PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “genomic data”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 181 records · Page 10Linked to original sources
PubMed2003

A rapid method for detection of putative RNAi target genes in genomic data.

RNAi, inhibition of gene expression by double stranded RNA molecules, has rapidly become a powerful laboratory technique to study gene function. The effectiveness of the procedure raised the question of whether this laboratory technique may actually mimic a natural cellular control mechanism that works on similar principles. Indeed recent evidence is accumulating to suggest that RNAi is a natural control mechanism that might even serve as a primitive immune response against RNA viruses and retroposons. Three different interference scenarios seem to be utilized by various RNAi mechanisms. One of the mechanisms involves degradation of mRNA molecules. Here we suggest a method to systematically scan entire genomes simultaneously for RNAi elements and the presence of cellular genes that are degraded by these RNAi elements via exact short base-pair matching. The method is based on scanning the genomes using a suffix tree data structure that was specifically modified to identify sets of combinations of repeated and inverted repeated sequences of 20 bp or more. Initial scan suggest that a large number, about 7% of C.elegans and 3% of C.briggsae genes, have the potential to be subject to natural RNAi control. Two methods are proposed to further analyze these genes to select the cases that are more likely to be actual cases of RNAi control. One method involves looking for ESTs that can provide direct evidence that RNAi control element are indeed expressed. The other method looks for synteny between C.elegans and C.briggsae assuming that genes that might be under RNAi control in both organisms are more likely to be biological significant. Taken together, supportive evidence was found for about 70 genes to be under RNAi control. Among these genes are: transposase, hormone receptors, homeobox proteins, defensin, actins, and several types of collagens. While our method is not capable of detecting all cases of natural RNAi control, it points to a large number of potential cases that can be further verified by experimental work.

Animals↗
PubMed2003

Secure communication and management of clinical and genomic data: the use of pseudonymisation as privacy enhancing technique.

The growing need of managing both clinical and genetic data raises important legal and ethical challenges. This article introduces some of the privacy protection problems related to genomic medicine and highlights the relevance of Trusted Third Parties and of Privacy Enhancing Techniques (PETs) in the context of e.g. research. Practical approaches based on two pseudonymisation models, for both batch and interactive data collection and exchange, are presented.

Access to Information↗
PubMed2004

MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes.

MAPMAN is a user-driven tool that displays large data sets onto diagrams of metabolic pathways or other processes. SCAVENGER modules assign the measured parameters to hierarchical categories (formed 'BINs', 'subBINs'). A first build of TRANSCRIPTSCAVENGER groups genes on the Arabidopsis Affymetrix 22K array into >200 hierarchical categories, providing a breakdown of central metabolism (for several pathways, down to the single enzyme level), and an overview of secondary metabolism and cellular processes. METABOLITESCAVENGER groups hundreds of metabolites into pathways or groups of structurally related compounds. An IMAGEANNOTATOR module uses these groupings to organise and display experimental data sets onto diagrams of the users' choice. A modular structure allows users to edit existing categories, add new categories and develop SCAVENGER modules for other sorts of data. MAPMAN is used to analyse two sets of 22K Affymetrix arrays that investigate the response of Arabidopsis rosettes to low sugar: one investigates the response to a 6-h extension of the night, and the other compares wild-type Columbia-0 (Col-0) and the starchless pgm mutant (plastid phosphoglucomutase) at the end of the night. There were qualitatively similar responses in both treatments. Many genes involved in photosynthesis, nutrient acquisition, amino acid, nucleotide, lipid and cell wall synthesis, cell wall modification, and RNA and protein synthesis were repressed. Many genes assigned to amino acid, nucleotide, lipid and cell wall breakdown were induced. Changed expression of genes for trehalose metabolism point to a role for trehalose-6-phosphate (Tre6P) as a starvation signal. Widespread changes in the expression of genes encoding receptor kinases, transcription factors, components of signalling pathways, proteins involved in post-translational modification and turnover, and proteins involved in the synthesis and sensing of cytokinins, abscisic acid (ABA) and ethylene revealing large-scale rewiring of the regulatory network is an early response to sugar depletion.

