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PubMed2001

A method for automated temporal knowledge acquisition applied to sleep-related breathing disorders.

This paper presents a method for the discovery of temporal patterns in multivariate time series and their conversion into a linguistic knowledge representation applied to sleep-related breathing disorders. The main idea lies in introducing several abstraction levels that allow a step-wise identification of temporal patterns. Self-organizing neural networks are used to discover elementary patterns in the time series. Machine learning (ML) algorithms use the results of the neural networks to automatically generate a rule-based description. At the next levels, temporal grammatical rules are inferred. This method covers one of the main "bottlenecks" in the design of knowledge-based systems, namely, the knowledge acquisition problem. An evaluation of the rules lead to an overall sensitivity of 0.762, and a specificity of 0.758.

Artificial Intelligence↗

PubMed2002

Role of afferent activity in the development of cortical specification.

The surgical cross-modal rewiring paradigm is an experimental method for examining the physiological and anatomical consequences of exposing developing cortical subregions to specific types of patterned sensory inputs. Data from these experiments provide strong inferences about the role of extrinsic (subcortical) cortical inputs in shaping the local cortical networks that organize and process sensory information. Behavioral results from this work also suggest that such activity (and activity in general) is a profound organizer of cerebral connectivity. We discuss one future direction of these studies: the implication that extrinsic inputs regulate developmental genes that are responsible for refining the connectivity within local circuits, and a strategy to discover and characterize such genes.

Afferent Pathways↗

PubMed2005

Modulation of short-latency intracortical inhibition in human primary motor cortex during synchronised versus syncopated finger movements.

Rhythmic movements are inherently more stable and easier to perform when they are synchronised with a periodic stimulus, as opposed to syncopated between the beats of a pacing stimulus. Although this behavioural phenomenon is well documented, its neurophysiological basis is poorly understood. In a first experiment, we demonstrated that all healthy subjects (N=8) performing index finger abduction in time with an auditory metronome exhibited transitions from syncopation to synchronisation when the metronome tempo was scaled from 0.8 to 2.0 Hz. Subjects' mean transition frequency was 1.7+/-0.2 Hz. In a second experiment, we used paired-pulse transcranial magnetic stimulation to examine short-latency intracortical inhibition (sICI) directed towards the first dorsal interosseous (FDI) muscle in healthy subjects (N=9) who made synchronised and syncopated phasic finger movements in time with metronome pacing of 1.0 Hz. Despite the equivalence between the patterns in terms of task performance and corticospinal excitability of FDI at this movement frequency, there was significantly greater sICI during syncopation than during synchronisation. From this result, we infer that the stability of movement patterns may be contingent upon excitability of inhibitory networks within primary motor cortex.

Adult↗

PubMed2026

Modelling the effects of biological intervention in a dynamical gene network.

Cellular response to environmental and internal signals can be modeled by dynamical gene regulatory networks (GRN). In the literature, three main classes of gene network models can be distinguished: (1) non-quantitative (or data-based) models which do not describe the probability distribution of gene expressions; (2) quantitative models which fully describe the probability distribution of all genes co-expression; and (3) mechanistic models which allow for a causal interpretation of gene interactions. We propose two rigorous frameworks to model gene alteration in a dynamical GRN, depending on whether the network model is quantitative or mechanistic. We explain how these models can be used for design of experiment, or, if additional alteration data are available, for validation purposes or to improve the parameter estimation of the original model. We apply these methods to the Gaussian graphical model, which is quantitative but non-mechanistic, and to mechanistic models of Bayesian networks and penalized linear regression.

Gene Regulatory Networks↗

PubMed2001

Sediment filled fractures in the Permo-Triassic sandstones of the Cheshire basin: observations and implications for pollutant transport.

