AIMC Topic: Gene Expression Profiling

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Cancer type and survival prediction based on transcriptomic feature map.

Computers in biology and medicine
This study achieved cancer type and survival time prediction by transforming transcriptomic features into feature maps and employing deep learning models. Using transcriptomic data from 27 cancer types and survival data from 10 types in the TCGA data...

Exploring the Latent Information in Spatial Transcriptomics Data via Multi-View Graph Convolutional Network Based on Implicit Contrastive Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Latest developments in spatial transcriptomics enable thoroughly profiling of gene expression while preserving tissue microenvironment. Connecting gene expression with spatial arrangement is key for precise spatial domain identification, enhancing th...

Upregulation of immune genes in the proliferative phase endometrium enables classification into women with recurrent pregnancy loss versus controls.

Human reproduction (Oxford, England)
STUDY QUESTION: Does the transcriptome of preconceptional endometrium in the proliferative phase show a specific profile in women with recurrent pregnancy loss (RPL)?

Pathway Enrichment-Based Unsupervised Learning Identifies Novel Subtypes of Cancer-Associated Fibroblasts in Pancreatic Ductal Adenocarcinoma.

Interdisciplinary sciences, computational life sciences
Existing single-cell clustering methods are based on gene expressions that are susceptible to dropout events in single-cell RNA sequencing (scRNA-seq) data. To overcome this limitation, we proposed a pathway-based clustering method for single cells (...

Uncovering hepatic transcriptomic and circulating proteomic signatures in MASH: A meta-analysis and machine learning-based biomarker discovery.

Computers in biology and medicine
BACKGROUND: Metabolic-associated steatohepatitis (MASH), the progressive form of metabolic-associated steatotic liver disease (MASLD), poses significant risks for liver fibrosis and cardiovascular complications. Despite extensive research, reliable b...

A novel machine learning-based workflow to capture intra-patient heterogeneity through transcriptional multi-label characterization and clinically relevant classification.

Journal of biomedical informatics
OBJECTIVES: Patient classification into specific molecular subtypes is paramount in biomedical research and clinical practice to face complex, heterogeneous diseases. Existing methods, especially for gene expression-based cancer subtyping, often simp...

MORPSO_ECD+ELM: A Unified Framework for Gene Selection and Cancer Classification.

IEEE journal of biomedical and health informatics
Gene selection and cancer classification are inherently multi-objective tasks that require balancing competing objectives, such as maximizing classification accuracy while minimizing irrelevant or redundant genes. Existing methods often optimize a si...

scDMSC: Deep Multi-View Subspace Clustering for Single-Cell Multi-Omics Data.

IEEE journal of biomedical and health informatics
Single-cell multi-omics sequencing technology comprehensively considers various molecular features to reveal the complexity of cells information. The clustering analysis of multi-omics data provides new insight into cellular heterogeneity. However, m...

Integrating bulk RNA-seq and scRNA-seq analyses with machine learning to predict platinum response and prognosis in ovarian cancer.

Scientific reports
Platinum-based therapy is an integral part of the standard treatment for ovarian cancer. However, despite extensive research spanning several decades, the identification of dependable predictive biomarkers for platinum response in clinical practice h...