Abscisic Acid↗
PubMed2006

Shrunken p-values for assessing differential expression with applications to genomic data analysis.

In many scientific problems involving high-throughput technology, inference must be made involving several hundreds or thousands of hypotheses. Recent attention has focused on how to address the multiple testing issue; much focus has been devoted toward the use of the false discovery rate. In this article, we consider an alternative estimation procedure titled shrunken p-values for assessing differential expression (SPADE). The estimators are motivated by risk considerations from decision theory and lead to a completely new method for adjustment in the multiple testing problem. In addition, the decision-theoretic framework can be used to derive a decision rule for controlling the number of false positive results. Some theoretical results are outlined. The proposed methodology is illustrated using simulation studies and with application to data from a prostate cancer gene expression profiling study.

Biometry↗
PubMed2026

NeuroOmics-Net: An interpretable multimodal deep learning framework for Alzheimer's disease diagnosis and progression prediction using neuroimaging, EEG, and genomic data.

Accurate diagnosis and progression prediction of Alzheimer's disease (AD) remain challenging due to the heterogeneous nature of the disease, which involves structural brain degeneration, electrophysiological dysfunction, and molecular dysregulation. Most existing deep learning approaches rely on a single modality or limited multimodal combinations, thereby failing to capture the complex cross-domain interactions underlying AD progression. Furthermore, the scarcity of large-scale datasets containing synchronized neuroimaging, electrophysiological, and genomic measurements restricts the development of comprehensive multimodal diagnostic systems. To address these challenges, this study proposes NeuroOmics-Net, a multimodal deep learning framework for Alzheimer's disease analysis that integrates structural magnetic resonance imaging (sMRI), electroencephalography (EEG), and gene expression data. The proposed framework combines a Hierarchical Multi-View Encoder (HME) for modality-specific feature extraction, a Cross-Omics Attention Fusion (CAF) module for adaptive integration of complementary biomarkers, and a Disease Progression Graph Learning (DPGL) module for modeling progression-related relationships across biological domains. To facilitate cross-modal integration from independent cohorts, Regularized Canonical Correlation Analysis (RCCA) is employed to align heterogeneous feature representations within a shared latent space. Experiments were conducted using publicly available datasets from ADNI, PhysioNet, and GEO repositories comprising 1120 diagnosis-aligned samples. The proposed framework achieved 94.3% classification accuracy and an AUC of 0.975 for distinguishing normal controls (NC), mild cognitive impairment (MCI), and Alzheimer's disease subjects, while attaining 93.7% accuracy for predicting conversion from stable mild cognitive impairment (sMCI) to progressive mild cognitive impairment (pMCI). However, a fairness sensitivity analysis using stratified demographic reweighting revealed accuracy ranging from 90.8% (low-education, high-comorbidity proxy subgroup) to 96.1% (low-risk, high-reserve proxy subgroup), a demographic parity gap of 5.3 percentage points, indicating that overall accuracy reflects a performance ceiling in a relatively homogeneous research cohort rather than a realistic estimate for demographically diverse clinical populations. Comparative evaluations demonstrated consistent improvements over state-of-the-art unimodal and multimodal deep learning models. Interpretability analysis further identified clinically relevant biomarkers, including hippocampal and entorhinal atrophy, theta-alpha EEG alterations, and APOE-associated molecular pathways. Because sMRI, EEG, and gene expression data were sourced from separate, unpaired cohorts with no subjects possessing all three synchronized measurements, all reported cross-modal associations reflect population-level statistical correspondence across diagnosis-matched groups rather than within-subject physiological coupling; no claim of intra-individual causal cross-modal interaction is made. These findings demonstrate that NeuroOmics-Net provides an effective computer-aided framework for multimodal biomedical data processing and Alzheimer's disease analysis. By integrating neuroimaging, electrophysiological, and genomic information, the proposed approach enables accurate diagnosis, progression prediction, and biologically interpretable decision support for clinical and translational applications.

Humans↗
PubMed2004

Mitochondrial genome data support the basal position of Acoelomorpha and the polyphyly of the Platyhelminthes.

