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PubMed2006

Mixing properties of growing networks and Simpson's paradox.

The mixing properties of networks are usually inferred by comparing the degree of a node with the average degree of its neighbors. This kind of analysis often leads to incorrect conclusions: Assortative patterns may appear reversed by a mechanism known as Simpson's paradox. We prove this fact by analytical calculations and simulations on three classes of growing networks based on preferential attachment and fitness, where the disassortative behavior observed is a spurious effect. Our results give a crucial contribution to the debate about the origin of disassortative mixing, since networks previously classified as disassortative reveal instead assortative behavior to a careful analysis.

Journal Article↗

PubMed2005

Reverse engineering genetic networks using evolutionary computation.

This paper proposes an improved evolutionary method for constructing the underlying network structure and inferring effective kinetic parameters from the time series data of gene expression using decoupled S-system formalism. We employed Trigonometric Differential Evolution (TDE) as the optimization engine of our algorithm for capturing the dynamics in gene expression data. A more effective fitness function for attaining the sparse structure, which is the hallmark of biological networks, has been applied. Experiments on artificial genetic network show the power of the algorithm in constructing the network structure and predicting the regulatory parameters. The method is used to evaluate interactions between genes in the SOS signaling pathway in Escherichia coli using gene expression data.

Algorithms↗

PubMed2006

Bayesian error analysis model for reconstructing transcriptional regulatory networks.

Transcription regulation is a fundamental biological process, and extensive efforts have been made to dissect its mechanisms through direct biological experiments and regulation modeling based on physical-chemical principles and mathematical formulations. Despite these efforts, transcription regulation is yet not well understood because of its complexity and limitations in biological experiments. Recent advances in high throughput technologies have provided substantial amounts and diverse types of genomic data that reveal valuable information on transcription regulation, including DNA sequence data, protein-DNA binding data, microarray gene expression data, and others. In this article, we propose a Bayesian error analysis model to integrate protein-DNA binding data and gene expression data to reconstruct transcriptional regulatory networks. There are two unique aspects to this proposed model. First, transcription is modeled as a set of biochemical reactions, and a linear system model with clear biological interpretation is developed. Second, measurement errors in both protein-DNA binding data and gene expression data are explicitly considered in a Bayesian hierarchical model framework. Model parameters are inferred through Markov chain Monte Carlo. The usefulness of this approach is demonstrated through its application to infer transcriptional regulatory networks in the yeast cell cycle.

Algorithms↗

PubMed2005

Adaptive prediction of internal target motion using external marker motion: a technical study.

An adaptive prediction approach was developed to infer internal target position by external marker positions. First, a prediction model (or adaptive neural network) is developed to infer target position from its former positions. For both internal target and external marker motion, two networks with the same type are created. Next, a linear model is established to correlate the prediction errors of both neural networks. Based on this, the prediction error of an internal target position can be reconstructed by the linear combination of the prediction errors of the external markers. Finally, the next position of the internal target is estimated by the network and subsequently corrected by the reconstructed prediction error. In a similar way, future positions are inferred as their previous positions are predicted and corrected. This method was examined by clinical data. The results demonstrated that an improvement (10% on average) of correlation between predicted signal and real internal motion was achieved, in comparison with the correlation between external markers and internal target motion. Based on the clinical data (with correlation coefficient 0.75 on average) observed between external marker and internal target motions, a prediction error (23% on average) of internal target position was achieved. The preliminary results indicated that this method is helpful to improve the predictability of internal target motion with the additional information of external marker signals. A consistent correlation between external and internal signals is important for prediction accuracy.

Algorithms↗

PubMed2025

Transmission of extended spectrum β-lactamase-producing Escherichia coli and antimicrobial resistance gene flow across One Health compartments in eastern Africa: a whole-genome sequence analysis from a prospective cohort study.

