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PubMed2000

Biobibliometrics: information retrieval and visualization from co-occurrences of gene names in Medline abstracts.

Successful information retrieval from biomedical literature databases is becoming increasingly difficult. We have developed a prototype system for retrieving and visualizing information from literature and genomic databases using gene names. The premise of our work is that, if two genes have a related biological function, the co-occurrence of two gene names (or aliases of those genes) within the biomedical literature is more likely. From a collection of Medline documents, we have extracted the number of co-occurrences of every pair of Saccharomyces cerevisiae genes. The query is automatically conflated to include gene aliases as well. In addition, the retrieved document set can be filtered by the user with a MeSH term. From this co-occurrence data we construct a matrix that contains dissimilarity measurements of every pair of genes, based on their joint and individual occurrence statistics. A graph is generated from this matrix, with node and edge inclusion being determined by a user-defined threshold. Nodes of the graph represent genes, while edge lengths are a function of the occurrence of the two genes within the literature. Nodes can be hypertext-linked to sequence databases, while edges are linked to those Medline documents that generated them. The system is a tool for efficiently exploring the biomedical information landscape and may act as a inference network.

Databases, Factual↗

PubMed2025

Challenges and Opportunities in Analyzing Cancer-Associated Microbiomes.

The study of cancer-associated microbiomes has gained significant attention in recent years, spurred by advances in high-throughput sequencing and metagenomic analysis. Microbiome research holds promise for identifying noninvasive biomarkers and possibly new paradigms for cancer treatment. In this review, we explore the key computational challenges and opportunities in analyzing cancer-associated microbiomes (in tumor/normal tissues and other body sites, e.g., gut, oral, and skin), focusing on sequencing-driven strategies and associated considerations for taxonomic and functional characterization. The discussion covers the strengths and limitations of current analysis tools for identifying contamination, determining compositional bias, and resolving species and strains, as well as the statistical, metabolic, and network inferences that are essential to uncover host-microbiome interactions. Several key considerations are required to guide the choice of databases used for metagenomic analysis in such studies. Recent advances in spatial and single-cell technologies have provided insights into cancer-associated microbiomes, and Artificial Intelligence-driven protein function prediction might enable rapid advances in this field. Finally, we provide a perspective on how the field can evolve to manage the ever-growing size of datasets and generate robust and testable hypotheses. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .

Humans↗

PubMed1996

Automated reasoning system in histopathologic diagnosis and prognosis of prostate cancer and its precursors.

OBJECTIVE: This article presents the rationale and options offered to diagnostic and prognostic decision support systems for prostate pathology by automated reasoning capabilities. METHODS: The symbolic information used in diagnostic decision-making is systematically ordered, compared, numerically assessed in its probability, and combined such that a conclusion can be drawn. The framework for the processing of such symbolic information may be an expert system, an inference network or a case-based reasoning system. Automated reasoning is implemented by the use of a rule base and information flow control modules. RESULTS: Automated reasoning allows decision support systems to follow highly adaptive decision sequences, capable of handling contradictory evidence, exceptions in diagnostic clue expression, and nonmonotonic decision-making. CONCLUSIONS: Automated reasoning capability in diagnostic and prognostic decision support systems allows highly flexible decision development, very close to human decision procedures.

Artificial Intelligence↗

PubMed2026

Deciphering the Genetic Underpinnings of Liver Cirrhosis-Heart Failure Comorbidity Through Multi-Omics: CRIM1 as a Key Endothelial Mediator.

