AIMC Topic: DNA Copy Number Variations
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Leveraging TME features and multi-omics data with an advanced deep learning framework for improved Cancer survival prediction.
Scientific reports
Apr 24, 2025
Glioma, a malignant intracranial tumor with high invasiveness and heterogeneity, significantly impacts patient survival. This study integrates multi-omics data to improve prognostic prediction and identify therapeutic targets. Using single-cell data ...
Integrative Multi-Omics Analysis Reveals Molecular Subtypes of Ovarian Cancer and Constructs Prognostic Models.
Journal of immunotherapy (Hagerstown, Md. : 1997)
Apr 9, 2025
Ovarian cancer (OV) remains the most lethal gynecological malignancy. The aim of this study was to identify molecular subtypes of OV through integrative multi-omics analysis and construct machine learning-based prognostic models for predicting the ef...
CNRein: an evolution-aware deep reinforcement learning algorithm for single-cell DNA copy number calling.
Genome biology
Apr 7, 2025
Low-pass single-cell DNA sequencing technologies and algorithmic advancements have enabled haplotype-specific copy number calling on thousands of cells within tumors. However, measurement uncertainty may result in spurious CNAs inconsistent with real...
Anticancer drug response prediction integrating multi-omics pathway-based difference features and multiple deep learning techniques.
PLoS computational biology
Mar 31, 2025
Individualized prediction of cancer drug sensitivity is of vital importance in precision medicine. While numerous predictive methodologies for cancer drug response have been proposed, the precise prediction of an individual patient's response to drug...
Unravelling single-cell DNA replication timing dynamics using machine learning reveals heterogeneity in cancer progression.
Nature communications
Feb 8, 2025
Genomic heterogeneity has largely been overlooked in single-cell replication timing (scRT) studies. Here, we develop MnM, an efficient machine learning-based tool that allows disentangling scRT profiles from heterogenous samples. We use single-cell c...
Integrating mitochondrial and lysosomal gene analysis for breast cancer prognosis using machine learning.
Scientific reports
Jan 27, 2025
The impact of mitochondrial and lysosomal co-dysfunction on breast cancer patient outcomes is unclear. The objective of this study is to develop a predictive machine learning (ML) model utilizing mitochondrial and lysosomal co-regulators in order to ...
Multimodal integration using a machine learning approach facilitates risk stratification in HR+/HER2- breast cancer.
Cell reports. Medicine
Jan 22, 2025
Hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) breast cancer is the most common type of breast cancer, with continuous recurrence remaining an important clinical issue. Current relapse predictive models in H...
Multiomics integration and machine learning reveal prognostic programmed cell death signatures in gastric cancer.
Scientific reports
Dec 28, 2024
Gastric cancer (GC) is characterized by notable heterogeneity and the impact of molecular subtypes on treatment and prognosis. The role of programmed cell death (PCD) in cellular processes is critical, yet its specific function in GC is underexplored...
Optimizing sequence data analysis using convolution neural network for the prediction of CNV bait positions.
BMC bioinformatics
Dec 24, 2024
BACKGROUND: Accurate prediction of copy number variations (CNVs) from targeted capture next-generation sequencing (NGS) data relies on effective normalization of read coverage profiles. The normalization process is particularly challenging due to hid...
Tumour purity assessment with deep learning in colorectal cancer and impact on molecular analysis.
The Journal of pathology
Dec 22, 2024
Tumour content plays a pivotal role in directing the bioinformatic analysis of molecular profiles such as copy number variation (CNV). In clinical application, tumour purity estimation (TPE) is achieved either through visual pathological review [conv...
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