AIMC Topic: Gene Expression Profiling
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A novel approach for the analysis of time-course gene expression data based on computing with words.
Journal of biomedical informatics
Jul 14, 2021
In this paper, a novel approach is proposed for the analysis of time-course gene expression data based on the path-breaking work of Zadeh, Computing with Words. This method can automatically discover the patterns of temporal gene expression profile i...
An active learning approach for clustering single-cell RNA-seq data.
Laboratory investigation; a journal of technical methods and pathology
Jul 9, 2021
Single-cell RNA sequencing (scRNA-seq) data has been widely used to profile cellular heterogeneities with a high-resolution picture. Clustering analysis is a crucial step of scRNA-seq data analysis because it provides a chance to identify and uncover...
An Unbiased Machine Learning Exploration Reveals Gene Sets Predictive of Allograft Tolerance After Kidney Transplantation.
Frontiers in immunology
Jul 8, 2021
Efforts at finding potential biomarkers of tolerance after kidney transplantation have been hindered by limited sample size, as well as the complicated mechanisms underlying tolerance and the potential risk of rejection after immunosuppressant withdr...
Transplantation Tolerance
Graft Rejection
Patient Selection
Oligonucleotide Array Sequence Analysis
Transcriptome
Clinical Decision-Making
Humans
Treatment Outcome
Time Factors
Risk Factors
Machine Learning
Graft Survival
Risk Assessment
Databases, Genetic
Kidney Transplantation
Gene Expression Profiling
Immunosuppressive Agents
Predictive Value of Tests
scGCN is a graph convolutional networks algorithm for knowledge transfer in single cell omics.
Nature communications
Jun 22, 2021
Single-cell omics is the fastest-growing type of genomics data in the literature and public genomics repositories. Leveraging the growing repository of labeled datasets and transferring labels from existing datasets to newly generated datasets will e...
Application of machine learning to large in-vitro databases to identify cancer cell characteristics: telomerase reverse transcriptase (TERT) expression.
Oncogene
Jun 16, 2021
Advances in biotechnology and machine learning have created an enhanced environment for unearthing and exploiting previously unrecognized relationships between genomic and epigenetic data with potential therapeutic implications. We applied advanced a...
Leveraging supervised learning for functionally informed fine-mapping of cis-eQTLs identifies an additional 20,913 putative causal eQTLs.
Nature communications
Jun 7, 2021
The large majority of variants identified by GWAS are non-coding, motivating detailed characterization of the function of non-coding variants. Experimental methods to assess variants' effect on gene expressions in native chromatin context via direct ...
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...
MichiGAN: sampling from disentangled representations of single-cell data using generative adversarial networks.
Genome biology
May 20, 2021
Deep generative models such as variational autoencoders (VAEs) and generative adversarial networks (GANs) generate and manipulate high-dimensional images. We systematically assess the complementary strengths and weaknesses of these models on single-c...
Survival prediction and treatment optimization of multiple myeloma patients using machine-learning models based on clinical and gene expression data.
Leukemia
May 18, 2021
Multiple myeloma (MM) remains mostly an incurable disease with a heterogeneous clinical evolution. Despite the availability of several prognostic scores, substantial room for improvement still exists. Promising results have been obtained by integrati...
Discovery of primary prostate cancer biomarkers using cross cancer learning.
Scientific reports
May 17, 2021
Prostate cancer (PCa), the second leading cause of cancer death in American men, is a relatively slow-growing malignancy with multiple early treatment options. Yet, a significant number of low-risk PCa patients are over-diagnosed and over-treated wit...
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