The co-occurrence of liver cirrhosis (LC) and heart failure (HF) poses considerable clinical challenges, yet the cellular and molecular determinants of this comorbidity remain poorly characterized. To address this, we developed an integrative multi-omics pipeline encompassing GWAS meta-analysis, gsMap-based spatial transcriptomic projection, GeneEnrich functional annotation, single-cell atlas construction, seismicGWAS and ECLIPSER cell-type scoring, eCAVIAR and fastenloc colocalization, hdWGCNA network inference, scTenifoldKnk in silico gene perturbation, and GCTA-COJO fine-mapping. Quality-controlled meta-analysis yielded 12,347,758 and 9,256,862 variant-level associations for LC and HF, respectively. Spatial projection confirmed preferential enrichment of disease signals within embryonic hepatic and cardiac compartments. Pathway analyses disclosed that LC-linked loci were concentrated in lipid metabolic programs, whereas HF-linked loci implicated mitochondrial bioenergetics and lysosomal degradation. At the cellular level, endothelial cells emerged as the dominant HF-associated population. Convergent evidence from five orthogonal algorithms pinpointed CRIM1 as the sole robustly supported shared gene, selectively enriched in HF endothelial cells; virtual perturbation further identified LCP1 and PTPRC as downstream regulatory nodes. Fine-mapping of the chromosome 2 locus harboring rs12476437 revealed multiple statistically independent signals in the vicinity of CRIM1. Collectively, these findings computationally prioritize the endothelial-CRIM1 axis as a previously unappreciated candidate mechanistic bridge between LC and HF requiring experimental validation.

Humans↗

PubMed2026

Perturbation of genes linked to common schizophrenia risk variants identifies cilia programs.

Schizophrenia (SCZ) is a common psychiatric disorder characterized by psychosis, emotional withdrawal, and cognitive deficits. Most SCZ risk variants reside in non-coding regions of the genome and are thought to influence disease risk by modulating gene regulation. However, the target genes, biological pathways, and cell types through which these variants exert their effects remain poorly understood. To address this gap, we employed in vivo CRISPR droplet sequencing (CROP-seq) in the postnatal mouse neocortex. We perturbed 12 SCZ risk genes previously linked to functionally validated risk variants, followed by single-cell RNA sequencing. We identified 3,031 differentially expressed genes (DEGs) that recapitulate transcriptional alterations observed in postmortem SCZ brains. Integrative analysis using DEG clustering, factor analysis, and gene regulatory network inference uncovered convergent gene programs with distinct biological functions and cell type specificity. Notably, ciliary transcriptional programs consistently emerged across analytical frameworks. The primary cilium is a neurocircuit modulating signaling organelle in neurons and glia that remains understudied in SCZ. Perturbation of key contributors to the ciliary transcriptional programs led to significant alterations in ciliary structure, suggesting that SCZ genetic risk factors may influence how brain cells sense and transduce extracellular signals through synapse-independent mechanisms. Together, this study provides the first in vivo characterization of the functional consequence of common variant architecture in SCZ and implicates ciliary dysfunction as a convergent downstream mechanism.

Journal Article↗

PubMed2005

Elucidation of the relationships between LexA-regulated genes in the SOS response.

Monitoring the expression of many genes under different conditions is a common approach for investigating gene relationships. In particular, the monitoring sheds light on the biological phenomena in which many genes are coordinately expressed. In this study, we analyzed the expression profiles of LexA-regulated genes after UV irradiation, to elucidate the genes related to the SOS response, which involves coordinately regulated gene expression. By the two-gene relationship analysis, the LexA-regulated genes were highly correlated with the genes involved in the DNA repair functions. The LexA-regulated genes with highly significant probability were divided into two groups: the LexA-regulated genes that were mutually related within them were related to the genes with DNA repair functions, while the LexA-regulated genes that were less related within them showed lower relation to the genes with DNA repair functions. By a multiple gene relationship analysis, the two types of LexA-regulated genes were clearly clustered, and the inferred network between the clusters indicated their sequential relationship of clusters in the two groups of LexA-regulated genes in the SOS response; the former type of genes emerged in the early stage of the SOS response upon the signal transduction by membrane proteins, cessation of cell division and recognition of DNA damage, and the latter type emerged in a later stage, and functioned in the repair mechanism and the resumption of DNA replication.

Bacterial Proteins↗

PubMed1995

Description generation of abnormal densities found in radiographs.

