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PubMed2026

CoxFormer enables spatial omics inference with multimodal generative modeling.

Gene co-expression maps transcriptome-wide gene-gene relationships, yet high-quality estimates cover less than half the genome. Meanwhile, spatial omics either profiles restricted in situ panels or lacks cellular resolution. Extending co-expression transcriptome-wide could overcome these limitations by inferring unassayed gene expression at subcellular resolution. Here we show that CoxFormer integrates literature-derived gene knowledge with co-expression networks from bulk tissues and large-scale single-cell atlases to learn 512-dimensional representations for 32,016 human genes. These embeddings capture functional gene relationships and serve as a generative prior for spatial inference across platforms and modalities. Without requiring a matched single-cell RNA-sequencing reference, CoxFormer supports four applications beyond measured genes: histology-based expression imputation, gene activity prediction from chromatin accessibility, subcellular super-resolution inference, and pathological region detection. Together, CoxFormer extends gene embedding from gene- and cell-level tasks to whole-transcriptome spatial inference, providing a unified framework for biological analysis beyond the limited gene coverage of current spatial omics technologies.

Humans↗

PubMed2000

Movement and mind: a functional imaging study of perception and interpretation of complex intentional movement patterns.

We report a functional neuroimaging study with positron emission tomography (PET) in which six healthy adult volunteers were scanned while watching silent computer-presented animations. The characters in the animations were simple geometrical shapes whose movement patterns selectively evoked mental state attribution or simple action description. Results showed increased activation in association with mental state attribution in four main regions: medial prefrontal cortex, temporoparietal junction (superior temporal sulcus), basal temporal regions (fusiform gyrus and temporal poles adjacent to the amygdala), and extrastriate cortex (occipital gyrus). Previous imaging studies have implicated these regions in self-monitoring, in the perception of biological motion, and in the attribution of mental states using verbal stimuli or visual depictions of the human form. We suggest that these regions form a network for processing information about intentions, and speculate that the ability to make inferences about other people's mental states evolved from the ability to make inferences about other creatures' actions.

Adult↗

PubMed2002

Abnormal functional connectivity in posttraumatic stress disorder.

This study investigated the efficacy of a combined multivariate/resampling procedure for the analysis of PET activation studies. The covariance-based multivariate analysis was used to investigate distributed brain systems in posttraumatic stress disorder (PTSD) patients and matched controls during performance of a working memory task. The results were compared to univariate results obtained in an earlier study. We also examined whether the PTSD patients demonstrated a breakdown in functional connectivity that may be associated with working memory difficulties often experienced by these patients. A resampling procedure was used specifically to test the reliability of measured between-group effects, to avoid mistaken inference on the basis of random intersubject differences. Significant and reproducible differences in network connectivity were obtained for the two groups. The functional connectivity pattern of the patient group was characterized by relatively more activation in the bilateral inferior parietal lobes and the left precentral gyrus than the control group, and less activation in the inferior medial frontal lobe, bilateral middle frontal gyri and right inferior temporal gyrus. The resampling procedure provided direct evidence that working memory updating was abnormal in PTSD patients relative to matched controls. This work focuses on the need to identify extended brain networks (in addition to regionally specific changes) for the full characterization of brain responses in neuroimaging experiments. Our multivariate analysis explicitly measures the reliability of the patterns of functional connectivity we obtain and demonstrates the potential of such analyses for the study of brain network dysfunction in psychopathology.

Adult↗

PubMed2003

Phylogeography of Cavernularia hultenii: evidence of slow genetic drift in a widely disjunct lichen.

Population structure and history is poorly known in most lichenized ascomycetes. Many species display large-scale infraspecific disjunctions, which have been explained alternately by range fragmentation in species of high age and widespread long-distance dispersal. Using the lichen Cavernularia hultenii, which is widely disjunct across North America and Europe, Pleistocene and Holocene population history was inferred. The internal transcribed spacer (ITS) and part of the the intergenic spacer (IGS) region of the nuclear ribosomal DNA were sequenced in 300 individuals representing 62 populations across the range of the species. While four ancestral haplotypes are found in all areas, none of the observed tip haplotypes is present in more than one of the three part ranges. Although this is evidence for a past fragmentation event, nested clade analysis (NCA) remains equivocal in the choice between allopatric fragmentation and long-distance dispersal. Mismatch distributions indicate exponential population growth, probably during postglacial invasion of C. hultenii into formerly glaciated areas of western North America. The presence of one southern and at least one northern glacial refugium in South Central Alaska is inferred. Evidence for another refugium in the Queen Charlotte Islands or Alexander Archipelago is inconclusive because of sparse sampling. However, a range expansion was not confirmed unambiguously by NCA. The limited power of NCA to infer past range fragmentations and expansions is due apparently to the shallow haplotype network and widespread ancestral haplotypes. This can be explained by slow genetic drift causing incomplete removal of ancestral haplotypes from the postfragmentation and postexpansion areas.

