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492 results for “sequence modeling”
Discovering molecular regulators of ageing using mixture models with RNA-sequencing data
<p>Identifying the molecular regulators that control ageing is challenging because the ageing process is influenced by a combination of genetic and environmental factors which makes it difficult to source the contribution of a single gene. Multiple studies have demonstrated that as humans age, increased gene expression heterogeneity results in the dysregulation of key regulators and pathways. Given the dynamic nature of gene expression, it is vital that this data be modelled by statistical approaches that can appropriately account for changes in variability to understand the contribution of heterogeneity during the aging process and properly identify its regulators. This study demonstrates the utility of using mixture models to model biological variability of gene expression occurring during ageing and how novel potential regulators of ageing can be identified.</p> <p>Our mixture modelling approach was applied to gene expression data from the Genotype-Tissue Expression (GTEx) cohort. For every gene, the expression profile was modelled using a mixture model across the cohort where the subset of donors corresponding to each mode was tested for a significant change in age group. The multi-tissue aspect of GTEx was leveraged to find ageing regulators based on this mixture model approach genes that were common across multiple tissues, suggesting that the regulation of ageing may also be controlled through a set of genes that have non-tissue-specific activity.</p> <p>Our approach identified well-documented ageing regulators <em>mTOR </em>and <em>RICTOR</em> and other potential ageing regulators such as <em>IL4</em> and <em>GPR4</em> which were detected only by our approach. Genes identified by edgeR, DESeq2 and the mixture model-based approach were enriched for similar biological pathways. This suggests that while the specific ageing regulators identified from our approach may be distinct, they generally belong in the same pathways as the genes identified by standard approaches. Overall, these results indicate that modelling gene expression variability using mixture models in conjunction with standard differential gene expression can help uncover new regulators that have a potential role for understanding human ageing.</p> <p>I</p>
PredictION: A predictive model to establish the performance of Oxford sequencing reads of SARS-CoV-2
<p>Dataset (1) that included 1461 samples and a dataset (2) with 471 samples that was a subset of dataset 1 that included Number of sequenced reads per genome, CT (Cycle threshold) value [N2 target gene], mean coverage depth, coverage genome (percentage), and quantification cDNA (ng/µl).</p>
Ranger models for predicting isoform abundance from UTR sequence features
<p>Each RDS file contains a ranger object, trained on transcripts after removing those associated with the held-out genes in one of the five cross-validation folds. The day and replicate number in the file name corresponds to the neuronal differentiation sample on which the model was trained. The file gene_folds.txt indicates the fold from which each gene was excluded during model training. The file transcript_gene_associations.txt contains transcript-gene associations. The file predictors.RDS contains the matrix of predictor variables.</p>
Evolution models of helium white dwarf-main-sequence star merger remnants: the mass distribution of single low-mass white dwarfs
<p>Inlists and data for "<a href="https://ui.adsabs.harvard.edu/#abs/2018MNRAS.474..427Z/abstract">Evolution models of helium white dwarf-main-sequence star merger remnants: the mass distribution of single low-mass white dwarfs</a>"</p>
Spectral models for binary products: Unifying subdwarfs and Wolf-Rayet stars as a sequence of stripped-envelope stars
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/2018A&A...615A..78G/abstract">Götberg et al. (2018)</a>. MESA version 8118.</p> <p>Publication DOI: <a href="https://doi.org/10.1051/0004-6361/201732274">10.1051/0004-6361/201732274</a></p>
Geodetic model of the March 2021 Thessaly seismic sequence inferred from seismological and InSAR data
<p>A selection of Sentinel-1 (S1) wrapped and unwrapped measurements used in this study (from "a" to "u" files in tiff format as indicated in the word file attached). S1 data were processed by using our own internally developed InSAR processing chain.<br> <br> Earthquakes data locations.</p> <p><br> </p> <p> </p>
