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PubMed2026

Disagreement-informed arbitration for gene regulatory network inference: A score-level meta-classifier and a diagnostic typology of inter-method conflict.

Gene regulatory network inference methods routinely disagree about individual edges, and practitioners resolve those conflicts by choosing one method or averaging them all. We ask whether the conflict can instead be arbitrated per edge. A gradient-boosted classifier is trained on the raw scores that ten inference methods-correlation-based, information-theoretic, sparse-regression and tree-ensemble, including GENIE3, GRNBoost2, CLR and ARACNe-assign to each candidate regulator-target pair, so that the weight given to each method varies from edge to edge. Across six single-cell perturbation screens spanning four cell types, arbitration improves on mean ensembling by +0.056 AUROC on Adamson and +0.083 on Shifrut under target-grouped cross-validation. The evaluation protocol turns out to matter more than the model. Edge-level cross-validation, standard in this literature, inflates apparent gains by 0.060 AUROC through target-gene leakage-comparable to the entire honest improvement. The effect is far larger for methods that represent genes implicitly: a supervised graph-attention link predictor trained on identical folds scores AUROC 0.930 under edge-level cross-validation, better than anything else we evaluate, and 0.533 once target genes are held out. Any method that parameterises genes is exposed, which covers most graph- and embedding-based approaches. A five-category typology of inter-method conflict localises where arbitration pays off, with the largest gains on edges where the methods disagree and the smallest where they already agree, while adding nothing as model input; we therefore report it as a diagnostic instrument rather than a modelling contribution. We also characterise what the ground truth measures: most perturbed genes in widely used screens are not transcription factors, and a mediation screen bounds how much of the perturbation response can be direct.

Ensemble methods↗
PubMed2005

Logical networks inferred from highly specific discovery of transcriptionally regulated genes predict protein states in cultured gliomas.

Cultured glioma cells are motile and invasive. The phenotype of tumor cell motility is likely created by a complex system of molecular interactions because it requires the orchestration of molecular and physical events that modify the cytoskeleton, cell membrane, extracellular matrix, and signaling. Recent reports have described an algorithm for microarray data analysis that generates highly specific genome-scale discovery; these methods identify states of differential gene expression that are true to a high degree of certainty. Here, highly specific discovery of transcriptionally regulated genes combined with logical networks inferred from the functions of known genes predicts states of protein activation, which are validated in cultured glioma cells by independent laboratories. Highly specific discovery of transcriptionally regulated genes facilitates functional genomics of complex molecular systems.

Actins↗
PubMed2025

Deep learning-based cell-specific gene regulatory networks inferred from single-cell multiome data.

Gene regulatory networks (GRNs) provide a global representation of how genetic/genomic information is transferred in living systems and are a key component in understanding genome regulation. Single-cell multiome data provide unprecedented opportunities to reconstruct GRNs at fine-grained resolution. However, the inference of GRNs is hindered by insufficient single omic profiles due to the characteristic high loss rate of single-cell sequencing data. In this study, we developed scMultiomeGRN, a deep learning framework to infer transcription factor (TF) regulatory networks via unique integration of single-cell genomic (single-cell RNA sequencing) and epigenomic (single-cell ATAC sequencing) data. We create scMultiomeGRN to elucidate these networks by conceptualizing TF network graph structures. Specifically, we build modality-specific neighbor aggregators and cross-modal attention modules to learn latent representations of TFs from single-cell multi-omics. We demonstrate that scMultiomeGRN outperforms state-of-the-art models on multiple benchmark datasets involved in diseases and health. Via scMultiomeGRN, we identified Alzheimer's disease-relevant regulatory network of SPI1 and RUNX1 for microglia. In summary, scMultiomeGRN offers a deep learning framework to identify cell type-specific gene regulatory network from single-cell multiome data.

Deep Learning↗
PubMed2005

Inferring network interactions within a cell.

The continuing growth in high-throughput data acquisition has led to a proliferation of network models to represent and analyse biological systems. These networks involve distinct interaction types detected by a combination of methods, ranging from directly observed physical interactions based in biochemistry to interactions inferred from phenotype measurements, genomic expression and comparative genomics. The discovery of interactions increasingly requires a blend of experimental and computational methods. Considering yeast as a model system, recent analytical methods are reviewed here and specific aims are proposed to improve network interaction inference and facilitate predictive biological modelling.

