Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

DeepTFactor, a deep learning-based tool for the identification of transcription factors

ABSTRACT: DeepTFactor, a deep learning-based tool for the identification of transcription factors

PROVIDER: PRJNA666186 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR12732981.fastq.gz	Fastqsanger.gz
	SRR12732982.fastq.gz	Fastqsanger.gz
	SRR12732983.fastq.gz	Fastqsanger.gz
	SRR12732984.fastq.gz	Fastqsanger.gz
	SRR12732985.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 6

Similar Datasets

DeepTFactor, a deep learning-based tool for the identification of transcription factors

Project description:We report the development of a deep learning-based tool, DeepTFactor, that predicts whether a protein of question is a transcription factor. DeepTFactor uses a convolutional neural network to extract features of protein sequences. We characterized the genome-wide binding sites of three TFs (i.e., YqhC, YiaU, and YahB), which are predicted by DeepTFactor

2020-10-09 | GSE158683 | GEO

Easy-Prime: a machine learning–based prime editor design tool

Project description:Easy-Prime: a machine learning–based prime editor design tool

| PRJNA734350 | ENA

DeNovo Peptide Identification Deep Learning Test Set

Project description:A set of bottom-up proteomics data for testing the deep learning network trained with data in PXD010000

2022-09-25 | PXD010613 | Pride

Solo: doublet identification via semi-supervised deep learning

Project description:Solo: doublet identification via semi-supervised deep learning

| PRJNA589061 | ENA

Solo: doublet identification via semi-supervised deep learning

Project description:We developed a semi-supervised deep learning framework for the identification of doublets in scRNA-seq analysis called Solo. To validate our method, we used MULTI-seq, cholesterol modified oligos (CMOs), to experimentally identify doublets in a solid tissue with diverse cell types, mouse kidney, and showed Solo recapitulated experimentally identified doublets.

2019-11-13 | GSE140262 | GEO

DeNovo Peptide Identification Deep Learning Training Set

Project description:A benchmark set of bottom-up proteomics data for training deep learning networks. It has data from 51 organisms and includes nearly 1 million peptides.

2018-06-13 | PXD010000 | Pride

A Deep Learning Approach For Programmable RNA Switches

Project description:Engineered RNA elements are programmable tools capable of detecting small molecules, proteins, and nucleic acids. Predicting the behavior of these tools remains a challenge, a situation that could be addressed through enhanced pattern recognition from deep learning. Thus, we investigate Deep Neural Networks (DNN) to predict toehold switch function as a canonical riboswitch model in synthetic biology. To facilitate DNN training, we synthesized and characterized in vivo a dataset of 91,534 toehold switches spanning 23 viral genomes and 906 human transcription factors. DNNs trained on nucleotide sequences outperformed (R2=0.43-0.70) previous state-of-the-art thermodynamic and kinetic models (R2=0.04-0.15) and allowed for human-understandable attention-visualizations (VIS4Map) to identify success and failure modes. This deep learning approach constitutes a major step forward in engineering and understanding of RNA synthetic biology.

2020-08-12 | GSE149225 | GEO

COSMOS: A Platform for real-time morphology-based, label-free cell sorting using deep learning (scRNA-Seq)

Project description:Cells are the singular building blocks of life, and comprehensive understanding of morphology among other properties is crucial to assessment of underlying heterogeneity. We developed Computational Sorting and Mapping of Single Cells (COSMOS), a platform based on Artificial Intelligence (AI) and microfluidics to characterize and sort single cells based on real-time deep learning interpretation of high-resolution brightfield images. Supervised deep learning models were applied to characterize and sort cell lines and dissociated primary tissue based on high-dimensional embedding vectors of morphology without need for biomarker labels and stains/dyes. We demonstrate COSMOS capabilities with multiple human cell lines and tissue samples. These early results suggest that our neural networks embedding space can capture and recapitulate deep visual characteristics and can be used to efficiently purify unlabeled viable cells with desired morphological traits. Our approach resolves a technical gap in ability to perform real-time deep learning assessment and sorting of cells based on high-resolution brightfield images.

2023-09-25 | GSE241834 | GEO

A Deep Learning based Efficacy Prediction System for Drug Discovery [Trametinib]

Project description:Target protein-based drug and research compound discovery has been undeniably successful strategy in life science research, yet many diseases and biological processes lack obvious targets to enable these approaches. Here, to overcome this major challenge we have developed a deep-learning based efficacy prediction system (DLEPS) to identify potent agents to treat diverse diseases; DLEPS was trained using L1000 project chemical induced “changes of transcriptional profiles” (CTP) data as input. Strikingly, we found that the CTPs for previously unexamined molecules were precisely predicted (0.74 Pearson correlation coefficient). We used DLEPS to examine 4 disorders, and experimentally validated that perillen, chikusetsusaponin IV, trametinib, and liquiritin confer disease-relevant impacts against obesity, hyperuricemia, NASH, and COVID-19, respectively. Importantly, DLEPS also uncovered the biological insight that the MEK-ERK signaling pathway should be understood as a target for developing anti-NASH agents. Beyond illustrating that DLEPS is an effective tool for drug repurposing and development with diverse diseases (including those lacking targets), our study shows how diverse transcriptomics datasets can be harnessed to identify inhibitors and activator chemicals that can expand the scope of biological investigations well beyond mutant-bases analyses.

2023-03-31 | GSE165174 | GEO

A Deep Learning based Efficacy Prediction System for Drug Discovery [Perillen]

2023-03-31 | GSE165173 | GEO