BACKGROUND: The One Health paradigm considers interdependence of human, animal, and environmental health. However, there is little evidence from high-income countries to support the importance of a One Health approach to addressing spread of antimicrobial resistance (AMR). Given AMR is a global threat, understanding how the close interactions of humans with animals and the environment in low-income settings affect the spread of AMR is important. We aimed to investigate diversity and transmission of extended spectrum β-lactamase (ESBL)-producing Escherichia coli across household-linked One Health compartments using genomic data. METHODS: We sequenced whole genomes of ESBL-producing E coli isolates from humans, animals, and the environment from a prospective, longitudinal cohort study conducted in Malawi (April 29, 2019, to Dec 3, 2020) and Uganda (July 16, 2020, to Aug 6, 2021). In the cohort study, 259 households were enrolled at baseline in Malawi and 92 in Uganda from a mix of urban, peri-urban, and rural areas. Households were followed up at months 1, 3, and 6 in Malawi and at months 1, 2, and 4 in Uganda. Samples collected at each visit included human and animal stool, environmental samples from hand-contact areas, food, and water, and broader environmental samples such as river water. Samples were cultured in buffered peptone water and then ESBL chromogenic agar to isolate ESBL-producing E coli. ESBL-producing E coli isolates underwent whole-genome sequencing. We performed phylogenetic analyses, and in-silico multi-locus sequence typing, characterised AMR determinants and linked genotypes with sample location, ecological source, and other covariates. We performed fine-scale single nucleotide polymorphism (SNP) and network analysis to infer strain and plasmid transmission across ecological compartments. The primary outcome was colonisation with ESBL-producing E coli. Secondary outcomes were genomic clusters and ESBL genomic determinants within and between One Health compartments. FINDINGS: We found high diversity of ESBL-producing E coli, with 170 sequence types and 166 genomic clusters identified from 2344 genomes, including 1814 genomes from Malawi (907 human, 221 animal, and 686 environmental) and 530 genomes from Uganda (380 human, 147 animal, and three environmental). Sequence type (ST)131 dominated in Malawi (209 [11·5%] of 1814 genomes), and ST10 dominated in Uganda (45 [8·5%] of 530 genomes). Common ESBL genes blaCTX-M-15 (1604 [68·4%] of 2344 genomes) and blaCTX-M-27 (336 [14·3%] of 2344 genomes) were carried on a complex network of 55 and 30 different plasmids. This diversity of plasmids presented multiple pathways for dissemination and revealed high force of selection. Phylogenetic analyses revealed common intermixing of isolates between humans, animals, and the environment. SNP transmission analysis revealed ecologically overlapping clusters, suggesting ESBL-producing E coli co-circulation both within and between compartments with frequent spillover events. Applying a five-SNP threshold, we inferred 463 human-environment transmission events, 146 human-animal events, and 142 animal-environment events. INTERPRETATION: Our work suggests that a One Health approach is crucial to addressing AMR in eastern Africa. Improving water, sanitation, and hygiene systems will create a safer environment, reduce spillovers of AMR bacteria between compartments, and eventually reduce AMR reservoirs in the environment and in animals. FUNDING: Medical Research Council, National Institute for Health and Care Research, and Wellcome Trust.

Humans↗

PubMed2006

Review of microarray experimental design strategies for genetical genomics studies.

Genetical genomics approaches provide a powerful tool for studying the genetic mechanisms governing variation in complex traits. By combining information on phenotypic traits, pedigree structure, molecular markers, and gene expression, such studies can be used for estimating heritability of mRNA transcript abundances, for mapping expression quantitative trait loci (eQTL), and for inferring regulatory gene networks. Microarray experiments, however, can be extremely costly and time consuming, which may limit sample sizes and statistical power. Thus it is crucial to optimize experimental designs by carefully choosing the subjects to be assayed, within a selective profiling approach, and by cautiously controlling systematic factors affecting the system. Also, a rigorous strategy should be used for allocating mRNA samples across assay batches, slides, and dye labeling, so that effects of interest are not confounded with nuisance factors. In this presentation, we review some selective profiling strategies for genetical genomics studies, including the selection of individuals for increased genetic dissimilarity and for a higher number of recombination events. Efficient designs for studying epistasis are also discussed, as well as experiments for inferring heritability of transcriptional levels. It is shown that solving an optimal design problem generally requires a numerical implementation and that the optimality criteria should be intimately related to the goals of the experiment, such as the estimation of additive, dominance, and interacting effects, localizing putative eQTL, or inferring genetic and environmental variance components associated with transcriptional abundances.

Animals↗

PubMed1999

Automatic synthesis of synergies for control of reaching--hierarchical clustering.