Base Sequence↗

PubMed2026

Phylogenomics reveals persistent gene-tree discordance in the Chenopodium album aggregate.

BACKGROUND AND AIMS: Complex genomic histories shaped by hybridisation and polyploidy can influence traits related to plant defence, stress tolerance and toxicity, particularly in Amaranthaceae, which includes crops such as quinoa and spinach. Within this family, white goosefoot (Chenopodium album), a widespread agricultural weed and traditional food resource, belongs to a diploid-polyploid aggregate with extensive phylogenetic discordance. Clarifying its evolutionary history provides context for interpreting ecologically and agronomically relevant trait variation across the aggregate. Building on the established genome-lineage framework, we tested whether discordance persists when constituent genome-lineage components are represented separately and whether the remaining signal is compatible with reticulate evolution. METHODS: We analysed 2,298 conserved nuclear BUSCO families across 27 assembly-level terminals using tree- and network-based approaches. Genome-lineage-aware analyses used 2,156 families after separating polyploid Chenopodium into A-H components, with Dysphania ambrosioides as outgroup. HyDe tested site-pattern asymmetry under global false-discovery-rate correction. KEY RESULTS: Assembly-level analyses grouped the Danish C. album aggregate accession Ca6-1 with hexaploid C. album sensu stricto dcCheAlbu1.1, whereas relationships among surrounding Chenopodium taxa were less stable. Genome-lineage-aware analyses recovered the expected B-, C- and D-affinity relationships, but substantial gene-family heterogeneity persisted. Reticulate network models fitted the assembly-level data better than bifurcating models, although inferred patterns differed between methods. HyDe detected significant site-pattern asymmetry in a small subset of loci, with most retained signal shared between the focal assemblies. CONCLUSIONS: Gene-tree discordance persists in the C. album aggregate after genome-lineage separation. The established genome-lineage framework captures the dominant phylogenomic structure, while residual heterogeneity is compatible with both tree-like and reticulate processes without identifying direct progenitors or a unique hybridisation history. This framework supports future analyses of lineage-specific and trait-associated loci related to plant defence, food quality and toxicity in C. album and related Amaranthaceae.

Chenopodium album↗

PubMed2006

A probabilistic dynamical model for quantitative inference of the regulatory mechanism of transcription.

MOTIVATION: Quantitative estimation of the regulatory relationship between transcription factors and genes is a fundamental stepping stone when trying to develop models of cellular processes. This task, however, is difficult for a number of reasons: transcription factors' expression levels are often low and noisy, and many transcription factors are post-transcriptionally regulated. It is therefore useful to infer the activity of the transcription factors from the expression levels of their target genes. RESULTS: We introduce a novel probabilistic model to infer transcription factor activities from microarray data when the structure of the regulatory network is known. The model is based on regression, retaining the computational efficiency to allow genome-wide investigation, but is rendered more flexible by sampling regression coefficients independently for each gene. This allows us to determine the strength with which a transcription factor regulates each of its target genes, therefore providing a quantitative description of the transcriptional regulatory network. The probabilistic nature of the model also means that we can associate credibility intervals to our estimates of the activities. We demonstrate our model on two yeast datasets. In both cases the network structure was obtained using chromatin immunoprecipitation data. We show how predictions from our model are consistent with the underlying biology and offer novel quantitative insights into the regulatory structure of the yeast cell. AVAILABILITY: MATLAB code is available from http://umber.sbs.man.ac.uk/resources/puma.

Computer Simulation↗

PubMed2003

Comparative quantification of health risks conceptual framework and methodological issues.

