses the TFLink human reference GRN as a starting point and integrates TCGA transcriptomic profiles to infer cancer-specific regulatory activity. Regulatory relevance was assessed using complementary machine learning and statistical methods, which were unified into a consensus score to prioritize and filter the most relevant candidate regulators for each target gene. By providing both curated context-specific GRNs and a user-friendly platform, GRNContext constitutes a comprehensive and accessible resource that supports mechanistic investigations, hypothesis generation, and translational research focused on transcriptional regulation in cancer. AVAILABILITY AND IMPLEMENTATION: GRNContext is supported by all major browsers and freely available on the web at https://apps.cienciavida.org/grncontext. It is implemented as a client-server web application featuring a FastAPI backend and a React frontend utilizing Cytoscape.js for interactive network visualization, all containerized via Docker for cross-platform compatibility.

Humans↗
PubMed2005

Transcript profiling of Eucalyptus xylem genes during tension wood formation.

Tension wood formed in response to gravitational force is a striking example of the plasticity of angiosperm wood. In this study our goal was to characterize the early changes in gene expression during tension wood formation in Eucalyptus. Using cDNA array technology, transcript profiling of 231 genes preferentially expressed in differentiating Eucalyptus xylem was followed from 6 h to 1 wk of a tension time course of artificially bent Eucalyptus trees. 196 genes were differentially regulated between control and bent trees, some exhibiting distinctive expression patterns related to changes in secondary cell wall structure and composition. For instance, expression of a cellulose synthase gene was well correlated with the appearance of the G-layers. Cluster correlation analysis revealed differential regulation of lignin biosynthetic genes and may also be used to help infer the function of unknown gene products. Eucalyptus wood transcriptome analysis during tension wood formation not only provided new clues into the transcriptional regulatory network of genes preferentially expressed in xylem, but also highlighted candidate genes responsible for the genetic and environmentally induced variation of wood quality traits.

Biomechanical Phenomena↗
PubMed2004

Enriching for direct regulatory targets in perturbed gene-expression profiles.

Here we build on a previously proposed algorithm to infer direct regulatory relationships using gene-expression profiles from cells in which individual genes are deleted or overexpressed. The updated algorithm can process networks containing feedback loops, incorporate positive and negative regulatory relationships during network reconstruction, and utilize data from double mutants to resolve ambiguous regulatory relationships. When applied to experimental data the reconstruction procedure preferentially retains direct transcription factor-target relationships.

Algorithms↗
PubMed2000

Serial processing in reading aloud: no challenge for a parallel model.

K. Rastle and M. Coltheart (1999) challenged parallel models of reading by showing that the cost of irregularity in low-frequency exception words was modulated by the position of the irregularity in the word. This position-of-irregularity effect was taken as strong evidence of serial processing in reading. This article refutes Rastle and Coltheart's theoretical conclusions in 3 ways: First, a parallel model, the connectionist dual process model (M. Zorzi, G. Houghton, & B. Butterworth, 1998b), produces a position-of-irregularity effect. Second, the supposed serial effect can be reduced to a position-specific grapheme-phoneme consistency effect. Third, the position-of-irregularity effect vanishes when the experimental data are reanalyzed using grapheme-phoneme consistency as the covariate. This demonstration has broader implications for studies aiming at adjudicating between models: Strong inferences should be avoided until the computational models are actually tested.

Attention↗
PubMed2006

Gene networks as a tool to understand transcriptional regulation.

Gene regulatory networks, or simply gene networks (GNs), have shown to be a promising approach that the bioinformatics community has been developing for studying regulatory mechanisms in biological systems. GNs are built from the genome-wide high-throughput gene expression data that are often available from DNA microarray experiments. Conceptually, GNs are (un)directed graphs, where the nodes correspond to the genes and a link between a pair of genes denotes a regulatory interaction that occurs at transcriptional level. In the present study, we had two objectives: 1) to develop a framework for GN reconstruction based on a Bayesian network model that captures direct interactions between genes through nonparametric regression with B-splines, and 2) to demonstrate the potential of GNs in the analysis of expression data of a real biological system, the yeast pheromone response pathway. Our framework also included a number of search schemes to learn the network. We present an intuitive notion of GN theory as well as the detailed mathematical foundations of the model. A comprehensive analysis of the consistency of the model when tested with biological data was done through the analysis of the GNs inferred for the yeast pheromone pathway. Our results agree fairly well with what was expected based on the literature, and we developed some hypotheses about this system. Using this analysis, we intended to provide a guide on how GNs can be effectively used to study transcriptional regulation. We also discussed the limitations of GNs and the future direction of network analysis for genomic data. The software is available upon request.

Bayes Theorem↗
PubMedDate not supplied

Some neural network applications in environmental sciences. Part I: forward and inverse problems in geophysical remote measurements.