In this paper we describe a novel method for determining synergies between joint motions in reaching movements by hierarchical clustering. A set of recorded elbow and shoulder trajectories is used in a learning algorithm to determine the relationships between angular velocities at elbow and shoulder joints. The learning algorithm is based on optimal criteria for obtaining the hierarchy of descriptions of movement trajectories. We show that this method finds complex synergism between optimal joint trajectories for a given set of data and angular velocities at the shoulder and elbow joints. Three other machine learning techniques (ML) are used for comparison with our method of hierarchical clustering of trajectories. These MLs are: (1) radial basis functions (RBF), (2) inductive learning (IL), and (3) adaptive-network-based fuzzy inference system (ANFIS). Better error characteristics were obtained using the method of hierarchical clustering in comparison with the other techniques. The advantage of the method of hierarchical clustering with respect to the other MLs is in integrating the spatial and temporal elements of reaching movements. Determination and analysis of spatio-temporal events of movement trajectories is a useful tool in designing control systems for functional electrical stimulation (FES) assisted manipulation.

Algorithms↗

PubMed2005

A review on the integration of artificial intelligence into coastal modeling.

With the development of computing technology, mechanistic models are often employed to simulate processes in coastal environments. However, these predictive tools are inevitably highly specialized, involving certain assumptions and/or limitations, and can be manipulated only by experienced engineers who have a thorough understanding of the underlying theories. This results in significant constraints on their manipulation as well as large gaps in understanding and expectations between the developers and practitioners of a model. The recent advancements in artificial intelligence (AI) technologies are making it possible to integrate machine learning capabilities into numerical modeling systems in order to bridge the gaps and lessen the demands on human experts. The objective of this paper is to review the state-of-the-art in the integration of different AI technologies into coastal modeling. The algorithms and methods studied include knowledge-based systems, genetic algorithms, artificial neural networks, and fuzzy inference systems. More focus is given to knowledge-based systems, which have apparent advantages over the others in allowing more transparent transfers of knowledge in the use of models and in furnishing the intelligent manipulation of calibration parameters. Of course, the other AI methods also have their individual contributions towards accurate and reliable predictions of coastal processes. The integrated model might be very powerful, since the advantages of each technique can be combined.

Artificial Intelligence↗

PubMed2001

Derived fuzzy knowledge model for estimating the depth of anesthesia.

Reliable and noninvasive monitoring of the depth of anesthesia (DOA) is highly desirable. Based on adaptive network-based fuzzy inference system (ANFIS) modeling, a derived fuzzy knowledge model is proposed for quantitatively estimating the DOA and validate it by 30 experiments using 15 dogs undergoing anesthesia with three different anesthetic regimens (propofol, isoflurane, and halothane). By eliciting fuzzy if-then rules, the model provides a way to address the DOA estimation problem by using electroencephalogram-derived parameters. The parameters include two new measures (complexity and regularity) extracted by nonlinear quantitative analyses, as well as spectral entropy. The model demonstrates good performance in discriminating awake and asleep states for three common anesthetic regimens (accuracy 90.3 % for propofol, 92.7 % for isoflurane, and 89.1% for halothane), real-time feasibility, and generalization ability (accuracy 85.9% across the three regimens). The proposed fuzzy knowledge model is a promising candidate as an effective tool for continuous assessment of the DOA.

Algorithms↗

PubMed2026

Endocrine-disrupting chemical-induced gene networks confer coronary heart disease risk revealed by causal inference and single-cell analyses.

BACKGROUND: Endocrine-disrupting chemicals (EDCs) are linked to coronary heart disease (CHD), but underlying mechanisms remain unclear. We aimed to identify EDC-related genes and evaluate their causal roles in CHD. METHODS: We curated EDC-related genes from a compound-gene interaction database and integrated them with CHD genome-wide association study (GWAS) summary statistics and tissue-specific expression quantitative trait loci (eQTL) data. Two-sample Mendelian randomization (MR) and Bayesian colocalization were applied to infer causality. Functional enrichment, single-cell RNA sequencing of human coronary arteries, and EDC-gene networks were further analyzed. RESULTS: After FDR correction, 39 genes were significantly associated with CHD risk via MR. Four genes-ZNF827, FCHO1, IPO9 (protective), and RPL13 (risk-increasing)-showed strong colocalization (PPH4 > 0.9). Pathway and single-cell analyses of coronary artery tissue indicated that vascular and immune pathways mediate these effects. An interaction network highlighted associations between specific EDCs and candidate genes implicated in CHD susceptibility. CONCLUSION: This integrative genomic study provides evidence that EDCs influence CHD susceptibility through distinct gene networks, revealing potential mechanisms and molecular targets for prevention and therapy.

