AIMC Topic: Transcriptome
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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 ...
Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance.
Nature communications
May 11, 2021
Resistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitivity is not currently implemented clinically due to insufficient accuracy of existing machine learnin...
Schema: metric learning enables interpretable synthesis of heterogeneous single-cell modalities.
Genome biology
May 3, 2021
A complete understanding of biological processes requires synthesizing information across heterogeneous modalities, such as age, disease status, or gene expression. Technological advances in single-cell profiling have enabled researchers to assay mul...
Machine learning-assisted high-content analysis of pluripotent stem cell-derived embryos in vitro.
Stem cell reports
Apr 22, 2021
Stem cell-based embryo models by cultured pluripotent and extra-embryonic lineage stem cells are novel platforms to model early postimplantation development. We showed that induced pluripotent stem cells (iPSCs) could form ITS (iPSCs and trophectoder...
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