AIMC Topic: Transcriptome
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Machine learning predicts stem cell transplant response in severe scleroderma.
Annals of the rheumatic diseases
Sep 15, 2020
OBJECTIVE: The Scleroderma: Cyclophosphamide or Transplantation (SCOT) trial demonstrated clinical benefit of haematopoietic stem cell transplant (HSCT) compared with cyclophosphamide (CYC). We mapped PBC (peripheral blood cell) samples from the SCOT...
A gene prioritization method based on a swine multi-omics knowledgebase and a deep learning model.
Communications biology
Sep 10, 2020
The analyses of multi-omics data have revealed candidate genes for objective traits. However, they are integrated poorly, especially in non-model organisms, and they pose a great challenge for prioritizing candidate genes for follow-up experimental v...
Prediction Power on Cardiovascular Disease of Neuroimmune Guidance Cues Expression by Peripheral Blood Monocytes Determined by Machine-Learning Methods.
International journal of molecular sciences
Sep 2, 2020
Atherosclerosis is the underlying pathology in a major part of cardiovascular disease, the leading cause of mortality in developed countries. The infiltration of monocytes into the vessel walls of large arteries is a key denominator of atherogenesis,...
Machine learning based refined differential gene expression analysis of pediatric sepsis.
BMC medical genomics
Aug 28, 2020
BACKGROUND: Differential expression (DE) analysis of transcriptomic data enables genome-wide analysis of gene expression changes associated with biological conditions of interest. Such analysis often provides a wide list of genes that are differentia...
Progressive Multiple Sclerosis Transcriptome Deconvolution Indicates Increased M2 Macrophages in Inactive Lesions.
European neurology
Aug 28, 2020
Accumulating evidence suggests M2 macrophages contribute to tissue reparation and limit inflammation in multiple sclerosis (MS). However, most studies have focused on murine models without substantial support through human MS observations. The presen...
Biomarkers of the Response to Immune Checkpoint Inhibitors in Metastatic Urothelial Carcinoma.
Frontiers in immunology
Aug 25, 2020
The mechanisms underlying the resistance to immune checkpoint inhibitors (ICIs) therapy in metastatic urothelial carcinoma (mUC) patients are not clear. It is of great significance to discern mUC patients who could benefit from ICI therapy in clinica...
Predictive Value of Tests
Carcinoma
Drug Resistance, Neoplasm
Urothelium
Treatment Outcome
Clinical Decision-Making
Male
Antibodies, Monoclonal, Humanized
Female
Risk Assessment
Urinary Bladder Neoplasms
Risk Factors
Gene Expression Profiling
Biomarkers, Tumor
Transcriptome
Humans
Time Factors
Machine Learning
Immune Checkpoint Inhibitors
Decision Support Techniques
B7-H1 Antigen
Nomograms
Classifying Breast Cancer Subtypes Using Deep Neural Networks Based on Multi-Omics Data.
Genes
Aug 4, 2020
With the high prevalence of breast cancer, it is urgent to find out the intrinsic difference between various subtypes, so as to infer the underlying mechanisms. Given the available multi-omics data, their proper integration can improve the accuracy o...
Machine learning-based classification and diagnosis of clinical cardiomyopathies.
Physiological genomics
Aug 3, 2020
Dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM) are two common types of cardiomyopathies leading to heart failure. Accurate diagnostic classification of different types of cardiomyopathies is critical for precision medicine in clinical...
Repurpose Open Data to Discover Therapeutics for COVID-19 Using Deep Learning.
Journal of proteome research
Jul 24, 2020
There have been more than 2.2 million confirmed cases and over 120 000 deaths from the human coronavirus disease 2019 (COVID-19) pandemic, caused by the novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), in the United States alone. How...
Community Assessment of the Predictability of Cancer Protein and Phosphoprotein Levels from Genomics and Transcriptomics.
Cell systems
Jul 24, 2020
Cancer is driven by genomic alterations, but the processes causing this disease are largely performed by proteins. However, proteins are harder and more expensive to measure than genes and transcripts. To catalyze developments of methods to infer pro...
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