Humans↗

PubMed2026

Integrated Genomic and Proteomic Analysis Reveals T-B Lymphocyte Signatures in the MYCN Driven "Immune Desert" of Specific Neuroblastoma Subtypes.

AIMS: This study aims to systematically dissect how MYCN amplification shapes the immunosuppressive tumor microenvironment (TME) in high-risk neuroblastoma, elucidating key mechanisms underlying immune evasion. METHODS: We performed an integrated multi-omics analysis of bulk RNA-seq (n = 721), single-cell RNA-seq (n = 9), proteomic data (n = 49) and spatial transcriptomics (Visium, with external validation in melanoma). Analyses included unsupervised clustering, cell-cell communication inference, transcriptional regulatory network reconstruction, and spatial proximity assessment to map the immune landscape. RESULTS: A distinct molecular subtype (Class C), defined by MYCN amplification and poor prognosis, exhibited a comprehensive "immune desert" phenotype characterized by low immune scores and minimal leukocyte infiltration. Single-cell analysis confirmed significant depletion of T and B lymphocytes within the Class C TME. Dysregulated transcriptional networks were identified, including upregulation of REL and EOMES in T cells-with EOMES potentially driving exhaustion via regulation of Transient Receptor Potential (TRP) genes, and REL inhibition enhancing cytotoxic function in vitro. A unique immunosuppressive B-cell subset (B7) engaged in enhanced crosstalk with exhausted T cells and harbored a MYC-centered network linked to cell cycle dysregulation and poor survival. Spatial transcriptomics revealed significant proximity between B7-active regions and Treg/exhaustion-enriched areas, externally validated in melanoma. Proteomic data validated elevated REL expression in MYCN-amplified tumors. CONCLUSION: This work delineates the immunosuppressive architecture of MYCN-driven neuroblastoma, revealing novel regulatory nodes within specific lymphocyte compartments. Integrating single-cell, spatial, and proteomic evidence, we propose REL inhibition as a therapeutic candidate, the EOMES/TRP axis as a bioinformatically supported hypothesis, and the B7/MYC hub as a hypothesis supported by transcriptomic and spatial evidence.

Humans↗

PubMed1995

Nonlinear autoregressive analysis of the 3/s ictal electroencephalogram: implications for underlying dynamics.

In a previous study, nonlinear autoregressive (NLAR) models applied to ictal electroencephalogram (EEG) recordings in six patients revealed nonlinear signal interactions that correlated with seizure type and clinical diagnosis. Here we interpret these models from a theoretical viewpoint. Extended models with multiple nonlinear terms are employed to demonstrate the independence of nonlinear dynamical interactions identified in the 'NLAR fingerprint' of patients with 3/s seizure discharges. Analysis of the role of periodicity in the EEG signal reveals that the fingerprints reflect the dynamics not only of the periodic discharge itself, but also of the fluctuations of each cycle about an average waveform. A stability analysis is used to make qualitative inferences concerning the network properties of the ictal generators. Finally, the NLAR fingerprint is analyzed in the context of Volterra-Weiner theory.

Electroencephalography↗

PubMed2006

A review on integration of artificial intelligence into water quality modelling.

With the development of computing technology, numerical models are often employed to simulate flow and water quality processes in coastal environments. However, the emphasis has conventionally been placed on algorithmic procedures to solve specific problems. These numerical models, being insufficiently user-friendly, lack knowledge transfers in model interpretation. This results in significant constraints on model uses and large gaps between model developers and practitioners. It is a difficult task for novice application users to select an appropriate numerical model. It is desirable to incorporate the existing heuristic knowledge about model manipulation and to furnish intelligent manipulation of calibration parameters. The advancement in artificial intelligence (AI) during the past decade rendered it possible to integrate the technologies into numerical modelling systems in order to bridge the gaps. The objective of this paper is to review the current state-of-the-art of the integration of AI into water quality modelling. Algorithms and methods studied include knowledge-based system, genetic algorithm, artificial neural network, and fuzzy inference system. These techniques can contribute to the integrated model in different aspects and may not be mutually exclusive to one another. Some future directions for further development and their potentials are explored and presented.

Algorithms↗

PubMedDate not supplied

The neural basis of conditional reasoning with arbitrary content.

