AIMC Topic: Gene Expression Regulation, Neoplastic
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[Effects of long non-coding RNA RP1-90L14.1 on the biological behaviors of cancer prostate LNCaP cells and its regulating mechanisms].
Zhonghua nan ke xue = National journal of andrology
Mar 1, 2019
OBJECTIVE: To investigate the effects of long non-coding RNA RP1-90L14.1 on the proliferation, migration and invasion of prostate cancer LNCaP cells and the expressions of GRIN2A and BACE2.
Exploring microRNA Regulation of Cancer with Context-Aware Deep Cancer Classifier.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2019
BACKGROUND: MicroRNAs (miRNAs) are small, non-coding RNA that regulate gene expression through post-transcriptional silencing. Differential expression observed in miRNAs, combined with advancements in deep learning (DL), have the potential to improve...
Humans
Gene Ontology
Databases, Nucleic Acid
Deep Learning
Computational Biology
Sequence Analysis, RNA
High-Throughput Nucleotide Sequencing
Neoplasms
Molecular Sequence Annotation
Neural Networks, Computer
Gene Expression Regulation, Neoplastic
Gene Expression Profiling
Female
MicroRNAs
Diagnosis, Computer-Assisted
Male
Constructing a Risk Prediction Model for Lung Cancer Recurrence by Using Gene Function Clustering and Machine Learning.
Combinatorial chemistry & high throughput screening
Jan 1, 2019
OBJECTIVE: A significant proportion of patients with early non-small cell lung cancer (NSCLC) can be cured by surgery. The distant metastasis of tumors is the most common cause of treatment failure. Precisely predicting the likelihood that a patient ...
Feature specific quantile normalization enables cross-platform classification of molecular subtypes using gene expression data.
Bioinformatics (Oxford, England)
Jun 1, 2018
MOTIVATION: Molecular subtypes of cancers and autoimmune disease, defined by transcriptomic profiling, have provided insight into disease pathogenesis, molecular heterogeneity and therapeutic responses. However, technical biases inherent to different...
DeepSynergy: predicting anti-cancer drug synergy with Deep Learning.
Bioinformatics (Oxford, England)
May 1, 2018
MOTIVATION: While drug combination therapies are a well-established concept in cancer treatment, identifying novel synergistic combinations is challenging due to the size of combinatorial space. However, computational approaches have emerged as a tim...
Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas.
Cell reports
Apr 3, 2018
Precision oncology uses genomic evidence to match patients with treatment but often fails to identify all patients who may respond. The transcriptome of these "hidden responders" may reveal responsive molecular states. We describe and evaluate a mach...
Identification of recurrent risk-related genes and establishment of support vector machine prediction model for gastric cancer.
Neoplasma
Mar 14, 2018
This study sought to investigate genes related to recurrent risk and establish a support vector machine (SVM) classifier for prediction of recurrent risk in gastric cancer (GC).Based on the gene expression profiling dataset GSE26253, feature genes th...
BioMuta and BioXpress: mutation and expression knowledgebases for cancer biomarker discovery.
Nucleic acids research
Jan 4, 2018
Single-nucleotide variation and gene expression of disease samples represent important resources for biomarker discovery. Many databases have been built to host and make available such data to the community, but these databases are frequently limited...
Electronic Health Record Phenotypes for Precision Medicine: Perspectives and Caveats From Treatment of Breast Cancer at a Single Institution.
Clinical and translational science
Jan 1, 2018
Precision medicine is at the forefront of biomedical research. Cancer registries provide rich perspectives and electronic health records (EHRs) are commonly utilized to gather additional clinical data elements needed for translational research. Howev...
Image-based surrogate biomarkers for molecular subtypes of colorectal cancer.
Bioinformatics (Oxford, England)
Jul 1, 2017
MOTIVATION: Whole genome expression profiling of large cohorts of different types of cancer led to the identification of distinct molecular subcategories (subtypes) that may partially explain the observed inter-tumoral heterogeneity. This is also the...
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