AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Gene Expression

Showing 81 to 90 of 183 articles

Clear Filters

Uncovering the prognostic gene signatures for the improvement of risk stratification in cancers by using deep learning algorithm coupled with wavelet transform.

BMC bioinformatics
BACKGROUND: The aim of gene expression-based clinical modelling in tumorigenesis is not only to accurately predict the clinical endpoints, but also to reveal the genome characteristics for downstream analysis for the purpose of understanding the mech...

Putative cell type discovery from single-cell gene expression data.

Nature methods
We present the Single-Cell Clustering Assessment Framework, a method for the automated identification of putative cell types from single-cell RNA sequencing (scRNA-seq) data. By iteratively applying a machine learning approach to a given set of cells...

Artificial Intelligence Analysis of Gene Expression Data Predicted the Prognosis of Patients with Diffuse Large B-Cell Lymphoma.

The Tokai journal of experimental and clinical medicine
OBJECTIVE: We aimed to identify new biomarkers in Diffuse Large B-cell Lymphoma (DLBCL) using the deep learning technique.

Avocado: a multi-scale deep tensor factorization method learns a latent representation of the human epigenome.

Genome biology
The human epigenome has been experimentally characterized by thousands of measurements for every basepair in the human genome. We propose a deep neural network tensor factorization method, Avocado, that compresses this epigenomic data into a dense, i...

Graph embedding and unsupervised learning predict genomic sub-compartments from HiC chromatin interaction data.

Nature communications
Chromatin interaction studies can reveal how the genome is organized into spatially confined sub-compartments in the nucleus. However, accurately identifying sub-compartments from chromatin interaction data remains a challenge in computational biolog...

RefDNN: a reference drug based neural network for more accurate prediction of anticancer drug resistance.

Scientific reports
Cancer is one of the most difficult diseases to treat owing to the drug resistance of tumour cells. Recent studies have revealed that drug responses are closely associated with genomic alterations in cancer cells. Numerous state-of-the-art machine le...

Machine learning with autophagy-related proteins for discriminating renal cell carcinoma subtypes.

Scientific reports
Machine learning techniques have been previously applied for classification of tumors based largely on morphological features of tumor cells recognized in H&E images. Here, we tested the possibility of using numeric data acquired from software-based ...

Logistic regression paradigm for training a single-hidden layer feedforward neural network. Application to gene expression datasets for cancer research.

Journal of biomedical informatics
OBJECTIVE: The speed of the diagnosis process is vital in pursuing the trial of curing cancer. During the last decade, precision medicine evolved by detecting different types of cancer through microarrays (MA) of deoxyribonucleic acid (DNA) processed...

Seeking for potential pathogenic genes of major depressive disorder in the Gene Expression Omnibus database.

Asia-Pacific psychiatry : official journal of the Pacific Rim College of Psychiatrists
INTRODUCTION: Major depressive disorder (MDD) is one of the most common mental disorders worldwide. The aim of this study was to identify potential pathological genes in MDD.

Construction of arming Yarrowia lipolytica surface-displaying soybean seed coat peroxidase for use as whole-cell biocatalyst.

Enzyme and microbial technology
Whole-cell biocatalysts could be used in wide-ranging applications. In this study, a new kind of whole-cell biocatalyst was successfully constructed by genetically immobilizing soybean seed coat peroxidase (SBP) on the cell surface of Yarrowia lipoly...