Reliable and comparable analysis of risks to health is key for preventing disease and injury. Causal attribution of morbidity and mortality to risk factors has traditionally been conducted in the context of methodological traditions of individual risk factors, often in a limited number of settings, restricting comparability.In this paper, we discuss the conceptual and methodological issues for quantifying the population health effects of individual or groups of risk factors in various levels of causality using knowledge from different scientific disciplines. The issues include: comparing the burden of disease due to the observed exposure distribution in a population with the burden from a hypothetical distribution or series of distributions, rather than a single reference level such as non-exposed; considering the multiple stages in the causal network of interactions among risk factor(s) and disease outcome to allow making inferences about some combinations of risk factors for which epidemiological studies have not been conducted, including the joint effects of multiple risk factors; calculating the health loss due to risk factor(s) as a time-indexed "stream" of disease burden due to a time-indexed "stream" of exposure, including consideration of discounting; and the sources of uncertainty.

Journal Article↗

PubMed2004

Cross-species comparison of genome-wide expression patterns.

The rapid accumulation of microarray data from multiple species provides unprecedented opportunities to study the evolution of biological systems. Recent studies have used cross-species comparisons of expression profiles to annotate gene functions, to draw evolutionary inferences concerning specific biological processes and to study the global properties of expression networks.

Animals↗

PubMed2004

Integrating databases and expert systems for the analysis of brain structures: connections, similarities, and homologies.

The NeuroHomology Database system (NHDB) combines databases related to brain structures from different species with different knowledge management systems (KMSs) for systematization, evaluation and processing neurobiological data. Special attention is assessment of similarity of data from different species as a basis for exploring neural homologies. NHDB includes modules that handle brain structure and connectivity data, as well as inference engines for evaluation of the stored neurobiological information. The spatial inference engine evaluates the possible topological relations between cortical structures in different neuroanatomical atlases. The connectivity inference engine evaluates the reliability of information pertaining to fiber tracts as those are reflected in the literature. The inference engine for translation of neuroanatomical connections in different atlases evaluates the probability of existence of connections of interest in different parcellation schemes. Finally, the similarity inference engine calculates the overall degree of similarity of pairs of brain structures from different species by taking into account a set of eight criteria. We present examples of search for information in NHDB system, inferences of relations between cortical structures from equivalent neuroanatomical atlases, reconstruction of functional networks of brain structures from data collated from the literature, translation of connectivity matrices in equivalent parcellation schemes, and evaluations of similarities of brain structures from humans, macaques and rats.

Algorithms↗

PubMed2003

Binarization of microarray data on the basis of a mixture model.

Although gathered as continuous data, expression measurements from gene microarrays may be quantized before downstream analysis and modeling. This is especially true for modeling gene prediction and genetic regulatory networks. Coarse quantization results in lower computational requirements, lower data requirements for model inference, and easier conceptualization. This paper proposes a mixture model for binarization. For each gene, the model, composed of a sum of two distributions, is fit to expression data for that gene, and data points are binarized according to the model. The mixture model is based on the assumption of multiplicative up-regulation. The proposed method is compared with mean and median binarization by comparing classification performance based on the binary data from the different methods. Classification is performed for simulated data generated from a microarray model studied previously and for cancer data arising from two studies involving hereditary breast cancer and small, round blue-cell tumors of childhood.

Breast Neoplasms↗

PubMed2000

Nested cladistic analysis indicates population fragmentation shapes genetic diversity in a freshwater mussel.

Recently developed phylogeographic analyses that incorporate genealogical relationships of alleles offer the exciting prospect of disentangling historical from contemporary events. However, the relative advantages and shortfalls of this approach remain to be studied. We compared the nested cladistic method to the more traditional analysis of variance approach in a study of intraspecific genetic variation in the freshwater mussel, Lampsilis hydiana. We surveyed 257 specimens for nucleotide sequence level variation in a fragment of the mitochondrial 16S rRNA gene. When compared side by side, nested cladistic analysis and analysis of molecular variance (AMOVA) identified fragmentation of Arkansas river populations from remaining populations to the southwest. Nested cladistic analysis identified a second, more recent separation of Ouachita and Upper Saline river populations that was not detected by AMOVA. Differences among analytical methods probably arise from treatment of spatial hierarchical information: hierarchical groups emerge via a parsimony criterion in nested cladistic analysis but must be specified a priori in AMOVA. Both methods identified significant genetic structure among localities within hierarchical groups. Results from AMOVA suggested little gene flow among local populations with an island model. However, inferences about process that gave rise to patterns at this level were not possible in nested cladistic analysis, because an ancestral (interior) haplotype was not observed for a key one-step clade in the parsimony network. Our results suggest that, under some circumstances, nested cladistic analysis has lower power than more traditional analysis of variance to infer processes at the local population level.