Behavioral predictions about reasoning have usually contrasted two accounts, Mental Logic and Mental Models. Neuroimaging techniques have been providing new measures that transcend this debate. We tested a hypothesis from Goel and Dolan (2003) that predicts neural activity predominantly in a left parietal-frontal system when participants reason with arbitrary (non-meaningful) materials. In an event-related fMRI investigation, we employed propositional syllogisms, the majority of which involved conditional reasoning. While investigating conditional reasoning generally, we ultimately focused on the neural activity linked to the two valid conditional forms--Modus Ponens (If p then q; p//q) and Modus Tollens (If p then q; not-q//not-p). Consistent with Goel and Dolan (2003), we found a left lateralized parietal frontal network for both inference forms with increasing activation when reasoning becomes more challenging by way of Modus Tollens. These findings show that the previous findings with more complex Aristotlean syllogisms are robust and cast doubt upon accounts of reasoning that accord primary inferential processes uniquely to either the right hemisphere or to language areas.

Adult↗

PubMed2004

Identifying interventional and pathogenic mechanisms by generative inverse modeling of gene expression profiles.

MOTIVATION: The characterization of genetic mechanisms underlying normal cellular function, cancer development, pathogenesis, and the effect of drug treatment is one of the most challenging topics for cancer research and molecular biology. Existing methods for inferring genetic regulatory networks from genome-wide expression profiles provide important information about gene interactions and regulatory relationships. However, these methods do not provide information about the impact of possible interventions or changes on such regulatory networks to study cause-effect relationships at a systems-biology level. RESULTS: We present a data-driven method called generative inverse modeling, which simulates the effect of local genetic changes on the global cellular state, as reflected by an altered genome-wide expression profile. For each genetic change we define a pathogenic score by calculating to what extent it transforms the simulated expression patterns into patterns measured for pathologically altered tissues. The method can be used to estimate the relevance of genes for disease-specific genetic mechanisms, e.g., as presented here for pathogenesis. Generative inverse modeling is based on a Bayesian probability density estimation from a set of measured gene-expression patterns.

Computational Biology↗

PubMed2004

hp-DPI: Helicobacter pylori database of protein interactomes--embracing experimental and inferred interactions.

We implemented a statistical model into our protein interaction database for validation of two-hybrid assays of Helicobacter pylori, and prediction of putative protein interactions not yet discovered experimentally. To present the enormous amount of experimental and inferred protein interaction networking maps, the H.pylori Database of Protein Interactomes (hp-DPI) is developed with a succinct yet comprehensive visualization tool integrated with annotation from Genbank, GO, and KEGG. hp-DPI is first built with, but not limited to, H.pylori protein interactions and is expected to naturally include other organisms' protein interacting relationships in the future.

Algorithms↗

PubMed2005

Statistical evaluation of pairwise protein sequence comparison with the Bayesian bootstrap.

MOTIVATION: Protein sequence comparison methods are routinely used to infer the intricate network of evolutionary relationships found within the rapidly growing library of protein sequences, and thereby to predict the structure and function of uncharacterized proteins. In the present study, we detail an improved statistical benchmark of pairwise protein sequence comparison algorithms. We use bootstrap resampling techniques to determine standard statistical errors and to estimate the confidence of our conclusions. We show that the underlying structure within benchmark databases causes Efron's standard, non-parametric bootstrap to be biased. Consequently, the standard bootstrap underpredicts average performance when used in the context of evaluating sequence comparison methods. We have developed, as an alternative, an unbiased statistical evaluation based on the Bayesian bootstrap, a resampling method operationally similar to the standard bootstrap. RESULTS: We apply our analysis to the comparative study of amino acid substitution matrix families and find that using modern matrices results in a small, but statistically significant improvement in remote homology detection compared with the classic PAM and BLOSUM matrices. AVAILABILITY: The sequence sets and code for performing these analyses are available from http://compbio.berkeley.edu/. CONTACT: brenner@compbio.berkeley.edu.

Algorithms↗

PubMed2026

Decoding gene regulation in plant genomes with artificial intelligence.

One of the central goals of plant functional genomics is to uncover regulatory mechanisms that shape agriculturally important traits to inform crop improvement. Recent advances in machine learning (ML) and artificial intelligence (AI), especially Large Language Models (LLMs), have greatly transformed our ability to derive regulatory information from complex genomics data. This review starts with a brief introduction of recent advances in AI and ML. We then present a plant-focused synthesis of emerging applications of AI- and LLM tools to: (i) predict epigenomic features, regulatory DNA elements, and gene expressions; (ii) infer gene regulatory network; and (iii) estimate post-transcriptional regulation.

Artificial intelligence↗