AIMC Topic: Gene Expression Regulation, Neoplastic

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A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning.

Biomolecules Apr 12, 2021
Although the incidence of central nervous system (CNS) cancers is not high, it significantly reduces a patient's quality of life and results in high mortality rates. A low incidence also means a low number of cases, which in turn means a low amount o...
Animals Computational Biology Gene Expression Regulation, Neoplastic Central Nervous System Neoplasms Humans Machine Learning Biomarkers, Tumor
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Deep generative neural network for accurate drug response imputation.

Nature communications Mar 19, 2021
Drug response differs substantially in cancer patients due to inter- and intra-tumor heterogeneity. Particularly, transcriptome context, especially tumor microenvironment, has been shown playing a significant role in shaping the actual treatment outc...
Humans Neural Networks, Computer Tumor Microenvironment Deep Learning Cell Line, Tumor Gene Expression Regulation, Neoplastic Pharmaceutical Preparations Neoplasms Antineoplastic Agents Transcriptome
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Explaining decisions of graph convolutional neural networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer.

Genome medicine Mar 11, 2021
BACKGROUND: Contemporary deep learning approaches show cutting-edge performance in a variety of complex prediction tasks. Nonetheless, the application of deep learning in healthcare remains limited since deep learning methods are often considered as ...
Gene Regulatory Networks Female Humans Neoplasm Metastasis Gene Expression Regulation, Neoplastic Breast Neoplasms Neural Networks, Computer Signal Transduction Protein Interaction Maps Algorithms
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Novel gene signatures for stage classification of the squamous cell carcinoma of the lung.

Scientific reports Mar 1, 2021
The squamous cell carcinoma of the lung (SCLC) is one of the most common types of lung cancer. As GLOBOCAN reported in 2018, lung cancer was the first cause of death and new cases by cancer worldwide. Typically, diagnosis is made in the later stages ...
Lung Neoplasms Gene Expression Regulation, Neoplastic Carcinoma, Non-Small-Cell Lung Databases, Nucleic Acid Biomarkers, Tumor Male Machine Learning Gene Expression Profiling Female Humans
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MethylationToActivity: a deep-learning framework that reveals promoter activity landscapes from DNA methylomes in individual tumors.

Genome biology Jan 19, 2021
Although genome-wide DNA methylomes have demonstrated their clinical value as reliable biomarkers for tumor detection, subtyping, and classification, their direct biological impacts at the individual gene level remain elusive. Here we present Methyla...
Sarcoma Promoter Regions, Genetic Histones Neural Networks, Computer Machine Learning Neoplasms Gene Expression Regulation, Neoplastic DNA Methylation Deep Learning Cell Line, Tumor Humans
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Network-based drug sensitivity prediction.

BMC medical genomics Dec 28, 2020
BACKGROUND: Drug sensitivity prediction and drug responsive biomarker selection on high-throughput genomic data is a critical step in drug discovery. Many computational methods have been developed to serve this purpose including several deep neural n...
Prognosis Biomarkers, Tumor Antineoplastic Agents Gene Regulatory Networks Lung Neoplasms Gene Expression Profiling Deep Learning Humans Neural Networks, Computer Software Computational Biology Gene Expression Regulation, Neoplastic Carcinoma, Non-Small-Cell Lung
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Immune profile of the tumor microenvironment and the identification of a four-gene signature for lung adenocarcinoma.

Aging Dec 9, 2020
The composition and relative abundances of immune cells in the tumor microenvironment are key factors affecting the progression of lung adenocarcinomas (LUADs) and the efficacy of immunotherapy. Using the cancer gene expression dataset from The Cance...
Receptors, Purinergic P2 Receptors, Purinergic P2Y12 Gene Ontology Adenocarcinoma of Lung Receptors, CCR4 Tumor Microenvironment Biomarkers, Tumor Male Gene Expression Regulation, Neoplastic Receptors, CCR2 Middle Aged Lung Neoplasms Humans Prognosis Female Aged
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Identifying transcriptomic correlates of histology using deep learning.

PloS one Nov 25, 2020
Linking phenotypes to specific gene expression profiles is an extremely important problem in biology, which has been approached mainly by correlation methods or, more fundamentally, by studying the effects of gene perturbations. However, genome-wide ...
Neoplasms Transcriptome Deep Learning Humans Genome Histology Gene Expression Regulation, Neoplastic Neoplasm Proteins Tissue Distribution
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A risk prediction model of gene signatures in ovarian cancer through bagging of GA-XGBoost models.

Journal of advanced research Nov 11, 2020
INTRODUCTION: Ovarian cancer (OC) is one of the most frequent gynecologic cancers among women, and high-accuracy risk prediction techniques are essential to effectively select the best intervention strategies and clinical management for OC patients a...
TOR Serine-Threonine Kinases Carcinoma, Ovarian Epithelial Risk Assessment Prognosis Humans Phosphatidylinositol 3-Kinases Ovarian Neoplasms Gene Expression Regulation, Neoplastic Female Machine Learning Proto-Oncogene Proteins c-akt Gene Expression Models, Theoretical Survival Rate Algorithms
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Biological interpretation of deep neural network for phenotype prediction based on gene expression.

BMC bioinformatics Nov 4, 2020
BACKGROUND: The use of predictive gene signatures to assist clinical decision is becoming more and more important. Deep learning has a huge potential in the prediction of phenotype from gene expression profiles. However, neural networks are viewed as...
Neoplasms Gene Expression Regulation, Neoplastic Databases, Genetic Humans Neural Networks, Computer
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