Analysis of Variance↗

PubMed2006

QPath: a method for querying pathways in a protein-protein interaction network.

BACKGROUND: Sequence comparison is one of the most prominent tools in biological research, and is instrumental in studying gene function and evolution. The rapid development of high-throughput technologies for measuring protein interactions calls for extending this fundamental operation to the level of pathways in protein networks. RESULTS: We present a comprehensive framework for protein network searches using pathway queries. Given a linear query pathway and a network of interest, our algorithm, QPath, efficiently searches the network for homologous pathways, allowing both insertions and deletions of proteins in the identified pathways. Matched pathways are automatically scored according to their variation from the query pathway in terms of the protein insertions and deletions they employ, the sequence similarity of their constituent proteins to the query proteins, and the reliability of their constituent interactions. We applied QPath to systematically infer protein pathways in fly using an extensive collection of 271 putative pathways from yeast. QPath identified 69 conserved pathways whose members were both functionally enriched and coherently expressed. The resulting pathways tended to preserve the function of the original query pathways, allowing us to derive a first annotated map of conserved protein pathways in fly. CONCLUSION: Pathway homology searches using QPath provide a powerful approach for identifying biologically significant pathways and inferring their function. The growing amounts of protein interactions in public databases underscore the importance of our network querying framework for mining protein network data.

Algorithms↗

PubMed2005

An integrative genomics approach to infer causal associations between gene expression and disease.

A key goal of biomedical research is to elucidate the complex network of gene interactions underlying complex traits such as common human diseases. Here we detail a multistep procedure for identifying potential key drivers of complex traits that integrates DNA-variation and gene-expression data with other complex trait data in segregating mouse populations. Ordering gene expression traits relative to one another and relative to other complex traits is achieved by systematically testing whether variations in DNA that lead to variations in relative transcript abundances statistically support an independent, causative or reactive function relative to the complex traits under consideration. We show that this approach can predict transcriptional responses to single gene-perturbation experiments using gene-expression data in the context of a segregating mouse population. We also demonstrate the utility of this approach by identifying and experimentally validating the involvement of three new genes in susceptibility to obesity.

11-beta-Hydroxysteroid Dehydrogenase Type 1↗

PubMed2004

A shared neural network for simple reaction time.

Simple reaction time, a simple model of sensory-to-motor behavior, has been extensively investigated and its role in inferring elementary mental organization has been postulated. However, little is known about the neuronal mechanisms underlying it. To elucidate the neuronal substrates, functional magnetic resonance imaging (fMRI) signals were collected during a simple reaction task paradigm using simple cues consisting of different modalities and simple triggered movements executed by different effectors. We hypothesized that a specific neural network that characterizes simple reaction time would be activated irrespective of the input modalities and output effectors. Such a neural network was found in the right posterior superior temporal cortex, right premotor cortex, left ventral premotor cortex, cerebellar vermis, and medial frontal gyrus. The right posterior superior temporal cortex and right premotor cortex were also activated by different modality sensory cues in the absence of movements. The shared neural network may play a role in sensory triggered movements.

Adult↗

PubMed2002

CCCP algorithms to minimize the Bethe and Kikuchi free energies: convergent alternatives to belief propagation.

This article introduces a class of discrete iterative algorithms that are provably convergent alternatives to belief propagation (BP) and generalized belief propagation (GBP). Our work builds on recent results by Yedidia, Freeman, and Weiss (2000), who showed that the fixed points of BP and GBP algorithms correspond to extrema of the Bethe and Kikuchi free energies, respectively. We obtain two algorithms by applying CCCP to the Bethe and Kikuchi free energies, respectively (CCCP is a procedure, introduced here, for obtaining discrete iterative algorithms by decomposing a cost function into a concave and a convex part). We implement our CCCP algorithms on two- and three-dimensional spin glasses and compare their results to BP and GBP. Our simulations show that the CCCP algorithms are stable and converge very quickly (the speed of CCCP is similar to that of BP and GBP). Unlike CCCP, BP will often not converge for these problems (GBP usually, but not always, converges). The results found by CCCP applied to the Bethe or Kikuchi free energies are equivalent, or slightly better than, those found by BP or GBP, respectively (when BP and GBP converge). Note that for these, and other problems, BP and GBP give very accurate results (see Yedidia et al., 2000), and failure to converge is their major error mode. Finally, we point out that our algorithms have a large range of inference and learning applications.