Algorithms↗
PubMed2000

A gene network inference method from continuous-value gene expression data of wild-type and mutants.

In this paper we introduce a new inference method of a gene regulatory network from steady-state gene expression data. Our method determines a regulatory structure consistent with an observed set of steady-state expression profiles, each generated from wild-type and single deletion mutant of the target network. Our method derives the regulatory relationships in the network using a graph theoretic approach. The advantage of our method is to be able to deal with continuous values of steady-state data, while most of the methods proposed in past use a Boolean network model with binary data. Performance of our method is evaluated on simulated networks with varying the size of networks, indegree of each gene, and the data characteristics (continuous-value/binary), and is compared with that of predictor method proposed by Ideker et al. As a result, we show the superiority of using continuous values to binary values, and the performance of our method is much better than that of the predictor method.

Animals↗
PubMed2005

Inferring network mechanisms: the Drosophila melanogaster protein interaction network.

Naturally occurring networks exhibit quantitative features revealing underlying growth mechanisms. Numerous network mechanisms have recently been proposed to reproduce specific properties such as degree distributions or clustering coefficients. We present a method for inferring the mechanism most accurately capturing a given network topology, exploiting discriminative tools from machine learning. The Drosophila melanogaster protein network is confidently and robustly (to noise and training data subsampling) classified as a duplication-mutation-complementation network over preferential attachment, small-world, and a duplication-mutation mechanism without complementation. Systematic classification, rather than statistical study of specific properties, provides a discriminative approach to understand the design of complex networks.

Algorithms↗
PubMed2000

Genetic network inference: from co-expression clustering to reverse engineering.

MOTIVATION: Advances in molecular biological, analytical and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. In particular, using high-throughput gene expression assays, we are able to measure the output of the gene regulatory network. We aim here to review datamining and modeling approaches for conceptualizing and unraveling the functional relationships implicit in these datasets. Clustering of co-expression profiles allows us to infer shared regulatory inputs and functional pathways. We discuss various aspects of clustering, ranging from distance measures to clustering algorithms and multiple-cluster memberships. More advanced analysis aims to infer causal connections between genes directly, i.e. who is regulating whom and how. We discuss several approaches to the problem of reverse engineering of genetic networks, from discrete Boolean networks, to continuous linear and non-linear models. We conclude that the combination of predictive modeling with systematic experimental verification will be required to gain a deeper insight into living organisms, therapeutic targeting and bioengineering.

Animals↗
PubMed2005

Inferring network activity from synaptic noise.

During intense network activity in vivo, cortical neurons are in a high-conductance state, in which the membrane potential (V(m)) is subject to a tremendous fluctuating activity. Clearly, this "synaptic noise" contains information about the activity of the network, but there are presently no methods available to extract this information. We focus here on this problem from a computational neuroscience perspective, with the aim of drawing methods to analyze experimental data. We start from models of cortical neurons, in which high-conductance states stem from the random release of thousands of excitatory and inhibitory synapses. This highly complex system can be simplified by using global synaptic conductances described by effective stochastic processes. The advantage of this approach is that one can derive analytically a number of properties from the statistics of resulting V(m) fluctuations. For example, the global excitatory and inhibitory conductances can be extracted from synaptic noise, and can be related to the mean activity of presynaptic neurons. We show here that extracting the variances of excitatory and inhibitory synaptic conductances can provide estimates of the mean temporal correlation-or level of synchrony-among thousands of neurons in the network. Thus, "probing the network" through intracellular V(m) activity is possible and constitutes a promising approach, but it will require a continuous effort combining theory, computational models and intracellular physiology.

Animals↗
PubMed2006

Gene annotation and network inference by phylogenetic profiling.

