Generating automated kidney transplant biopsy reports combining molecular measurements with ensembles of machine learning classifiers
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ABSTRACT:
PROVIDER: PRJNA511014 | ENA |
REPOSITORIES: ENA
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Project description:We previously reported a system for assessing rejection in kidney transplant biopsies using microarray-based gene expression data, the Molecular Microscope® Diagnostic System (MMDx). Ensembles generated diagnoses that were both more accurate than the best individual classifiers, and nearly as stable as the best, consistent with expectations from the machine learning literature. Human experts had ~93% agreement (balanced accuracy) signing out the reports, and random forest-based automated sign-outs showed similar levels of agreement with the human experts (92% and 94% for predicting the expert MMDx sign-outs for T cell-mediated (TCMR) and antibody-mediated rejection (ABMR) respectively). In most cases disagreements, whether between experts or between experts and automated sign-outs, were in biopsies near diagnostic thresholds. Considerable disagreement with histology persisted. The balanced accuracies of MMDx sign-outs for histology diagnoses of TCMR and ABMR were 73% and 78% respectively. Disagreement with histology is largely due to the known noise in histology assessments (ClinicalTrials.gov NCT01299168).
2019-11-07 | GSE124203 | GEO
Project description:Maladaptive reward seeking is a hallmark of cocaine use disorder. To develop therapeutic targets, it is critical to understand the neurobiological changes specific to cocaine-seeking without altering the seeking of natural rewards, e.g., sucrose. The prefrontal cortex (PFC) and the nucleus accumbens core (NAcore) are known regions associated with cocaine- and sucrose-seeking ensembles, i.e., a sparse population of co-activated neurons. Within ensembles, transcriptomic alterations in the PFC and NAcore underlie the learning and persistence of cocaine- and sucrose-seeking behavior. However, transcriptomes exclusively driving cocaine seeking independent from sucrose seeking have not yet been defined using a within-subject approach. Using Ai14:cFos-TRAP2 transgenic mice in a dual cocaine and sucrose self-administration model, we fluorescently sorted (FACS) and characterized (RNAseq) the transcriptomes defining cocaine- and sucrose-seeking ensembles. We found reward- and region-specific transcriptomic changes that will help develop clinically relevant genetic approaches to decrease cocaine-seeking behavior without altering non-drug reward-based positive reinforcement.
2023-11-08 | GSE247029 | GEO
Project description: Not available
| S-EPMC2213690 | biostudies-literature
Project description:Nuclear receptors function as ligand-regulated transcription factors whose ability to regulate diverse physiological processes is closely linked with conformational changes induced upon ligand binding. Understanding how conformational populations of nuclear receptors are shifted by various ligands could illuminate strategies for the design of synthetic modulators to regulate specific transcriptional programs. Here, we investigate ligand-induced conformational changes using a reconstructed, ancestral nuclear receptor. By making substitutions at a key position, we engineer receptor variants with altered ligand specificities. We use atomistic molecular dynamics (MD) simulations with enhanced sampling to generate ensembles of wildtype and engineered receptors in combination with multiple ligands, followed by conformational analysis and prediction of ligand activity. We combine cellular and biophysical experiments to allow correlation of MD-based predictions with functional ligand profiles, as well as elucidation of mechanisms underlying altered transcription in receptor variants. We determine that conformational ensembles accurately predict ligand responses based on observed population shifts, even within engineered receptors that were constitutively active or transcriptionally unresponsive in experiments. These studies provide a platform which will allow structural characterization of physiologically-relevant conformational ensembles, as well as provide the ability to design and predict transcriptional responses in novel ligands.
2022-10-12 | PXD036076 | Pride
Project description: Not available
| S-EPMC7492367 | biostudies-literature
Project description:Genome-scale deconvolution of RNA structure ensembles
| PRJNA663861 | ENA
Project description:Background: Development and application of transcriptomics-based gene classifiers for ecotoxicological applications lag far behind those of human biomedical science. Many such classifiers discovered thus far lack vigorous statistical and experimental validations, with their stability and reliability unknown. A combination of genetic algorithm/support vector machines and genetic algorithm/K nearest neighbors were used in this study to search for classifiers of endocrine disrupting chemicals (EDCs) in zebrafish. Searches were conducted on both tissue-specific and all tissue combined datasets, either across the entire transcriptome or within individual transcription factor (TF) networks previously linked to EDC effects. Candidate classifiers were evaluated by gene set enrichment analysis (GSEA) on both the original training data and a dedicated validation dataset. Results: Multi-tissue dataset yielded no classifiers. Among the 19 chemical-tissue conditions evaluated, the transcriptome-wide searches yielded classifiers for six of them, each having approximately 20 to 30 gene features unique to a condition. Searches within individual TF networks produced classifiers for 15 chemical-tissue conditions, each containing 100 or fewer top-ranked gene features pooled from those of multiple TF networks and also unique to each condition. For the training dataset, 10 out of 11 classifiers successfully identified the gene expression profiles (GEPs) of their intended chemical-tissue conditions by GSEA. For the validation dataset, classifiers for prochloraz-ovary and flutamide-ovary also correctly identified the GEPs of corresponding conditions while no classifier