We determined 9.7, 5.2, and 6.8 kb, respectively, of the mitochondrial genomes of the acoel Paratomella rubra, the nemertodermatid Nemertoderma westbladi, and the free-living rhabditophoran platyhelminth Microstomum lineare. The identified gene arrangements are unique among metazoans, including each other, sharing no more than one or two single gene boundaries with a few distantly related taxa. Phylogenetic analysis of the amino acid sequences inferred from the sequenced genes confirms that the acoelomorph flatworms (acoels+nemertodermatids) do not belong to the Platyhelminthes, but are, instead, the most basal extant bilaterian group. Therefore, the Platyhelminthes, as traditionally constituted, is a polyphyletic phylum.

Animals↗
PubMed2026

POISE: Spectral Inference of Parent-of-Origin Effects in Unlabeled Genomic Data.

MOTIVATION: Parent of Origin Effects (POEs), where the effect of an an allele on a phenotype differs based on maternal or paternal inheritance implicated in growth, metabolism, and neurodevelopment. Traditional tests for POEs require family data to determine parental origins of transmitted alleles. Given that such studies are expensive and time consuming compared to genome-wide association studies (GWAS), tests that function absent inheritance information are highly desirable. We develop a method, based on community detection from machine learning, that infers POEs via a spectral decomposition, obtains confidence intervals via a non-parametric bootstrap, and safeguards against confounding by non POE sources of variation. We refer to our method as Parent of Origin Inference via Spectral Estimation (POISE). RESULTS: We demonstrate that POISE is well-calibrated under both Gaussian and heavy-tailed noise in simulation studies, with improved robustness to true POEs compared to existing covariance-based tests. POISE provides per-trait effect estimates with bias-corrected bootstrap confidence intervals and incorporates an information-theoretic minimum detectable effect size that filters unreliable estimates, conferring robustness to covariance-deflating variance QTL. We then apply POISE to GWAS data from the UK Biobank using BMI, LDL cholesterol, and HDL cholesterol. POISE recovers established POE loci and identifies 134 additional variants at genes implicated in lipid metabolism, immune regulation, and growth. AVAILABILITY AND IMPLEMENTATION: The code for this method in Python is available at https://github.com/bystrogenomics/POISE.

Community Detection↗
PubMed2004

Genome data mining of lactic acid bacteria: the impact of bioinformatics.

Lactic acid bacteria (LAB) have been widely used in food fermentations and, more recently, as probiotics in health-promoting food products. Genome sequencing and functional genomics studies of a variety of LAB are now rapidly providing insights into their diversity and evolution and revealing the molecular basis for important traits such as flavor formation, sugar metabolism, stress response, adaptation and interactions. Bioinformatics plays a key role in handling, integrating and analyzing the flood of 'omics' data being generated. Reconstruction of metabolic potential using bioinformatics tools and databases, followed by targeted experimental verification and exploration of the metabolic and regulatory network properties, are the present challenges that should lead to improved exploitation of these versatile food bacteria.

Adaptation, Biological↗
PubMed2026

Sparse Logistic Regression on Genomic Data for Prediction of Tumour Pathological Subtype.

The correct prediction of tumour subtype is critical for the treatment of cancer patients to maximise the chance of survival. The patients' genomic information, such as copy number alterations (CNA) profile, has increasingly become an important factor in the prediction to supplement the traditional pathological subtyping. The incorporation of the CNA information in a prediction model, such as logistic regression, faces two major statistical challenges: first, how to estimate the model parameters in the thousands and, second, how to deal with the correlation of CNA between genomic regions. To address them, we propose a sparse logistic regression model with random effects where some of its parameters are estimated to zero while the other parameters are non-zero. In effect, a variable selection is embedded in the modelling. To deal with the correlation of CNA across genomic regions, we extend further the model to incorporate an additional penalty in the corresponding likelihood function in the logistic regression. The results show that we can identify selected genomic regions that are informative to distinguish different tumour subtypes, while giving a good prediction ability. We illustrate the methodology using CNA dataset from a lung cancer cohort.

