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Gene Expression Regulation, Neoplastic

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Prognostic outcome prediction by semi-supervised least squares classification.

Briefings in bioinformatics
Although great progress has been made in prognostic outcome prediction, small sample size remains a challenge in obtaining accurate and robust classifiers. We proposed the Rescaled linear square Regression based Least Squares Learning (RRLSL), a join...

Machine learning application identifies novel gene signatures from transcriptomic data of spontaneous canine hemangiosarcoma.

Briefings in bioinformatics
Angiosarcomas are soft-tissue sarcomas that form malignant vascular tissues. Angiosarcomas are very rare, and due to their aggressive behavior and high metastatic propensity, they have poor clinical outcomes. Hemangiosarcomas commonly occur in domest...

FS-GBDT: identification multicancer-risk module via a feature selection algorithm by integrating Fisher score and GBDT.

Briefings in bioinformatics
Cancer is a highly heterogeneous disease caused by dysregulation in different cell types and tissues. However, different cancers may share common mechanisms. It is critical to identify decisive genes involved in the development and progression of can...

scCancer: a package for automated processing of single-cell RNA-seq data in cancer.

Briefings in bioinformatics
Molecular heterogeneities and complex microenvironments bring great challenges for cancer diagnosis and treatment. Recent advances in single-cell RNA-sequencing (scRNA-seq) technology make it possible to study cancer cell heterogeneities and microenv...

Prediction of Chemosensitivity in Multiple Primary Cancer Patients Using Machine Learning.

Anticancer research
BACKGROUND/AIM: Many cancer patients face multiple primary cancers. It is challenging to find an anticancer therapy that covers both cancer types in such patients. In personalized medicine, drug response is predicted using genomic information, which ...

A novel machine learning approach (svmSomatic) to distinguish somatic and germline mutations using next-generation sequencing data.

Zoological research
Somatic mutations are a large category of genetic variations, which play an essential role in tumorigenesis. Detection of somatic single nucleotide variants (SNVs) could facilitate downstream analysis of tumorigenesis. Many computational methods have...

Deep learning for drug response prediction in cancer.

Briefings in bioinformatics
Predicting the sensitivity of tumors to specific anti-cancer treatments is a challenge of paramount importance for precision medicine. Machine learning(ML) algorithms can be trained on high-throughput screening data to develop models that are able to...

Using machine learning method to identify as a novel marker to predict biochemical recurrence in prostate cancer.

Biomarkers in medicine
This study aims to identify novel marker to predict biochemical recurrence (BCR) in prostate cancer patients after radical prostatectomy with negative surgical margin. The Cancer Genome Atlas database, Gene Expression Omnibus database and Cancer Ce...

Common gene signatures and key pathways in hypopharyngeal and esophageal squamous cell carcinoma: Evidence from bioinformatic analysis.

Medicine
BACKGROUND: Hypopharyngeal and esophageal squamous cell carcinoma (ESCC) are the most common double primary tumors with poor prognosis. Intensive work has been made to illuminate the etiology, but the common carcinogenic mechanism remains unclear. Th...

Screening key lncRNAs with diagnostic and prognostic value for head and neck squamous cell carcinoma based on machine learning and mRNA-lncRNA co-expression network analysis.

Cancer biomarkers : section A of Disease markers
BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is the seventh most common type of cancer around the world. The aim of this study was to seek the long non-coding RNAs (lncRNAs) acting as diagnostic and prognostic biomarker of HNSCC.