Training data for the Sei framework sequence model
<p>Training data for the Sei framework deep learning model. The data contains chromatin profiles from the Cistrome Project: <strong>please agree to the terms of usage at the Cistrome Project (http://cistrome.org/db/#/bdown) before downloading. </strong></p>
Long reads and Hi-C sequencing illuminate the two-compartment genome of the model arbuscular mycorrhizal symbiont Rhizophagus irregularis
<p>This repository contains annotations for the strains of <em>R. irregularis</em> chromosome assemblies.</p>
Data for "Unsupervised learning of sequence-specific aggregation behavior for a model copolymer"
<p>These are the data associated with the paper, "Unsupervised learning of sequence-specific aggregation behavior for a model copolymer" (DOI 10.1039/D1SM01012C). Each of the directories contains subdirectories with `GSD` files dumped from HOOMD. Each subdirectory roughly corresponds to one or two of the figures in the paper.</p>
Saved model and preprocessed data for "CRMnet:a deep learning model for predicting gene expression from large regulatory sequence datasets"
<p>Saved TUNet model and preprocessed training data for "CRMnet: a deep learning model for predicting gene expression from large regulatory sequence datasets"</p> <p>To load the trained model:</p> <pre><code class="language-python">import tensorflow as tf tf.keras.models.load_model("path to the model folder")</code></pre> <p>for more information please find our repository: https://github.com/jiayuwen/CRMnet</p>
Modeling the Sequence Dependence of Differential Antibody Binding in the Immune Response to Infectious Disease
<p>Raw peptide microarray data of fluorescence intensities representing relative binding of antibodies in sera samples collected from different cohorts of patients diagnosed with a number of viral infections. Healthy controls are also included.</p> <p>Columns:</p> <p>Sequence - peptide sequence</p> <p>HCV - Hepatitis Virus C</p> <p>Dengue - Dengue virus</p> <p>WNV - West Nile Virus</p> <p>HBV - Hepatitis Virus B</p> <p>Chagas - Chagas disease</p> <p>ND - negative/healthy donor</p> <p>LowCV - low coefficient of variation</p> <p>HighCV - high coefficient of variation</p>
Models and Data associated with: Single-cell gene expression prediction from DNA sequence at large contexts
<p>This archive holds trained models and associated data for the <a href="https://www.biorxiv.org/content/10.1101/2023.07.26.550634v1">manuscript</a>:<br> "Single-cell gene expression prediction from DNA sequence at large contexts"</p> <p>Structure:</p> <ul> <li>configs - example configs for the workflows to produce publication data </li> <li>data_* - pre-processed single cell data used for publication</li> <li>models_* - model checkpoints, hyperparameters and training progress in tensorboard logs</li> <li>preprocessing - additional data required to reproduce the pre-processing workflow</li> </ul> <p> </p> <p>"Copyright 2023 GlaxoSmithKline Research & Development Limited. All rights reserved."</p>
Colonization history of the Canary Islands endemic Lavatera acerifolia, (Malvaceae) unveiled with Genotyping-by-Sequencing data and niche modeling
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Generation of synthetic whole-slide image tiles of tumours from RNA-sequencing data via cascaded diffusion models
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Visitation sequence data from: Alternative flowers affect model and mimic flower discrimination performance of bumble bees
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Data from: Interaction of sequence data and paleogeographic priors in biogeographic dating: How could biological data inform time-constrained geological models?
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Supplementary table, figures and DNA sequences of sorghum gene models SbiRTx430.01G455400 and SbiRTx.02G006600 that feature primers, gRNAs and indels created
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Sequence-dependent model of genes with dual σ factor preference
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Single-Cell RNA-sequencing of neural precursor cells from an Alzheimer's mouse model, wild-type mice, and Alzheimer's mice rescued with Usp16 haploinsufficiency
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Data for: Accurate sequence-to-affinity models for SH2 domains from multi-round peptide binding assays coupled with free-energy regression
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.