Generating automated kidney transplant biopsy reports combining molecular measurements with ensembles of machine learning classifiers
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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:The physical manifestations of memory formation and recall are fundamental questions that remain unresolved. At the cellular level, ensembles of neurons called engrams are activated by learning events and control memory recall. Astrocytes are in close proximity to neurons and engage in a range of activities that support neurotransmission and circuit plasticity. Moreover, astrocytes exhibit experience dependent plasticity; however whether specific ensembles of astrocytes participate in memory recall remains obscure. Here we show that learning events induce c-Fos expression in a subset of hippocampal astrocytes, which subsequently regulates hippocampal circuit function. Intersectional, c-Fos based labeling of these astrocyte ensembles after learning events reveals that they are closely affiliated with engram neurons, while re-activation of these astrocyte ensembles stimulates memory recall. At the molecular level, these astrocyte ensembles exhibit elevated expression of NFIA and its selective deletion from this population suppresses memory recall. Together, our studies identify learning-associated astrocyte ensembles as a new form of plasticity that is sufficient to provoke memory recall, while implicating astrocytes as a reservoir for the storage of memories.
2024-06-30 | GSE254016 | 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:BACKGROUND: Arabidopsis thaliana is the model species of current plant genomic research with a genome size of 125 Mb and approximately 28,000 genes. The function of half of these genes is currently unknown. The purpose of this study is to infer gene function in Arabidopsis using machine-learning algorithms applied to large-scale gene expression data sets, with the goal of identifying genes that are potentially involved in plant response to abiotic stress. RESULTS: Using in house and publicly available data, we assembled a large set of gene expression measurements for A. thaliana. Using those genes of known function, we first evaluated and compared the ability of basic machine-learning algorithms to predict which genes respond to stress. Predictive accuracy was measured using ROC50 and precision curves derived through cross validation. To improve accuracy, we developed a method for combining these classifiers using a weighted-voting scheme. The combined classifier was then trained on genes of known function and applied to genes of unknown function, identifying genes that potentially respond to stress. Visual evidence corroborating the predictions was obtained using electronic Northern analysis. Three of the predicted genes were chosen for biological validation. Gene knockout experiments confirmed that all three are involved in a variety of stress responses. The biological analysis of one of these genes (At1g16850) is presented here, where it is shown to be necessary for the normal response to temperature and NaCl. CONCLUSION: Supervised learning methods applied to large-scale gene expression measurements can be used to predict gene function. However, the ability of basic learning methods to predict stress response varies widely and depends heavily on how much dimensionality reduction is used. Our method of combining classifiers can improve the accuracy of such predictions - in this case, predictions of genes involved in stress response in plants - and it effectively chooses the appropriate amount of dimensionality reduction automatically. The method provides a useful means of identifying genes in A. thaliana that potentially respond to stress, and we expect it would be useful in other organisms and for other gene functions.
| 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:PurposeTo compare gradient-boosting classifier (GBC) analysis of optical coherence tomography angiography (OCTA)-measured vessel density (VD) and OCT-measured tissue thickness to standard OCTA VD and OCT thickness parameters for classifying healthy eyes and eyes with early to moderate glaucoma.DesignComparison of diagnostic tools.MethodsA total of 180 healthy eyes and 193 glaucomatous eyes with OCTA and OCT imaging of the macula and optic nerve head (ONH) were studied. Four GBCs were evaluated that combined 1) all macula VD and thickness measurements (Macula GBC), 2) all ONH VD and thickness measurements (ONH GBC), 3) all VD measurements from the macula and ONH (vessel density GBC), and 4) all thickness measurements from the macula and ONH (thickness GBC). ROC curve (AUROC) analyses compared the diagnostic accuracy of GBCs to that of standard instrument-provided parameters. A fifth GBC that combined all parameters (full GBC) also was investigated.ResultsGBCs had better diagnostic accuracy than standard OCTA and OCT parameters with AUROCs ranging from 0.90 to 0.93 and 0.64 to 0.91, respectively. The full GBC (AUROC = 0.93) performed significantly better than the ONH GBC (AUROC = 0.91; P = .036) and the vessel density GBC (AUROC = 0.90; P = .010). All other GBCs performed similarly. The mean relative influence of each parameter included in the full GBC identified a combination of macular thickness and ONH VD measurements as the greatest contributors.ConclusionsGBCs that combine OCTA and OCT macula and ONH measurements can improve diagnostic accuracy for glaucoma detection compared to most but not all instrument provided parameters.
| S-EPMC7492367 | biostudies-literature
Project description:Learning-Associated Astrocyte Ensembles Regulate Memory Recall
| PRJNA1068117 | ENA
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: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