could predict the GEP from prochloraz-brain. Conclusions: The discrepancies in the performance of these classifiers were attributed in part to varying data complexity among the conditions, as measured to some degree by Fisher’s discriminant ratio statistic. This variation in data complexity could likely be compensated by adjusting sample size for individual chemical-tissue conditions, thus suggesting a need for a preliminary survey of transcriptomic responses before launching a full scale classifier discovery effort. While GSEA appeared to provide a flexible and effective tool for application of gene classifiers, a similar but more refined algorithm, connectivity mapping, should also be explored for ecotoxicological applications. The distribution characteristics of classifiers across tissues, chemicals, and TF networks suggested a differential biological impact among the EDCs on zebrafish transcriptome involving some basic cellular functions. chemical abbreviations: EE2, 17α-ethynyl estradiol; FAD, fadrozole; TRB, 17 -trenbolone; FIP, fipronil; PRO, prochloraz; FLU, flutamide; MUS, muscimol; KET, ketoconazole; TRI, trilostane; VIN, vinclozolin
2012-08-22 | GSE38070 | GEO
Project description:The stable formation of remote fear memories is thought to require neuronal gene induction in cortical ensembles that are activated during learning. However, the set of genes expressed specifically in these activated ensembles is not known; knowledge of such transcriptional profiles may offer insights into the molecular program underlying stable memory formation. Here we use RNA-Seq to identify genes whose expression is enriched in activated cortical ensembles labeled during associative fear learning. We first establish that mouse temporal association cortex (TeA) is required for remote recall of auditory fear memories. We then perform RNA-Seq in TeA neurons that are labeled by the activity reporter Arc-dVenus during learning. We identify 944 genes with enriched expression in Arc-dVenus+ neurons. These genes include markers of L2/3, L5b, and L6 excitatory neurons but not glial or inhibitory markers, confirming Arc-dVenus to be an excitatory neuron-specific, layer non-specific activity reporter. Cross comparisons to other transcriptional profiles show that 125 of the enriched genes are also activity-regulated in vitro or induced by visual stimulus in the visual cortex, suggesting that they may be induced generally in the cortex in an experience-dependent fashion. Prominent among the enriched genes are those encoding potassium channels that down-regulate neuronal activity, suggesting the possibility that part of the molecular program induced by fear conditioning may initiate homeostatic plasticity.
2016-08-31 | GSE85128 | GEO
Project description:Background: Development and application of transcriptomics-based gene classifiers for ecotoxicological applications lag far behind those of human biomedical science. Many such classifiers discovered thus far lack vigorous statistical and experimental validations, with their stability and reliability unknown. A combination of genetic algorithm/support vector machines and genetic algorithm/K nearest neighbors were used in this study to search for classifiers of endocrine disrupting chemicals (EDCs) in zebrafish. Searches were conducted on both tissue-specific and all tissue combined datasets, either across the entire transcriptome or within individual transcription factor (TF) networks previously linked to EDC effects. Candidate classifiers were evaluated by gene set enrichment analysis (GSEA) on both the original training data and a dedicated validation dataset. Results: Multi-tissue dataset yielded no classifiers. Among the 19 chemical-tissue conditions evaluated, the transcriptome-wide searches yielded classifiers for six of them, each having approximately 20 to 30 gene features unique to a condition. Searches within individual TF networks produced classifiers for 15 chemical-tissue conditions, each containing 100 or fewer top-ranked gene features pooled from those of multiple TF networks and also unique to each condition. For the training dataset, 10 out of 11 classifiers successfully identified the gene expression profiles (GEPs) of their intended chemical-tissue conditions by GSEA. For the validation dataset, classifiers for prochloraz-ovary and flutamide-ovary also correctly identified the GEPs of corresponding conditions while no classifier could predict the GEP from prochloraz-brain. Conclusions: The discrepancies in the performance of these classifiers were attributed in part to varying data complexity among the conditions, as measured to some degree by Fisher’s discriminant ratio statistic. This variation in data complexity could likely be compensated by adjusting sample size for individual chemical-tissue conditions, thus suggesting a need for a preliminary survey of transcriptomic responses before launching a full scale classifier discovery effort. While GSEA appeared to provide a flexible and effective tool for application of gene classifiers, a similar but more refined algorithm, connectivity mapping, should also be explored for ecotoxicological applications. The distribution characteristics of classifiers across tissues, chemicals, and TF networks suggested a differential biological impact among the EDCs on zebrafish transcriptome involving some basic cellular functions. chemical abbreviations: EE2, 17α-ethynyl estradiol; FAD, fadrozole; TRB, 17 -trenbolone; FIP, fipronil; PRO, prochloraz; FLU, flutamide; MUS, muscimol; KET, ketoconazole; TRI, trilostane; VIN, vinclozolin Since this study was conducted in several phases, three different version of Agilent zebrafish two color microarrays were used based on their availability at the time. These include G2518A (designID 013223) and G2519F (designID 015064, 019161). There were a total of 58 treatment conditions with various combinations of chemical, tissue type, exposure time, and gender. Each condition contained eight to 12 independent samples, half from chemical-treated fish and half from water- control fish.
2012-08-22 | E-GEOD-38070 | biostudies-arrayexpress
Project description: Not available
| S-EPMC2220009 | biostudies-literature