A broad class of neural network (NN) applications dealing with the remote measurements of geophysical (physical, chemical, and biological) parameters of the oceans, atmosphere, and land surface is presented. In order to infer these parameters from remote sensing (RS) measurements, standard retrieval and variational techniques are applied. Both techniques require a data converter (transfer function or forward model) to convert satellite measurements into geophysical parameters or vice versa. In many cases, the transfer function and the forward model can be represented as a continuous nonlinear mapping. Because the NN technique is a generic technique for nonlinear mapping, it can be used beneficially for modeling transfer functions and forward models. These applications are introduced in a broader framework of solving forward and inverse problems in RS. In this broader context, we show that NN is an appropriate and efficient tool for solving forward and inverse problems in RS and for developing fast and accurate forward models and accurate and robust retrieval algorithms. Theoretical considerations are illustrated by several real-life examples-operational NN applications developed by the authors for SSM/I and medium resolution imaging spectrometer sensors.

Environment↗
PubMed1976

Uterine vasculature of the pregnant pig: a scanning electron microscope study.

Colored cold setting acrylic cement was injected through catheters into the arteries and veins of sixteen gravid uteri obtained from pigs at various stages of pregnancy. After the cement had set hard, the reproductive tracts were macerated in concentrated acid and the vascular casts resulting were washed clean of digested tissues. Small pieces of cast were coated with gold and viewed by means of a scanning electron microscope. Early in pregnancy the capillary network consists of low parallel ranks of ridges and troughs; later, cross ridging was observed which developed in complexity as pregnancy progressed. The density of capillaries comprising the microvascular network also increased towards term. No comparable changes were noted in the relatively less well vascularized areas surrounding the mouths of the uterine glands. This technique surmounted the problems of obtaining an adequate depth of focus for viewing the capillary network of the gravid uterus with the light microscope. The relationships that the blood vessels bore to the overlying epithelial layers were discussed and the inference tentatively drawn that blood flow at the capillary level was from ridge top to trough base.

Animals↗
PubMed1987

Detection and estimation of neural connectivity based on crosscorrelation analysis.

Crosscorrelation analysis of simultaneously recorded activity of pairs of neurons is a common tool to infer functional neural connectivity. The adequacy of crosscorrelation procedures to detect and estimate neural connectivity has been investigated by means of computer simulations of small networks composed of fairly realistic modelneurons. If the mean interval of neural firings is much larger than the duration of postsynaptic potentials, which will be the case in many central brain areas excitatory connections are easier to detect than inhibitory ones. On the other hand, inhibitory connections are revealed better if the mean firing interval is much smaller than post-synaptic potential duration. In general the effects of external stimuli and the effects of neural connectivity do not add linearly. Furthermore, neurons may exhibit a certain degree of timelock to the stimulus. For these reasons the commonly applied "shift predictor" procedure to separate stimulus and neural effects appears to be of limited value. In case of parallel direct and indirect neural pathways between two neurons crosscorrelation analysis does not estimate the direct connection but instead an effective connectivity, which reflects the combined influences of the parallel pathways.

Animals↗
PubMed1990

Kinetoplast DNA minicircles are inherited from both parents in genetic hybrids of Trypanosoma brucei.

We have examined the inheritance of kinetoplast DNA (kDNA) in gentic crosses of trypanosomes. In 2 independent crosses of Trypanosoma brucei spp. trypanosomes, the kDNA maxicircles which carry the genes for mitochondrial biogenesis, were inherited from one parent only, as already found by other workers. However, the other component of kDNA, the minicircles, were inherited from both parents. This was demonstrated by Southern analysis using cloned minicircle probes. The inheritance of kDNA is therefore not uniparental. Our data point to fusion of the parental kinetoplast DNA networks during genetic exchange, with gradual loss of one or other parental maxicircle type due to random segregation of maxicircles at subsequent mitotic divisions. We infer that the first event of genetic exchange is fusion of parental trypanosomes (either haploid or diploid), followed at some point by fusion of the parental mitochondria.

Animals↗
PubMed2005

Feature detection using spikes: the greedy approach.

A goal of low-level neural processes is to build an efficient code extracting the relevant information from the sensory input. It is believed that this is implemented in cortical areas by elementary inferential computations dynamically extracting the most likely parameters corresponding to the sensory signal. We explore here a neuro-mimetic feed-forward model of the primary visual area (VI) solving this problem in the case where the signal may be described by a robust linear generative model. This model uses an over-complete dictionary of primitives which provides a distributed probabilistic representation of input features. Relying on an efficiency criterion, we derive an algorithm as an approximate solution which uses incremental greedy inference processes. This algorithm is similar to 'Matching Pursuit' and mimics the parallel architecture of neural computations. We propose here a simple implementation using a network of spiking integrate-and-fire neurons which communicate using lateral interactions. Numerical simulations show that this Sparse Spike Coding strategy provides an efficient model for representing visual data from a set of natural images. Even though it is simplistic, this transformation of spatial data into a spatio-temporal pattern of binary events provides an accurate description of some complex neural patterns observed in the spiking activity of biological neural networks.

Action Potentials↗