Fracture mapping in a tunnel system and at nearby outcrop on the Runcorn Penninsula, UK, suggests the need for a review of the potential pathways for pollutant transport in Permo-Triassic sandstone aquifers. Sediment infilling is pervasive in the largest sub-vertical multi-layer fractures in the study area, both at the surface and to a depth of about 40 m below ground level. Sediment infill is inferred to have formed in situ. The conventional models of pollutant transport in fracture networks assume that they comprise open fractures, with pollutant mobility depending on fracture connectivity (a function of density, length, orientation and intersection) and aperture. The presence of extensive sediment fills in fractures will materially change their permeability, thereby reducing pollutant flux, and be of significance in the assessment of risks arising from chemical spillages. There has been little or no substantive evidence for such fills in Permo-Triassic sandstones in the UK, apart from observations at outcrop and anecdotes of sand being pumped from boreholes. Here, we report surface and rare, but complementary, subsurface observations of extensive fills in the Cheshire basin, and argue that they will only act as preferential pathways where they crosscut low-permeability horizons such as mudstones.

Geologic Sediments↗

PubMed2006

Communication between levels of transcriptional control improves robustness and adaptivity.

Regulation of eukaryotic gene expression depends on groups of related proteins acting at the levels of chromatin organization, transcriptional initiation, RNA processing, and nuclear transport. However, a unified understanding of how these different levels of transcriptional control interact has been lacking. Here, we combine genome-wide protein-DNA binding data from multiple sources to infer the connections between functional groups of regulators in Saccharomyces cerevisiae. Our resulting transcriptional network uncovers novel biological relationships; supporting experiments confirm new associations between actively transcribed genes and Sir2 and Esc1, two proteins normally linked to silencing chromatin. Analysis of the regulatory network also reveals an elegant architecture for transcriptional control. Using communication theory, we show that most protein regulators prefer to form modules within their functional class, whereas essential proteins maintain the sparse connections between different classes. Moreover, we provide evidence that communication between different regulatory groups improves the robustness and adaptivity of the cell.

Gene Expression Regulation, Fungal↗

PubMed2005

Explore biological pathways from noisy array data by directed acyclic Boolean networks.

We consider the structure of directed acyclic Boolean (DAB) networks as a tool for exploring biological pathways. In a DAB network, the basic objects are binary elements and their Boolean duals. A DAB is characterized by two kinds of pairwise relations: similarity and prerequisite. The latter is a partial order relation, namely, the on-status of one element is necessary for the on-status of another element. A DAB network is uniquely determined by the state space of its elements. We arrange samples from the state space of a DAB network in a binary array and introduce a random mechanism of measurement error. Our inference strategy consists of two stages. First, we consider each pair of elements and try to identify their most likely relation. In the meantime, we assign a score, s-p-score, to this relation. Second, we rank the s-p-scores obtained from the first stage. We expect that relations with smaller s-p-scores are more likely to be true, and those with larger s-p-scores are more likely to be false. The key idea is the definition of s-scores (referring to similarity), p-scores (referring to prerequisite), and s-p-scores. As with classical statistical tests, control of false negatives and false positives are our primary concerns. We illustrate the method by a simulated example, the classical arginine biosynthetic pathway, and show some exploratory results on a published microarray expression dataset of yeast Saccharomyces cerevisiae obtained from experiments with activation and genetic perturbation of the pheromone response MAPK pathway.

Algorithms↗

PubMedDate not supplied

3-D components of a biological neural network visualized in computer generated imagery. II. Macular neural network organization.

Computer-assisted reconstructions of small parts of the macular neural network show how the nerve terminals and receptive fields are organized in 3-dimensional space. This biological neural network is anatomically organized for parallel distributed processing of information. Processing appears to be more complex than in computer-based neural networks, because spatiotemporal factors figure into synaptic weighting. Serial reconstruction data show anatomical arrangements which suggest that 1) assemblies of cells analyse and distribute information with inbuilt redundancy, to improve reliability; 2) feedforward/feedback loops provide the capacity for presynaptic modulation of output during processing; 3) constrained randomness in connectivities contributes to adaptability; and 4) local variations in network complexity permit differing analyses of incoming signals to take place simultaneously. The last inference suggests that there may be segregation of information flow to central stations subserving particular functions.