In this paper we present a system for describing renal stones found in radiographs. The system generates descriptions that adhere to those generated by radiologists. The descriptions are formulated by discovering the spatial relationships that exist between the major organs and the renal stones. The system consists of three major components. The first is the image processing component which is responsible for locating the stone. The second component is the inference network minimization component which determines which spatial relationships, of all those that exist between the stone and the organs, is the most descriptive. The third component is the natural language generation component which is responsible for translating the spatial relationships into appropriate medical terminology. We will illustrate all these components on several examples.

Algorithms↗

PubMed1995

Diagnostic and prognostic decision support systems.

Diagnostic decision support systems provide a quantitative evaluation of diagnostic evidence and the capability to combine diagnostic evidence in such a manner that a numeric measure of certainty in a final diagnostic recommendation results. Generally, expert systems serve to establish a diagnostic decision, inference networks allow a detailed analysis of the diagnostic value of diagnostic clues, case-based reasoning systems are designed to provide a prognostic assessment targeted to an individual patient. In all of these systems, symbolic information, i.e., traditional diagnostic, linguistic terms and concepts are processed and quantitatively evaluated.

Decision Support Techniques↗

PubMed1995

Effective access to distributed heterogeneous medical text databases.

INQUERY is an advanced text information retrieval system developed by the Information Retrieval Laboratory of the University of Massachusetts in Amherst. It is based on Bayesian inference networks, which are probabilistic models for reasoning with multiple sources of uncertain evidence. The evidence, in this case, is the presence or absence of words and/or phrases in a document. Evidence is combined into belief that a document is relevant. The INQUERY retrieval engine has been developed with the support of ARPA, NSF, and industrial funding. It has a number of unique features and has achieved excellent results in the TIPSTER and TREC evaluations. Informatics research and application development using INQUERY has recently begun in the medical domain, including a new ARPA initiative concerned with clinical text. The features that we will focus on in this demonstration are: Automatic processing of natural language queries, including the extraction of phrases and specific medical concepts such as drug doses; Document selection through automatic relevance feedback and routing techniques, including the construction of complex queries using the INQUERY query language; The integration of conventional database techniques with text analysis and retrieval; Automatic thesaurus generation and query expansion using the PhraseFinder system; Distributed database access, including automatic database selection and merging of local searches; this will be demonstrated using a collection of medical databases; Retrieval based on passages, rather than whole documents.

Bayes Theorem↗

PubMed2004

Application of adaptive noise cancellation with neural-network-based fuzzy inference system for visual evoked potentials estimation.

This paper presents an application of adaptive noise cancellation with neural-network-based fuzzy inference system (NNFIS) for rapid estimation of visual evoked potentials (VEPs). Usually a recorded VEP is severely contaminated by background ongoing activities of the spontaneous EEG signal in the human brain. Many approaches have been adopted to enhance the signal-to-noise ratio (SNR) of the recorded signal. However, nonlinear dynamic methods are rarely investigated in view of their complexity, and the fact that the nonlinear characteristics of the signal are hard to determine in general. An adaptive noise cancellation method with NNFIS was carefully designed to estimate the VEP signal. NNFIS, based on Takagi and Sugeno's fuzzy model, has the advantage of being linear-in-parameter; thus the conventional adaptive methods can be efficiently utilized to estimate its parameters. Another advantage of NNFIS lies in that it can track the dynamic behavior of VEP in a real-time fashion because the VEP variation tracking is important for critical patient monitoring in the clinical situation. A series of computer experiments conducted on simulated and real-test responses have confirmed the superiority of the method developed in this paper.

Algorithms↗

PubMed2027

Inferring Gene Regulatory Networks in Stem Cells: Methods and Applications.

