AIMC Topic: Gene Expression Profiling
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Partial label learning for automated classification of single-cell transcriptomic profiles.
PLoS computational biology
Apr 5, 2024
Single-cell RNA sequencing (scRNASeq) data plays a major role in advancing our understanding of developmental biology. An important current question is how to classify transcriptomic profiles obtained from scRNASeq experiments into the various cell t...
Machine learning-driven prediction of brain metastasis in lung adenocarcinoma using miRNA profile and target gene pathway analysis of an mRNA dataset.
Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
Apr 3, 2024
BACKGROUND: Brain metastasis (BM) is common in lung adenocarcinoma (LUAD) and has a poor prognosis, necessitating predictive biomarkers. MicroRNAs (MiRNAs) promote cancer cell growth, infiltration, and metastasis. However, the relationship between th...
A radiogenomic multimodal and whole-transcriptome sequencing for preoperative prediction of axillary lymph node metastasis and drug therapeutic response in breast cancer: a retrospective, machine learning and international multicohort study.
International journal of surgery (London, England)
Apr 1, 2024
BACKGROUND: Axillary lymph nodes (ALN) status serves as a crucial prognostic indicator in breast cancer (BC). The aim of this study was to construct a radiogenomic multimodal model, based on machine learning and whole-transcriptome sequencing (WTS), ...
Evaluation of Clinically Significant miRNAs Level by Machine Learning Approaches Utilizing Total Transcriptome Data.
Doklady. Biochemistry and biophysics
Mar 27, 2024
Analysis of the mechanisms underlying the occurrence and progression of cancer represents a key objective in contemporary clinical bioinformatics and molecular biology. Utilizing omics data, particularly transcriptomes, enables a detailed characteriz...
Machine Learning Analysis Using RNA Sequencing to Distinguish Neuromyelitis Optica from Multiple Sclerosis and Identify Therapeutic Candidates.
The Journal of molecular diagnostics : JMD
Mar 22, 2024
This study aims to identify RNA biomarkers distinguishing neuromyelitis optica (NMO) from relapsing-remitting multiple sclerosis (RRMS) and explore potential therapeutic applications leveraging machine learning (ML). An ensemble approach was develope...
TENET: Triple-enhancement based graph neural network for cell-cell interaction network reconstruction from spatial transcriptomics.
Journal of molecular biology
Mar 18, 2024
Cellular communication relies on the intricate interplay of signaling molecules, forming the Cell-cell Interaction network (CCI) that coordinates tissue behavior. Researchers have shown the capability of shallow neural networks in reconstructing CCI,...
Stable feature selection utilizing Graph Convolutional Neural Network and Layer-wise Relevance Propagation for biomarker discovery in breast cancer.
Artificial intelligence in medicine
Mar 11, 2024
High-throughput technologies are becoming increasingly important in discovering prognostic biomarkers and in identifying novel drug targets. With Mammaprint, Oncotype DX, and many other prognostic molecular signatures breast cancer is one of the para...
Mining phase separation-related diagnostic biomarkers for endometriosis through WGCNA and multiple machine learning techniques: a retrospective and nomogram study.
Journal of assisted reproduction and genetics
Mar 8, 2024
OBJECTIVE: The objective of this study was to investigate the role of phase separation-related genes in the development of endometriosis (EMs) and to identify potential characteristic genes associated with the condition.
scFSNN: a feature selection method based on neural network for single-cell RNA-seq data.
BMC genomics
Mar 8, 2024
While single-cell RNA sequencing (scRNA-seq) allows researchers to analyze gene expression in individual cells, its unique characteristics like over-dispersion, zero-inflation, high gene-gene correlation, and large data volume with many features pose...
Exploring Prognostic Gene Factors in Breast Cancer via Machine Learning.
Biochemical genetics
Feb 21, 2024
Breast cancer remains the most prevalent cancer in women. To date, its underlying molecular mechanisms have not been fully uncovered. The determination of gene factors is important to improve our understanding on breast cancer, which can correlate th...
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