AIMC Topic: Gene Expression Regulation, Neoplastic

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

Zoological research Mar 18, 2021
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...
Humans Computational Biology Gene Expression Regulation, Neoplastic DNA Copy Number Variations Mutation High-Throughput Nucleotide Sequencing Machine Learning Algorithms Animals Neoplasms
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Deep learning for drug response prediction in cancer.

Briefings in bioinformatics Jan 18, 2021
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...
Gene Expression Regulation, Neoplastic Drug Resistance, Neoplasm Pharmacogenomic Variants Humans Deep Learning Genomics
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Using machine learning method to identify as a novel marker to predict biochemical recurrence in prostate cancer.

Biomarkers in medicine Jan 1, 2021
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...
Prostatectomy DNA Methylation Male Humans Gene Expression Regulation, Neoplastic Prostatic Neoplasms Machine Learning Neoplasm Recurrence, Local Prognosis Biomarkers, Tumor
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Common gene signatures and key pathways in hypopharyngeal and esophageal squamous cell carcinoma: Evidence from bioinformatic analysis.

Medicine Oct 16, 2020
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...
Humans Prognosis Carcinoma, Squamous Cell Databases, Genetic Esophageal Neoplasms Biomarkers, Tumor Gene Ontology Gene Expression Profiling Pharyngeal Neoplasms Computational Biology Gene Expression Regulation, Neoplastic
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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 Jan 1, 2020
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.
Prognosis Biomarkers, Tumor Gene Regulatory Networks Computational Biology Humans Machine Learning RNA, Long Noncoding High-Throughput Screening Assays RNA, Messenger Gene Expression Regulation, Neoplastic Squamous Cell Carcinoma of Head and Neck Head and Neck Neoplasms
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Effects of Jiazhu decoction in combination with cyclophosphamide on breast cancer in mice.

Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan Oct 1, 2019
OBJECTIVE: To investigate the therapeutic effects of Jiazhu decoction (JZD) in combination with cyclophosphamide (CTX) on the growth of breast cancer in mice and to explore the possible molecular mechanisms of action.
Drugs, Chinese Herbal Drug Interactions Xenograft Model Antitumor Assays Cyclin D1 Mice Breast Neoplasms Cyclophosphamide Animals RNA, Messenger Antineoplastic Agents Genes, myc Gene Expression Regulation, Neoplastic Cell Line, Tumor beta Catenin Cell Proliferation
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Artificial intelligence in drug combination therapy.

Briefings in bioinformatics Jul 19, 2019
Currently, the development of medicines for complex diseases requires the development of combination drug therapies. It is necessary because in many cases, one drug cannot target all necessary points of intervention. For example, in cancer therapy, a...
Drug Resistance Stochastic Processes Logistic Models Gene Expression Regulation, Neoplastic Support Vector Machine Bayes Theorem Least-Squares Analysis Drug Interactions Expert Systems Drug Therapy, Combination Machine Learning Neoplasms Deep Learning Computational Biology Humans Neural Networks, Computer Artificial Intelligence
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SuperCT: a supervised-learning framework for enhanced characterization of single-cell transcriptomic profiles.

Nucleic acids research May 7, 2019
Characterization of individual cell types is fundamental to the study of multicellular samples. Single-cell RNAseq techniques, which allow high-throughput expression profiling of individual cells, have significantly advanced our ability of this task....
Mice Sequence Analysis, RNA Gene Expression Regulation, Neoplastic Cluster Analysis Pancreatic Neoplasms Supervised Machine Learning High-Throughput Nucleotide Sequencing Datasets as Topic Animals Gene Expression Profiling Transcriptome Single-Cell Analysis Humans Cell Lineage Software RNA, Small Cytoplasmic
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DriverML: a machine learning algorithm for identifying driver genes in cancer sequencing studies.

Nucleic acids research May 7, 2019
Although rapid progress has been made in computational approaches for prioritizing cancer driver genes, research is far from achieving the ultimate goal of discovering a complete catalog of genes truly associated with cancer. Driver gene lists predic...
Oncogenes Cell Proliferation Software Nuclear Proteins Humans Neoplasms Cell Movement Cell Line, Tumor Neoplasm Proteins Machine Learning Gene Expression Regulation, Neoplastic Monte Carlo Method Mutation Cell Cycle Proteins Datasets as Topic Atlases as Topic
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A parsimonious 3-gene signature predicts clinical outcomes in an acute myeloid leukemia multicohort study.

Blood advances Apr 23, 2019
Acute myeloid leukemia (AML) is a genetically heterogeneous hematological malignancy with variable responses to chemotherapy. Although recurring cytogenetic abnormalities and gene mutations are important predictors of outcome, 50% to 70% of AMLs harb...
Models, Biological Disease-Free Survival Survival Rate Leukemia, Myeloid, Acute Child Humans Risk Assessment Neural Networks, Computer Male Databases, Genetic Gene Expression Regulation, Neoplastic Infant Female Neoplasm Proteins Middle Aged Adolescent Infant, Newborn Predictive Value of Tests Child, Preschool Adult
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