BACKGROUND: Phylogenetic analysis is emerging as one of the most informative computational methods for the annotation of genes and identification of evolutionary modules of functionally related genes. The effectiveness with which phylogenetic profiles can be utilized to assign genes to pathways depends on an appropriate measure of correlation between gene profiles, and an effective decision rule to use the correlate. Current methods, though useful, perform at a level well below what is possible, largely because performance of the latter deteriorates rapidly as coverage increases. RESULTS: We introduce, test and apply a new decision rule, correlation enrichment (CE), for assigning genes to functional categories at various levels of resolution. Among the results are: (1) CE performs better than standard guilt by association (SGA, assignment to a functional category when a simple correlate exceeds a pre-specified threshold) irrespective of the number of genes assigned (i.e. coverage); improvement is greatest at high coverage where precision (positive predictive value) of CE is approximately 6-fold higher than that of SGA. (2) CE is estimated to allocate each of the 2918 unannotated orthologs to KEGG pathways with an average precision of 49% (approximately 7-fold higher than SGA) (3) An estimated 94% of the 1846 unannotated orthologs in the COG ontology can be assigned a function with an average precision of 0.4 or greater. (4) Dozens of functional and evolutionarily conserved cliques or quasi-cliques can be identified, many having previously unannotated genes. CONCLUSION: The method serves as a general computational tool for annotating large numbers of unknown genes, uncovering evolutionary and functional modules. It appears to perform substantially better than extant stand alone high throughout methods.

Algorithms↗
PubMed2004

An integrated comprehensive workbench for inferring genetic networks: voyagene.

We propose an integrated, comprehensive network-inferring system for genetic interactions, named VoyaGene, which can analyze experimentally observed expression profiles by using and combining the following five independent inferring models: Clustering, Threshold-Test, Bayesian, multi-level digraph and S-system models. Since VoyaGene also has effective tools for visualizing the inferred results, researchers may evaluate the combination of appropriate inferring models, and can construct a genetic network to an accuracy that is beyond the reach of a single inferring model. Through the use of VoyaGene, the present study demonstrates the effectiveness of combining different inferring models.

Algorithms↗
PubMed2025

Beyond Level-1: Identifiability of a Class of Galled Tree-Child Networks.

Inference of phylogenetic networks is of increasing interest in the genomic era. However, the extent to which phylogenetic networks are identifiable from various types of data remains poorly understood, despite its crucial role in justifying methods. This work obtains strong identifiability results for large sub-classes of galled tree-child semidirected networks. Some of the conditions our proofs require, such as the identifiability of a network's tree of blobs or the circular order of 4 taxa around a cycle in a level-1 network, are already known to hold for many data types. We show that all these conditions hold for quartet concordance factor data under various gene tree models, yielding the strongest results from 2 or more samples per taxon. Although the network classes we consider have topological restrictions, they include non-planar networks of any level and are substantially more general than level-1 networks - the only class previously known to enjoy identifiability from many data types. Our work establishes a route for proving future identifiability results for tree-child galled networks from data types other than quartet concordance factors, by checking that explicit conditions are met.

Mathematical Concepts↗
PubMed2004

Computational inference of transcriptional regulatory networks from expression profiling and transcription factor binding site identification.

We have developed a computational method for transcriptional regulatory network inference, CARRIE (Computational Ascertainment of Regu latory Relationships Inferred from Expression), which combines microarray and promoter sequence analysis. CARRIE uses sources of data to identify the transcription factors (TFs) that regulate gene expression changes in response to a stimulus and generates testable hypotheses about the regulatory network connecting these TFs to the genes they regulate. The promoter analysis component of CARRIE, ROVER (Relative OVER-abundance of cis-elements), is highly accurate at detecting the TFs that regulate the response to a stimulus. ROVER also predicts which genes are regulated by each of these TFs. CARRIE uses these transcriptional interactions to infer a regulatory network. To demonstrate our method, we applied CARRIE to six sets of publicly available DNA microarray experiments on Saccharomyces cerevisiae. The predicted networks were validated with comparisons to literature sources, experimental TF binding data, and gene ontology biological process information.

Binding Sites↗
PubMed2005

Inferring gene networks from steady-state response to single-gene perturbations.

Inferring gene networks from gene expression data is an important step in understanding the molecular machinery of life. Three methods for establishing and quantifying causal relationships between genes based on steady-state measurements in single-gene perturbation experiments have recently been proposed: the regulatory strength method, the local regulatory strength method, and Gardner's method. The theoretical basis of these methods is presented here in a thorough and consistent fashion. In principle, for the same data set all three methods would generate identical networks, but they would quantify the strengths of connections in different ways. The regulatory strength method is shown here to be topology-dependent. It adopts the format of the data collected in gene expression microarray experiments and therefore can be immediately used with this technology. The regulatory strengths obtained by this method can also be used to compute local regulatory strengths. In contrast, Gardner's method requires both measurements of mRNA concentrations and measurements of the applied rate perturbations, which is not usually part of a standard microarray experimental protocol. The results generated by Gardner's method and by the two regulatory strengths methods differ only by scaling constants, but Gardner's method requires more measurements. On the other hand, the explicit use of rate perturbations in Gardner's approach allows one to address new questions with this method, like what perturbations caused given responses of the system. Results of the application of the three techniques to real experimental data are presented and discussed. The comparative analysis presented in this paper can be helpful for identifying an appropriate technique for inferring genetic networks and for interpreting the results of its application to experimental data.