Algorithms↗

PubMed2002

The fingerprint of phantom mutations in mitochondrial DNA data.

Phantom mutations are systematic artifacts generated in the course of the sequencing process itself. In sequenced mitochondrial DNA (mtDNA), they generate a hotspot pattern quite different from that of natural mutations in the cell. To identify the telltale patterns of a particular phantom mutation process, one first filters out the well-established frequent mutations (inferred from various data sets with additional coding region information). The filtered data are represented by their full (quasi-)median network, to visualize the character conflicts, which can be expressed numerically by the cube spectrum. Permutation tests are used to evaluate the overall phylogenetic content of the filtered data. Comparison with benchmark data sets helps to sort out suspicious data and to infer features and potential causes for the phantom mutation process. This approach, performed either in the lab or at the desk of a reviewer, will help to avoid errors that otherwise would go into print and could lead to erroneous evolutionary interpretations. The filtering procedure is illustrated with two mtDNA data sets that were severely affected by phantom mutations.

Artifacts↗

PubMed2005

The yeast kinome displays scale free topology with functional hub clusters.

BACKGROUND: The availability of interaction databases provides an opportunity for researchers to utilize immense amounts of data exclusively in silico. Recently there has been an emphasis on studying the global properties of biological interactions using network analysis. While this type of analysis offers a wide variety of global insights it has surprisingly not been used to examine more localized interactions based on mechanism. In as such we have particular interest in the role of key topological components in signal transduction cascades as they are vital regulators of healthy and diseased cell states. RESULTS: We have used publicly available databases and a novel software tool termed Hubview to model the interactions of a subset of the yeast interactome, specifically protein kinases and their interaction partners. Analysis of the connectivity distribution has inferred a fat-tailed degree distribution with parameters consistent with those found in other biological networks. In addition, Hubview identified a functional clustering of a large group of kinases, distributed between three separate groupings. The complexity and average degree for each of these clusters is indicative of a specialized function (cell cycle propagation, DNA repair and pheromone response) and relative age for each cluster. CONCLUSION: Using connectivity analysis on a functional subset of proteins we have evidence that reinforces the scale free topology as a model for protein network evolution. We have identified the hub components of the kinase network and observed a tendency for these kinases to cluster together on a functional basis. As such, these results suggest an inherent trend to preserve scale free characteristics at a domain based modular level within large evolvable networks.

Multigene Family↗

PubMed2006

A joint model of regulatory and metabolic networks.

BACKGROUND: Gene regulation and metabolic reactions are two primary activities of life. Although many works have been dedicated to study each system, the coupling between them is less well understood. To bridge this gap, we propose a joint model of gene regulation and metabolic reactions. RESULTS: We integrate regulatory and metabolic networks by adding links specifying the feedback control from the substrates of metabolic reactions to enzyme gene expressions. We adopt two alternative approaches to build those links: inferring the links between metabolites and transcription factors to fit the data or explicitly encoding the general hypotheses of feedback control as links between metabolites and enzyme expressions. A perturbation data is explained by paths in the joint network if the predicted response along the paths is consistent with the observed response. The consistency requirement for explaining the perturbation data imposes constraints on the attributes in the network such as the functions of links and the activities of paths. We build a probabilistic graphical model over the attributes to specify these constraints, and apply an inference algorithm to identify the attribute values which optimally explain the data. The inferred models allow us to 1) identify the feedback links between metabolites and regulators and their functions, 2) identify the active paths responsible for relaying perturbation effects, 3) computationally test the general hypotheses pertaining to the feedback control of enzyme expressions, 4) evaluate the advantage of an integrated model over separate systems. CONCLUSION: The modeling results provide insight about the mechanisms of the coupling between the two systems and possible "design rules" pertaining to enzyme gene regulation. The model can be used to investigate the less well-probed systems and generate consistent hypotheses and predictions for further validation.

Cell Physiological Phenomena↗