AIMC Topic: Transcriptome
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Machine learning compensates fold-change method and highlights oxidative phosphorylation in the brain transcriptome of Alzheimer's disease.
Scientific reports
Jul 1, 2021
Alzheimer's disease (AD) is a neurodegenerative disorder causing 70% of dementia cases. However, the mechanism of disease development is still elusive. Despite the availability of a wide range of biological data, a comprehensive understanding of AD's...
The Immune Subtypes and Landscape of Gastric Cancer and to Predict Based on the Whole-Slide Images Using Deep Learning.
Frontiers in immunology
Jun 28, 2021
BACKGROUND: Gastric cancer (GC) is a highly heterogeneous tumor with different responses to immunotherapy. Identifying immune subtypes and landscape of GC could improve immunotherapeutic strategies.
Prediction of drug efficacy from transcriptional profiles with deep learning.
Nature biotechnology
Jun 17, 2021
Drug discovery focused on target proteins has been a successful strategy, but many diseases and biological processes lack obvious targets to enable such approaches. Here, to overcome this challenge, we describe a deep learning-based efficacy predicti...
Novel deep learning-based transcriptome data analysis for drug-drug interaction prediction with an application in diabetes.
BMC bioinformatics
Jun 11, 2021
BACKGROUND: Drug-drug interaction (DDI) is a serious public health issue. The L1000 database of the LINCS project has collected millions of genome-wide expressions induced by 20,000 small molecular compounds on 72 cell lines. Whether this unified and...
A deep learning approach to identify gene targets of a therapeutic for human splicing disorders.
Nature communications
Jun 7, 2021
Pre-mRNA splicing is a key controller of human gene expression. Disturbances in splicing due to mutation lead to dysregulated protein expression and contribute to a substantial fraction of human disease. Several classes of splicing modulator compound...
Data-driven approaches to advance research and clinical care for pediatric cancer.
Biochimica et biophysica acta. Reviews on cancer
May 26, 2021
Pediatric cancer is a rare disease with a distinct etiology and mutational landscape compared with adult cancer. Multi-omics profiling of retrospective and prospective cohorts coupled with innovative computational analysis have been instrumental in u...
A joint deep learning model enables simultaneous batch effect correction, denoising, and clustering in single-cell transcriptomics.
Genome research
May 25, 2021
Recent developments of single-cell RNA-seq (scRNA-seq) technologies have led to enormous biological discoveries. As the scale of scRNA-seq studies increases, a major challenge in analysis is batch effects, which are inevitable in studies involving hu...
Application of multi-omics data integration and machine learning approaches to identify epigenetic and transcriptomic differences between in vitro and in vivo produced bovine embryos.
PloS one
May 24, 2021
Pregnancy rates for in vitro produced (IVP) embryos are usually lower than for embryos produced in vivo after ovarian superovulation (MOET). This is potentially due to alterations in their trophectoderm (TE), the outermost layer in physical contact w...
Deep learning predicts gene expression as an intermediate data modality to identify susceptibility patterns in Mycobacterium tuberculosis infected Diversity Outbred mice.
EBioMedicine
May 14, 2021
BACKGROUND: Machine learning sustains successful application to many diagnostic and prognostic problems in computational histopathology. Yet, few efforts have been made to model gene expression from histopathology. This study proposes a methodology w...
CancerSiamese: one-shot learning for predicting primary and metastatic tumor types unseen during model training.
BMC bioinformatics
May 12, 2021
BACKGROUND: The state-of-the-art deep learning based cancer type prediction can only predict cancer types whose samples are available during the training where the sample size is commonly large. In this paper, we consider how to utilize the existing ...
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