Animals↗
PubMed2006

Computer inference of network of ecological interactions from sampling data.

Both direct and indirect ecological interactions may occur in an ecosystem with large numbers of taxa. Traditional food web technique is a popular tool to measure the quality and health of the environment. Much of works must be done before constructing a food web for an ecosystem especially with many taxa. This food web is generally specific for some ecological interactions and fixed for a set of given species. It is therefore not an effective method for dynamic and prompt assessment of environment. Ecological interactions and their interactive intensity may be detected by sampling biological taxa in the field and by detecting various between-taxa distances or similarities. Network may clearly exhibit the complex interactions among biological taxa. Statistic tests on various distance or similarity measures and computer designs are required to infer the {network. We develop an algorithm and software to infer the network of direct or indirect ecological interactions in ecosystem. It is a prompt and effective tool in monitoring and assessment of the environment. A redundant network may be inferred and drawn by computer based on the statistic tests on sampling data or the pathway information given in HTML file. Dominant taxa may be found in the network. In total of 16 distance and similarity measures, including Euclidean distance, Manhattan distance, Pearson correlation, partial correlation, point correlation, linkage coefficients, Jaccard coefficient etc., are provided to detect taxa pairs with significant parametric or nonparametric similarities, based on randomization tests and ordinary statistic tests. Criteria to use distance and similarity measures are discussed.

Algorithms↗
PubMed2000

Inferring genetic networks from DNA microarray data by multiple regression analysis.

Inferring gene regulatory networks by differential equations from the time series data of a DNA microarray is one of the most challenging tasks in the post-genomic era. However, there have been no studies actually inferring gene regulatory networks by differential equations from genome-level data. The reason for this is that the number of parameters in the equations exceeds the number of measured time points. We here succeeded in executing the inference, not by directly determining parameters but by applying multiple regression analysis to our equations. We derived our differential equations and steady state equations from the rate equations of transcriptional reactions in an organism. Verification with a number of genes related to respiration indicated the validity and effectiveness of our method. Moreover, the steady state equations were more appropriate than the differential equations for the microarray data used.

Computational Biology↗
PubMed2003

Inferring gene networks from time series microarray data using dynamic Bayesian networks.

Dynamic Bayesian networks (DBNs) are considered as a promising model for inferring gene networks from time series microarray data. DBNs have overtaken Bayesian networks (BNs) as DBNs can construct cyclic regulations using time delay information. In this paper, a general framework for DBN modelling is outlined. Both discrete and continuous DBN models are constructed systematically and criteria for learning network structures are introduced from a Bayesian statistical viewpoint. This paper reviews the applications of DBNs over the past years. Real data applications for Saccharomyces cerevisiae time series gene expression data are also shown.

Algorithms↗
PubMed2005

HomoMINT: an inferred human network based on orthology mapping of protein interactions discovered in model organisms.

BACKGROUND: The application of high throughput approaches to the identification of protein interactions has offered for the first time a glimpse of the global interactome of some model organisms. Until now, however, such genome-wide approaches have not been applied to the human proteome. RESULTS: In order to fill this gap we have assembled an inferred human protein interaction network where interactions discovered in model organisms are mapped onto the corresponding human orthologs. In addition to a stringent assignment to orthology classes based on the InParanoid algorithm, we have implemented a string matching algorithm to filter out orthology assignments of proteins whose global domain organization is not conserved. Finally, we have assessed the accuracy of our own, and related, inferred networks by benchmarking them against i) an assembled experimental interactome, ii) a network derived by mining of the scientific literature and iii) by measuring the enrichment of interacting protein pairs sharing common Gene Ontology annotation. CONCLUSION: The resulting networks are named HomoMINT and HomoMINT_filtered, the latter being based on the orthology table filtered by the domain architecture matching algorithm. They contains 9749 and 5203 interactions respectively and can be analyzed and viewed in the context of the experimentally verified interactions between human proteins stored in the MINT database. HomoMINT is constantly updated to take into account the growing information in the MINT database.

Algorithms↗