Ontology highlight
ABSTRACT:
PROVIDER: PRJNA672779 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Automated and Machine Learning Guided Culturomics of Personalized Gut Microbiomes
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Project description:We report the application of single-cell RNA sequencing(scRNA-seq) in mouse monocyte cells by integrating scRNA-seq, transcriptionfactor binding motifs, and ATAC-seq data using machine learning. We generated scRNA-seqdata from mouse monocytes treated with PBS, SD-LPS, 4-PBA, and SD-LPS + 4-PBA tounderstand the gene regulatory networks of monocytes under the low-grade inflammatorycondition and the mechanism of action for 4-PBA. We find two novelsubpopulations of monocyte cells in response to SD-LPS. We show that 4-PBApotently reprograms an anti-inflammatory monocyte phenotype and masks theeffects of subclinical low dose LPS. Together with TF binding motifs and ATAC-seqdata, a machine learning method, using guided, regularized random forest (GRRF)and feature selection was developed to select the best candidate TFs that areinvolved in the activation of monocytes within different clusters. Our results suggestthat our new machine learning method can select candidate regulatory genes aspotential targets for developing new therapeutics against low-gradeinflammation.
2021-01-29 | GSE160450 | GEO
Project description:Development of a novel machine learning guided ctDNA detection platform for use in liquid biopsy detection and therapeutic monitoring of solid tumors in several clinical contexts. Included are WGS alignments from our study.
| EGAS00001007306 | EGA
Project description:Gene expression profiles were generated from 199 primary breast cancer patients. Samples 1-176 were used in another study, GEO Series GSE22820, and form the training data set in this study. Sample numbers 200-222 form a validation set. This data is used to model a machine learning classifier for Estrogen Receptor Status. RNA was isolated from 199 primary breast cancer patients. A machine learning classifier was built to predict ER status using only three gene features.
2013-01-01 | E-GEOD-29210 | biostudies-arrayexpress
Project description:Small non-coding RNAs can be secreted through a variety of mechanisms, including exosomal sorting, in small extracellular vesicles, and within lipoprotein complexes. However, the mechanisms that govern their sorting and secretion are still not well understood. In this study, we present ExoGRU, a machine learning model that predicts small RNA secretion probabilities from primary RNA sequence. We experimentally validated the performance of this model through ExoGRU-guided mutagenesis and synthetic RNA sequence analysis, and confirmed that primary RNA sequence is a major determinant in small RNA secretion. Additionally, we used ExoGRU to reveal cis and trans factors that underlie small RNA secretion, including known and novel RNA-binding proteins, e.g., YBX1, HNRNPA2B1, and RBM24. We also developed a novel technique called exoCLIP, which reveals the RNA interactome of RBPs within the cell-free space. We used exoCLIP to reveal the RNA interactome of HNRNPA2B1 and RBM24 in extracellular vesicles. Together, our results demonstrate the power of machine learning in revealing novel biological mechanisms. In addition to providing deeper insight into complex processes such as small RNA secretion, this knowledge can be leveraged in therapeutic and synthetic biology applications.
2024-01-28 | GSE230006 | GEO
Project description:Small non-coding RNAs can be secreted through a variety of mechanisms, including exosomal sorting, in small extracellular vesicles, and within lipoprotein complexes. However, the mechanisms that govern their sorting and secretion are still not well understood. In this study, we present ExoGRU, a machine learning model that predicts small RNA secretion probabilities from primary RNA sequence. We experimentally validated the performance of this model through ExoGRU-guided mutagenesis and synthetic RNA sequence analysis, and confirmed that primary RNA sequence is a major determinant in small RNA secretion. Additionally, we used ExoGRU to reveal cis and trans factors that underlie small RNA secretion, including known and novel RNA-binding proteins, e.g., YBX1, HNRNPA2B1, and RBM24. We also developed a novel technique called exoCLIP, which reveals the RNA interactome of RBPs within the cell-free space. We used exoCLIP to reveal the RNA interactome of HNRNPA2B1 and RBM24 in extracellular vesicles. Together, our results demonstrate the power of machine learning in revealing novel biological mechanisms. In addition to providing deeper insight into complex processes such as small RNA secretion, this knowledge can be leveraged in therapeutic and synthetic biology applications.
2024-01-28 | GSE230007 | GEO
Project description:Machine learning methods, particularly neural networks trained on large datasets, are transforming how scientists approach scientific discovery and experimental design. However, current state-of-the-art neural networks are limited by their uninterpretability: despite providing accurate predictions, they cannot describe how they arrived at their predictions. Here, using an ``interpretable-by-design'' approach, we present a neural network model that provides insights into RNA splicing, a fundamental process in the transfer of genomic information into functional biochemical products. Although we designed our model to emphasize interpretability, its predictive accuracy is on par with state-of-the-art models. To demonstrate the model's interpretability, we introduce a visualization that, for any given exon, allows us to trace and quantify the entire decision process from input sequence to output splicing prediction. Importantly, the model revealed novel components of the splicing logic, which we experimentally validated. This study highlights how interpretable machine learning can advance scientific discovery.
2022-10-01 | GSE200096 | GEO
Project description:Large-scale serum miRNomics in combination with machine learning could lead to the development of a blood-based cancer classification system.
2022-11-12 | GSE211692 | GEO