Gene regulatory networks (GRNs) represent the complex interplay of transcription factors, regulatory elements, and target genes that orchestrate cellular identity and function, playing a crucial role in the differentiation and maintenance of stem cells. This chapter provides an overview of experimental and computational methodologies for inferring GRNs, with particular emphasis on single-cell approaches. We first review key experimental techniques for detecting transcription factor binding sites, chromatin accessibility, and DNA motifs, alongside essential databases that support GRN reconstruction. We then introduce computational inference methods that can be categorized into four principal frameworks: correlation-based approaches, regression and machine learning models, probabilistic and deep learning methods, and integrative or message-passing frameworks. To illustrate practical application, we present a case study applying the pySCENIC workflow to a peripheral blood mononuclear cell single-cell RNA sequencing dataset from mouse, demonstrating how regulon-based analysis can reveal cell-type-specific regulatory programs. This chapter aims to serve as a practical guide for researchers seeking to understand and implement GRN inference methodologies in stem cell biology and related fields.

Gene Regulatory Networks↗

PubMed2002

Linking the genes: inferring quantitative gene networks from microarray data.

Modern microarray technology is capable of providing data about the expression of thousands of genes, and even of whole genomes. An important question is how this technology can be used most effectively to unravel the workings of cellular machinery. Here, we propose a method to infer genetic networks on the basis of data from appropriately designed microarray experiments. In addition to identifying the genes that affect a specific other gene directly, this method also estimates the strength of such effects. We will discuss both the experimental setup and the theoretical background.

Animals↗

PubMed2003

Inferring gene regulatory networks from time-ordered gene expression data of Bacillus subtilis using differential equations.

We describe a new method to infer a gene regulatory network, in terms of a linear system of differential equations, from time course gene expression data. As biologically the gene regulatory network is known to be sparse, we expect most coefficients in such a linear system of differential equations to be zero. In previously proposed methods, the number of nonzero coefficients in the system was limited based on ad hoc assumptions. Instead, we propose to infer the degree of sparseness of the gene regulatory network from the data, where we use Akaike's Information Criterion to determine which coefficients are nonzero. We apply our method to MMGE time course data of Bacillus subtilis.

Bacillus subtilis↗

PubMed2006

Inferring gene regulatory networks from multiple microarray datasets.

MOTIVATION: Microarray gene expression data has increasingly become the common data source that can provide insights into biological processes at a system-wide level. One of the major problems with microarrays is that a dataset consists of relatively few time points with respect to a large number of genes, which makes the problem of inferring gene regulatory network an ill-posed one. On the other hand, gene expression data generated by different groups worldwide are increasingly accumulated on many species and can be accessed from public databases or individual websites, although each experiment has only a limited number of time-points. RESULTS: This paper proposes a novel method to combine multiple time-course microarray datasets from different conditions for inferring gene regulatory networks. The proposed method is called GNR (Gene Network Reconstruction tool) which is based on linear programming and a decomposition procedure. The method theoretically ensures the derivation of the most consistent network structure with respect to all of the datasets, thereby not only significantly alleviating the problem of data scarcity but also remarkably improving the prediction reliability. We tested GNR using both simulated data and experimental data in yeast and Arabidopsis. The result demonstrates the effectiveness of GNR in terms of predicting new gene regulatory relationship in yeast and Arabidopsis. AVAILABILITY: The software is available from http://zhangorup.aporc.org/bioinfo/grninfer/, http://digbio.missouri.edu/grninfer/ and http://intelligent.eic.osaka-sandai.ac.jp or upon request from the authors.

Algorithms↗

PubMed2000

Inferring qualitative relations in genetic networks and metabolic pathways.

MOTIVATION: Inferring genetic network architecture from time series data of gene expression patterns is an important topic in bioinformatics. Although inference algorithms based on the Boolean network were proposed, the Boolean network was not sufficient as a model of a genetic network. RESULTS: First, a Boolean network model with noise is proposed, together with an inference algorithm for it. Next, a qualitative network model is proposed, in which regulation rules are represented as qualitative rules and embedded in the network structure. Algorithms are also presented for inferring qualitative relations from time series data. Then, an algorithm for inferring S-systems (synergistic and saturable systems) from time series data is presented, where S-systems are based on a particular kind of nonlinear differential equation and have been applied to the analysis of various biological systems. Theoretical results are shown for Boolean networks with noises and simple qualitative networks. Computational results are shown for Boolean networks with noises and S-systems, where real data are not used because the proposed models are still conceptual and the quantity and quality of currently available data are not enough for the application of the proposed methods.