Acoustic Maculae↗

PubMed1991

Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base. II. Evaluation of diagnostic performance.

We have developed a probabilistic reformulation of the Quick Medical Reference (QMR) system. In Part I of this two-part series, we described a two-level, multiply connected belief-network representation of the QMR knowledge base and a simulation algorithm to perform probabilistic inference on the reformulated knowledge base. In Part II of this series, we report on an evaluation of the probabilistic QMR, in which we compare the performance of QMR to that of our probabilistic system on cases abstracted from continuing medical education materials from Scientific American Medicine. In addition, we analyze empirically several components of the probabilistic model and simulation algorithm.

Algorithms↗

PubMed2026

Emergence of two novel HIV-1 Circulating Recombinant Forms (CRF190_0708 and CRF191_0708): molecular characterization and clinical insights from a five-year study in Yunnan, China.

BACKGROUND: To characterize HIV-1 molecular epidemiology and identify novel circulating recombinant forms (CRFs) among antiretroviral therapy (ART)-naïve heterosexuals in Yunnan, China, and evaluate their clinical impact. METHODS: This study examined 636 HIV-1 pol sequences to analyze genetic diversity, pretreatment drug resistance (PDR), and transmission networks. Near full-length genomes were obtained to identify and characterize novel recombinants, with their evolutionary history inferred by Bayesian analysis. Co-receptor tropism was predicted, and the five-year clinical outcomes (including immune reconstitution and virologic response) of patients infected with the novel CRFs were compared. RESULTS: The most prevalent type identified was CRF08_BC, accounting for 50.16% of cases. The prevalence of drug resistance was 5.97% (38/636), with the K103N mutation being the most common. An analysis of transmission networks revealed that 52.2% (272/521) of clusters were associated with CRF07_BC and CRF08_BC. Two novel second-generation CRFs were identified: CRF190_0708, with an estimated time to the most recent common ancestor (tMRCA) of 1998.9, and CRF191_0708, with a more recent tMRCA ranging from 2009.5 to 2011.6. During the five-year follow-up period, viral rebound was observed in 7 patients in the CRF190_0708 group and in 1 patient in the CRF191_0708 group. Drug-resistance mutations (M184V and K103N) were detected in a subset of rebound cases in the CRF190_0708 group. CONCLUSIONS: This study identifies two novel HIV-1 recombinants, CRF190_0708 and CRF191_0708, highlighting ongoing viral evolution in Yunnan. Preliminary findings suggest possible clinical differences, warranting further investigation. Continued molecular surveillance is needed. TRIAL REGISTRATION: The clinical study was registered at ClinicalTrials.gov under the identifier NCT03852849. The date of registration was March 22, 2019.

Adult↗

PubMed2002

BioMiner--modeling, analyzing, and visualizing biochemical pathways and networks.

MOTIVATION: Understanding the biochemistry of a newly sequenced organism is an essential task for post-genomic analysis. Since, however, genome and array data grow much faster than biochemical information, it is necessary to infer reactions by comparative analysis. No integrated and easy to use software tool for this purpose exists as yet. RESULTS: We present a new software system--BioMiner--for analyzing and visualizing biochemical pathways and networks. BioMiner is based on a new comprehensive, extensible and reusable data model--BioCore--which can be used to model biochemical pathways and networks. As a first application we present PathFinder, a new tool predicting biochemical pathways by comparing groups of related organisms based on sequence similarity. We successfully tested PathFinder with a number of experiments, e.g. the well studied glycolysis in bacteria. Additionally, an application called PathViewer for the visualization of metabolic networks is presented. PathViewer is the first application we are aware of which supports the graphical comparison of metabolic networks of different organisms. AVAILABILITY: http://www.zbi.uni-saarland.de/chair/projects/BioMiner SUPPLEMENTARY INFORMATION: Additional information on experimental results can be found on our web site.

