4.6 Article

meth-SemiCancer: a cancer subtype classification framework via semi-supervised learning utilizing DNA methylation profiles

Related references

Note: Only part of the references are listed.
Article Biochemical Research Methods

Semi-Supervised Deep Learning for Cell Type Identification From Single-Cell Transcriptomic Data

Xishuang Dong et al.

Summary: The researchers propose a semi-supervised learning model, SemiRNet, to identify cell types using unlabeled and limited labeled single-cell transcriptomic data. The model is based on recurrent convolutional neural networks and consists of shared, supervised, and unsupervised networks. Evaluation on two large-scale single-cell transcriptomic datasets shows that the proposed model achieves impressive performance by learning from a small number of labeled cells and a large number of unlabeled cells.

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2023)

Article Genetics & Heredity

Identifying Cancer Subtypes Using a Residual Graph Convolution Model on a Sample Similarity Network

Wei Dai et al.

Summary: Cancer subtype classification is important for understanding cancer pathogenesis and developing new treatment strategies. This study proposes a method based on residual graph convolutional networks and sample similarity networks, achieving excellent classification results and identifying clinically significant cancer subtypes.

GENES (2022)

Article Biochemical Research Methods

Single-cell RNA-seq data semi-supervised clustering and annotation via structural regularized domain adaptation

Liang Chen et al.

Summary: Inspired by unsupervised domain adaptation, the study introduces a flexible single-cell semi-supervised clustering and annotation framework, scSemiCluster. By integrating reference and target data for training, the model utilizes structure similarity regularization and pairwise constraints to optimize clustering results. Without explicit domain alignment and batch effect correction, scSemiCluster outperforms other state-of-the-art algorithms, making it the first to utilize both deep discriminative clustering and deep generative clustering in the single-cell field.

BIOINFORMATICS (2021)

Article Cell Biology

Establishment of Novel DNA Methylation-Based Prostate Cancer Subtypes and a Risk-Predicting Eight-Gene Signature

Enchong Zhang et al.

Summary: Prostate cancer (PCa) is the most common malignant tumor affecting males worldwide. This study identified two novel PCa subtypes and developed a risk predictive signature based on DNA methylation status, which can effectively stratify patients based on prognosis.

FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY (2021)

Article Oncology

A novel DNA methylation signature is associated with androgen receptor activity and patient prognosis in bone metastatic prostate cancer

Erik Bovinder Ylitalo et al.

Summary: Integrated epigenome and transcriptome analysis identified pronounced hypermethylation in malignant compared to non-malignant areas of localized prostate tumors. Metastases showed an overall hypomethylation in relation to primary PC, including CpGs in the AR promoter accompanied with induction of AR mRNA levels. We identified a Methylation Classifier for Androgen receptor activity (MCA) signature, which separated metastases into two clusters (MCA positive/negative) related to tumor characteristics and patient prognosis.

CLINICAL EPIGENETICS (2021)

Article Biochemical Research Methods

Deep-learning approach to identifying cancer subtypes using high-dimensional genomic data

Runpu Chen et al.

BIOINFORMATICS (2020)

Article Biochemical Research Methods

Cancer subtype classification and modeling by pathway attention and propagation

Sangseon Lee et al.

BIOINFORMATICS (2020)

Review Biochemistry & Molecular Biology

Epigenetic regulation in human cancer: the potential role of epi-drug in cancer therapy

Yuanjun Lu et al.

MOLECULAR CANCER (2020)

Article Cell Biology

Multilevel Genomics-Based Taxonomy of Renal Cell Carcinoma

Fengju Chen et al.

CELL REPORTS (2016)

Article Biochemistry & Molecular Biology

The Molecular Taxonomy of Primary Prostate Cancer

Adam Abeshouse et al.

Article Biochemical Research Methods

Integrative Data Analysis of Multi-Platform Cancer Data with a Multimodal Deep Learning Approach

Muxuan Liang et al.

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2015)

Article Biochemistry & Molecular Biology

The consensus molecular subtypes of colorectal cancer

Justin Guinney et al.

NATURE MEDICINE (2015)

Article Oncology

A DNA methylation-based definition of biologically distinct breast cancer subtypes

Olafur A. Stefansson et al.

MOLECULAR ONCOLOGY (2015)

Article Biochemistry & Molecular Biology

Integrated Genomic Characterization of Papillary Thyroid Carcinoma

Nishant Agrawal et al.

Editorial Material Genetics & Heredity

The Cancer Genome Atlas Pan-Cancer analysis project

John N. Weinstein et al.

NATURE GENETICS (2013)

Review Oncology

Challenges in the Management of Stage II Colon Cancer

Efrat Dotan et al.

SEMINARS IN ONCOLOGY (2011)

Review Pathology

Mutational Heterogeneity in Human Cancers: Origin and Consequences

Jesse J. Salk et al.

ANNUAL REVIEW OF PATHOLOGY-MECHANISMS OF DISEASE (2010)

Article Medicine, General & Internal

Identification of Novel Methylation Markers in Hepatocellular Carcinoma using a Methylation Array

So Hyun Shin et al.

JOURNAL OF KOREAN MEDICAL SCIENCE (2010)

Review Biotechnology & Applied Microbiology

Epigenetic modifications and human disease

Anna Portela et al.

NATURE BIOTECHNOLOGY (2010)

Article Oncology

DNA methylation epigenotypes in breast cancer molecular subtypes

Naiara G. Bediaga et al.

BREAST CANCER RESEARCH (2010)

Article

Semi-Supervised Learning (Chapelle, O. et al., Eds.; 2006) [Book reviews]

O. Chapelle et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS (2009)

Article Oncology

Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes

Joel S. Parker et al.

JOURNAL OF CLINICAL ONCOLOGY (2009)

Article Multidisciplinary Sciences

Repeated observation of breast tumor subtypes in independent gene expression data sets

T Sorlie et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2003)

Article Biochemistry & Molecular Biology

Gene Expression Omnibus: NCBI gene expression and hybridization array data repository

R Edgar et al.

NUCLEIC ACIDS RESEARCH (2002)