AIMC Topic: Transcriptome
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Image-based deep learning identifies glioblastoma risk groups with genomic and transcriptomic heterogeneity: a multi-center study.
European radiology
Aug 24, 2022
OBJECTIVES: To develop and validate a deep learning imaging signature (DLIS) for risk stratification in patients with multiforme (GBM), and to investigate the biological pathways and genetic alterations underlying the DLIS.
Model-free prediction test with application to genomics data.
Proceedings of the National Academy of Sciences of the United States of America
Aug 15, 2022
Testing the significance of predictors in a regression model is one of the most important topics in statistics. This problem is especially difficult without any parametric assumptions on the data. This paper aims to test the null hypothesis that give...
Assessment and Optimization of Explainable Machine Learning Models Applied to Transcriptomic Data.
Genomics, proteomics & bioinformatics
Aug 3, 2022
Explainable artificial intelligence aims to interpret how machine learning models make decisions, and many model explainers have been developed in the computer vision field. However, understanding of the applicability of these model explainers to bio...
DeepPlnc: Bi-modal deep learning for highly accurate plant lncRNA discovery.
Genomics
Aug 2, 2022
We present here a bi-modal CNN based deep-learning system, DeepPlnc, to identify plant lncRNAs with high accuracy while using sequence and structural properties. Unlike most of the existing software, it works accurately even in conditions with ambigu...
A miRNA Target Prediction Model Based on Distributed Representation Learning and Deep Learning.
Computational and mathematical methods in medicine
Jul 25, 2022
MicroRNAs (miRNAs) are a kind of noncoding RNA, which plays an essential role in gene regulation by binding to messenger RNAs (mRNAs). Accurate and rapid identification of miRNA target genes is helpful to reveal the mechanism of transcriptome regulat...
A deep learning model to classify neoplastic state and tissue origin from transcriptomic data.
Scientific reports
Jun 11, 2022
Application of deep learning methods to transcriptomic data has the potential to enhance the accuracy and efficiency of tissue classification and cell state identification. Herein, we developed a multitask deep learning model for tissue classificatio...
Simulating the restoration of normal gene expression from different thyroid cancer stages using deep learning.
BMC cancer
Jun 4, 2022
BACKGROUND: Thyroid cancer (THCA) is the most common endocrine malignancy and incidence is increasing. There is an urgent need to better understand the molecular differences between THCA tumors at different pathologic stages so appropriate diagnostic...
Metabonomic and transcriptomic analyses of glycosides tablet-induced hepatotoxicity in rats.
Drug and chemical toxicology
May 23, 2022
We aimed to explore novel biomarkers involved in alterations of metabolism and gene expression related to the hepatotoxic effects of glycosides tablet (TGT) in rats. Rats were randomly divided into groups based on oral administration of TGTs for 6 w...
Cell cycle gene regulation dynamics revealed by RNA velocity and deep-learning.
Nature communications
May 23, 2022
Despite the fact that the cell cycle is a fundamental process of life, a detailed quantitative understanding of gene regulation dynamics throughout the cell cycle is far from complete. Single-cell RNA-sequencing (scRNA-seq) technology gives access to...
Identifying common transcriptome signatures of cancer by interpreting deep learning models.
Genome biology
May 17, 2022
BACKGROUND: Cancer is a set of diseases characterized by unchecked cell proliferation and invasion of surrounding tissues. The many genes that have been genetically associated with cancer or shown to directly contribute to oncogenesis vary widely bet...
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