Biochemistry↗

PubMed2005

Respiratory motion prediction by using the adaptive neuro fuzzy inference system (ANFIS).

The quality of radiation therapy delivered for treating cancer patients is related to set-up errors and organ motion. Due to the margins needed to ensure adequate target coverage, many breast cancer patients have been shown to develop late side effects such as pneumonitis and cardiac damage. Breathing-adapted radiation therapy offers the potential for precise radiation dose delivery to a moving target and thereby reduces the side effects substantially. However, the basic requirement for breathing-adapted radiation therapy is to track and predict the target as precisely as possible. Recent studies have addressed the problem of organ motion prediction by using different methods including artificial neural network and model based approaches. In this study, we propose to use a hybrid intelligent system called ANFIS (the adaptive neuro fuzzy inference system) for predicting respiratory motion in breast cancer patients. In ANFIS, we combine both the learning capabilities of a neural network and reasoning capabilities of fuzzy logic in order to give enhanced prediction capabilities, as compared to using a single methodology alone. After training ANFIS and checking for prediction accuracy on 11 breast cancer patients, it was found that the RMSE (root-mean-square error) can be reduced to sub-millimetre accuracy over a period of 20 s provided the patient is assisted with coaching. The average RMSE for the un-coached patients was 35% of the respiratory amplitude and for the coached patients 6% of the respiratory amplitude.

Algorithms↗

PubMed2002

Nonlinear dynamics of ice-wedge networks and resulting sensitivity to severe cooling events.

Patterns of subsurface wedges of ice that form along cooling-induced tension fractures, expressed at the ground surface by ridges or troughs spaced 10 30 m apart, are ubiquitous in polar lowlands. Fossilized ice wedges, which are widespread at lower latitudes, have been used to infer the duration and mean temperature of cold periods within Proterozoic and Quaternary climates, and recent climate trends have been inferred from fracture frequency in active ice wedges. Here we present simulations from a numerical model for the evolution of ice-wedge networks over a range of climate scenarios, based on the interactions between thermal tensile stress, fracture and ice wedges. We find that short-lived periods of severe cooling permanently alter the spacing between ice wedges as well as their fracture frequency. This affects the rate at which the widths of ice wedges increase as well as the network's response to subsequent climate change. We conclude that wedge spacing and width in ice-wedge networks mainly reflect infrequent episodes of rapidly falling ground temperatures rather than mean conditions.

Journal Article↗

PubMed1999

Systematic determination of genetic network architecture.

Technologies to measure whole-genome mRNA abundances and methods to organize and display such data are emerging as valuable tools for systems-level exploration of transcriptional regulatory networks. For instance, it has been shown that mRNA data from 118 genes, measured at several time points in the developing hindbrain of mice, can be hierarchically clustered into various patterns (or 'waves') whose members tend to participate in common processes. We have previously shown that hierarchical clustering can group together genes whose cis-regulatory elements are bound by the same proteins in vivo. Hierarchical clustering has also been used to organize genes into hierarchical dendograms on the basis of their expression across multiple growth conditions. The application of Fourier analysis to synchronized yeast mRNA expression data has identified cell-cycle periodic genes, many of which have expected cis-regulatory elements. Here we apply a systematic set of statistical algorithms, based on whole-genome mRNA data, partitional clustering and motif discovery, to identify transcriptional regulatory sub-networks in yeast-without any a priori knowledge of their structure or any assumptions about their dynamics. This approach uncovered new regulons (sets of co-regulated genes) and their putative cis-regulatory elements. We used statistical characterization of known regulons and motifs to derive criteria by which we infer the biological significance of newly discovered regulons and motifs. Our approach holds promise for the rapid elucidation of genetic network architecture in sequenced organisms in which little biology is known.

Animals↗

PubMed1991

Algorithms for Bayesian belief-network precomputation.