Journal Article↗
PubMed2006

Testing the neutral theory of molecular evolution using genomic data: a comparison of the human and bovine transcriptome.

Despite growing evidence of rapid evolution in protein coding genes, the contribution of positive selection to intra- and interspecific differences in protein coding regions of the genome is unclear. We attempted to see if genes coding for secreted proteins and genes with narrow expression, specifically those preferentially expressed in the mammary gland, have diverged at a faster rate between domestic cattle (Bos taurus) and humans (Homo sapiens) than other genes and whether positive selection is responsible. Using a large data set, we identified groups of genes based on secretion and expression patterns and compared them for the rate of nonsynonymous (dN) and synonymous (dS) substitutions per site and the number of radical (Dr) and conservative (Dc) amino acid substitutions. We found evidence of rapid evolution in genes with narrow expression, especially for those expressed in the liver and mammary gland and for genes coding for secreted proteins. We compared common human polymorphism data with human-cattle divergence and found that genes with high evolutionary rates in human-cattle divergence also had a large number of common human polymorphisms. This argues against positive selection causing rapid divergence in these groups of genes. In most cases dN/dS ratios were lower in human-cattle divergence than in common human polymorphism presumably due to differences in the effectiveness of purifying selection between long-term divergence and short-term polymorphism.

Animals↗
PubMed2000

Finding pathogenicity islands and gene transfer events in genome data.

MOTIVATION: There is a growing literature on wavelet theory and wavelet methods showing improvements on more classical techniques, especially in the contexts of smoothing and extraction of fundamental components of signals. G+C patterns occur at different lengths (scales) and, for this reason, G+C plots are usually difficult to interpret. Current methods for genome analysis choose a window size and compute a chi(2) statistics of the average value for each window with respect to the whole genome. RESULTS: Firstly, wavelets are used to smooth G+C profiles to locate characteristic patterns in genome sequences. The method we use is based on performing a chi(2) statistics on the wavelet coefficients of a profile; thus we do not need to choose a fixed window size, in that the smoothing occurs at a set of different scales. Secondly, a wavelet scalogram is used as a measure for sequence profile comparison; this tool is very general and can be applied to other sequence profiles commonly used in genome analysis. We show applications to the analysis of Deinococcus radiodurans chromosome I, of two strains of Helicobacter pylori (26695, J99) and two of Neisseria meningitidis (serogroup B strain MC58 and serogroup A strain Z2491). We report a list of loci that have different G+C content with respect to the nearby regions; the analysis of N. meningitidis serogroup B shows two new large regions with low G+C content that are putative pathogenicity islands. AVAILABILITY: Software and numerical results (profiles, scalograms, high and low frequency components) for all the genome sequences analyzed are available upon request from the authors.

Base Composition↗
PubMed2003

Genomic data sampling and its effect on classification performance assessment.

BACKGROUND: Supervised classification is fundamental in bioinformatics. Machine learning models, such as neural networks, have been applied to discover genes and expression patterns. This process is achieved by implementing training and test phases. In the training phase, a set of cases and their respective labels are used to build a classifier. During testing, the classifier is used to predict new cases. One approach to assessing its predictive quality is to estimate its accuracy during the test phase. Key limitations appear when dealing with small-data samples. This paper investigates the effect of data sampling techniques on the assessment of neural network classifiers. RESULTS: Three data sampling techniques were studied: Cross-validation, leave-one-out, and bootstrap. These methods are designed to reduce the bias and variance of small-sample estimations. Two prediction problems based on small-sample sets were considered: Classification of microarray data originating from a leukemia study and from small, round blue-cell tumours. A third problem, the prediction of splice-junctions, was analysed to perform comparisons. Different accuracy estimations were produced for each problem. The variations are accentuated in the small-data samples. The quality of the estimates depends on the number of train-test experiments and the amount of data used for training the networks. CONCLUSION: The predictive quality assessment of biomolecular data classifiers depends on the data size, sampling techniques and the number of train-test experiments. Conservative and optimistic accuracy estimations can be obtained by applying different methods. Guidelines are suggested to select a sampling technique according to the complexity of the prediction problem under consideration.

Computational Biology↗