Algorithms↗

PubMed1998

Reveal, a general reverse engineering algorithm for inference of genetic network architectures.

Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able to infer the sets of input elements controlling each element or gene in the network. This process is unequivocal and exact for complete state transition tables. We implemented this REVerse Engineering ALgorithm (REVEAL) in a C program, and found the problem to be tractable within the conditions tested so far. For n = 50 (elements) and k = 3 (inputs per element), the analysis of incomplete state transition tables (100 state transition pairs out of a possible 10(15)) reliably produced the original rule and wiring sets. While this study is limited to synchronous Boolean networks, the algorithm is generalizable to include multi-state models, essentially allowing direct application to realistic biological data sets. The ability to adequately solve the inverse problem may enable in-depth analysis of complex dynamic systems in biology and other fields.

Algorithms↗

PubMed2006

Functional and evolutionary inference in gene networks: does topology matter?

The relationship between the topology of a biological network and its functional or evolutionary properties has attracted much recent interest. It has been suggested that most, if not all, biological networks are 'scale free.' That is, their connections follow power-law distributions, such that there are very few nodes with very many connections and vice versa. The number of target genes of known transcriptional regulators in the yeast, Saccharomyces cerevisiae, appears to follow such a distribution, as do other networks, such as the yeast network of protein-protein interactions. These findings have inspired attempts to draw biological inferences from general properties associated with scale-free network topology. One often cited general property is that, when compromised, highly connected nodes will tend to have a larger effect on network function than sparsely connected nodes. For example, more highly connected proteins are more likely to be lethal when knocked out. However, the correlation between lethality and connectivity is relatively weak, and some highly connected proteins can be removed without noticeable phenotypic effect. Similarly, network topology only weakly predicts the response of gene expression to environmental perturbations. Evolutionary simulations of gene-regulatory networks, presented here, suggest that such weak or non-existent correlations are to be expected, and are likely not due to inadequacy of experimental data. We argue that 'top-down' inferences of biological properties based on simple measures of network topology are of limited utility, and we present simulation results suggesting that much more detailed information about a gene's location in a regulatory network, as well as dynamic gene-expression data, are needed to make more meaningful functional and evolutionary predictions. Specifically, we find in our simulations that: (1) the relationship between a gene's connectivity and its fitness effect upon knockout depends on its equilibrium expression level; (2) correlation between connectivity and genetic variation is virtually non-existent, yet upon independent evolution of networks with identical topologies, some nodes exhibit consistently low or high polymorphism; and (3) certain genes show low polymorphism yet high divergence among independent evolutionary runs. This latter pattern is generally taken as a signature of positive selection, but in our simulations its cause is often neutral coevolution of regulatory inputs to the same gene.

Algorithms↗

PubMed2006

Inferring biomolecular regulatory networks from phase portraits of time-series expression profiles.

Reverse engineering of biomolecular regulatory networks such as gene regulatory networks, protein interaction networks, and metabolic networks has received an increasing attention as more high-throughput time-series measurements become available. In spite of various approaches developed from this motivation, it still remains as a challenging subject to develop a new reverse engineering scheme that can effectively uncover the functional interaction structure of a biomolecular network from given time-series expression profiles (TSEPs). We propose a new reverse engineering scheme that makes use of phase portraits constructed by projection of every two TSEPs into respective phase planes. We introduce two measures of a slope index (SI) and a winding index (WI) to quantify the interaction properties embedded in the phase portrait. Based on the SI and WI, we can reconstruct the functional interaction network in a very efficient and systematic way with better inference results compared to previous approaches. By using the SI, we can also estimate the time-lag accompanied with the interaction between molecular components of a network.

Animals↗