Bayesian belief networks provide an intuitive and concise means of representing probabilistic relationships among the variables in expert systems. A major drawback to this methodology is its computational complexity. We present an introduction to belief networks, and describe methods for precomputing, or caching, part of a belief network based on metrics of probability and expected utility. These algorithms are examples of a general method for decreasing expected running time for probabilistic inference. We first present the necessary background, and then present algorithms for producing caches based on metrics of expected probability and expected utility. We show how these algorithms can be applied to a moderately complex belief network, and present directions for future research.

Algorithms↗

PubMed2026

Whole-transcriptome RNA sequencing and ceRNA network analyses provide novel insights into the antibacterial immune response of Hippocampus abdominalis against Vibrio harveyi.

Long non-coding RNAs (lncRNAs) stand as newly-arisen molecular types that exert regulatory effects, able to operate as competitive endogenous RNAs (ceRNAs) to engage microRNAs (miRNAs) in interaction, resulting in the recovery of target mRNA expression and activity. Increasing evidences indicate that the ceRNA network affects various biological processes in mammals, including development, cellular differentiation, metabolism, immune response, and disease pathogenesis. In teleost fish, the lncRNA-miRNA-mRNA regulatory networks have been reported occasionally. However, up to now, the roles of lncRNAs in the big-belly seahorse (Hippocampus abdominalis) remains unclear. In this study, we reported for the first time, via whole-transcriptome RNA sequencing, the lncRNA mediated ceRNA regulatory network in Vibrio harveyi-infected H. abdominalis. A total of 4197 differentially expressed mRNAs (DE-mRNAs), 1317 DE-lncRNAs, and 183 DE-miRNAs were identified. Furthermore, the crosstalk between miRNAs and lncRNAs as well as between miRNAs and mRNAs was inferred based on the negative correlations between miRNAs and their target lncRNAs/mRNAs. A core immune associated lncRNA-miRNA-mRNA putative regulatory network was thus constructed, comprising 211 lncRNA-miRNA and 224 mRNA-miRNA pairs. In conclusion, our findings provide an integrative overview of the ceRNA regulatory networks on the underlying immune responses to V. harveyi infection in the big-belly seahorse, and offer a solid theoretical foundation for the comparative immunological research of teleost fish.

Animals↗

PubMed2006

Spatial genetic pattern in the land mollusc Helix aspersa inferred from a 'centre-based clustering' procedure.

The present work provides the first broad-scale screening of allozymes in the land snail Helix aspersa. By using overall information available on the distribution of genetic variation between 102 populations previously investigated, we expect to strengthen our knowledge on the spread of the invasive aspersa subspecies in the Western Mediterranean. We propose a new approach based on a centre-based clustering procedure to cluster populations into groups following rules of geographical proximity and genetic similarity. Assuming a stepping-stone model of diffusion, we apply a partitioning algorithm which clusters only populations that are geographically contiguous. The algorithm used, which is actually part of leading methods developed for analysing large microarray datasets, is that of the k-means. Its goal is to minimize the within-group variance. The spatial constraint is provided by a list of connections between localities deduced from a Delaunay network. After testing each optimal group for the presence of spatial arrangement in the genetic data, the inferred genetic structure was compared with partitions obtained from other methods published for defining homogeneous groups (i.e. the Monmonier and SAMOVA algorithms). Competing biogeographical scenarios inferred from the k-means procedure were then compared and discussed to shed more light on colonization routes taken by the species.

Animals↗

PubMed2003

Dissecting the transcription networks of a cell using computational genomics.

A great challenge in understanding biological complexity in the post-genome era is to reconstruct the regulatory networks governing the patterns of gene expression. In the past few years, the rapid accumulation of genomic sequence and functional data has led to the development of computational approaches to systematically dissect transcriptional regulatory networks. Effective algorithms have been developed to predict cis-regulatory elements in a genome, to identify the target genes of transcription factors, to infer the conditions under which each transcription factor is either activated or deactivated, and to analyze combinatorial regulation by multiple transcription factors. Genomic approaches have profoundly changed the way biologists investigate transcriptional regulation, and global pictures of the transcription networks for several model organisms are beginning to emerge.

Algorithms↗