AIMC Topic: Gene Expression Regulation, Neoplastic

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A six‑gene support vector machine classifier contributes to the diagnosis of pediatric septic shock.

Molecular medicine reports Jan 23, 2020
Septic shock is induced by an uncontrolled inflammatory immune response to pathogens and the survival rate of patients with pediatric septic shock (PSS) is particularly low, with a mortality rate of 25‑50%. The present study explored the mechanisms o...
Biomarkers Reproducibility of Results Infant Gene Expression Profiling Humans Gene Regulatory Networks Computational Biology Gene Expression Regulation, Neoplastic Shock, Septic Support Vector Machine Adolescent Child, Preschool Child ROC Curve Databases, Genetic Age Factors Gene Ontology
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Multifactorial Deep Learning Reveals Pan-Cancer Genomic Tumor Clusters with Distinct Immunogenomic Landscape and Response to Immunotherapy.

Clinical cancer research : an official journal of the American Association for Cancer Research Jan 7, 2020
PURPOSE: Tumor genomic features have been of particular interest because of their potential impact on the tumor immune microenvironment and response to immunotherapy. Due to the substantial heterogeneity, an integrative approach incorporating diverse...
Neoplasms Deep Learning Prognosis Genomics Gene Expression Regulation, Neoplastic Humans Survival Rate Immunotherapy Tumor Microenvironment DNA Copy Number Variations Biomarkers, Tumor Microsatellite Instability Follow-Up Studies
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FOLFOX treatment response prediction in metastatic or recurrent colorectal cancer patients via machine learning algorithms.

Cancer medicine Jan 1, 2020
Early identification of metastatic or recurrent colorectal cancer (CRC) patients who will be sensitive to FOLFOX (5-FU, leucovorin and oxaliplatin) therapy is very important. We performed microarray meta-analysis to identify differentially expressed ...
Gene Expression Profiling Gene Expression Regulation, Neoplastic Antineoplastic Combined Chemotherapy Protocols Neoplasm Recurrence, Local Machine Learning Prognosis Colorectal Neoplasms Humans Biomarkers, Tumor Cell Cycle Proteins Fluorouracil Oligonucleotide Array Sequence Analysis Protein Kinases Intracellular Signaling Peptides and Proteins Datasets as Topic Response Evaluation Criteria in Solid Tumors Organoplatinum Compounds Leucovorin
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Identification of aberrantly methylated‑differentially expressed genes and gene ontology in prostate cancer.

Molecular medicine reports Dec 11, 2019
Prostate cancer (PCa) is the most frequent urological malignancy in men worldwide. DNA methylation has an essential role in the etiology and pathogenesis of PCa. The purpose of the present study was to identify the aberrantly methylated‑differentiall...
Protein Interaction Mapping DNA Methylation Prostatic Neoplasms Kaplan-Meier Estimate Gene Ontology Male Gene Expression Profiling Gene Regulatory Networks Gene Expression Regulation, Neoplastic Humans Reproducibility of Results
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The integrative knowledge base for miRNA-mRNA expression in colorectal cancer.

Scientific reports Dec 2, 2019
"miRNA colorectal cancer" (https://mirna-coadread.omics.si/) is a freely available web application for studying microRNA and mRNA expression and their correlation in colorectal cancer. To the best of our knowledge, "miRNA colorectal cancer" has the l...
Feasibility Studies Knowledge Bases High-Throughput Screening Assays Humans Colorectal Neoplasms Colon Gene Expression Regulation, Neoplastic MicroRNAs Rectum Gene Expression Profiling RNA, Messenger
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Analyzing gene expression data for pediatric and adult cancer diagnosis using logic learning machine and standard supervised methods.

BMC bioinformatics Nov 22, 2019
BACKGROUND: Logic Learning Machine (LLM) is an innovative method of supervised analysis capable of constructing models based on simple and intelligible rules. In this investigation the performance of LLM in classifying patients with cancer was evalua...
Adult Male Neoplasms Child Databases, Genetic Logic ROC Curve Gene Expression Regulation, Neoplastic Humans Neural Networks, Computer Machine Learning Female
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A 21‑gene Support Vector Machine classifier and a 10‑gene risk score system constructed for patients with gastric cancer.

Molecular medicine reports Nov 21, 2019
Gastric cancer (GC) ranks fifth in terms of incidence and third in terms of tumor mortality worldwide. The present study was designed to construct a Support Vector Machine (SVM) classifier and risk score system for GC. The GSE62254 (training set) and...
Gene Expression Regulation, Neoplastic Membrane Proteins Humans Support Vector Machine Cytokines Databases, Genetic Kaplan-Meier Estimate A Kinase Anchor Proteins Male Risk Factors Angiopoietin-like Proteins Carrier Proteins Angiopoietin-Like Protein 1 Middle Aged Aged Nerve Tissue Proteins Prognosis Gene Expression Profiling Stomach Neoplasms Neoplasm Recurrence, Local Female Gene Regulatory Networks Tumor Suppressor Proteins
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Unsupervised feature selection algorithm for multiclass cancer classification of gene expression RNA-Seq data.

Genomics Nov 20, 2019
This paper presents a Grouping Genetic Algorithm (GGA) to solve a maximally diverse grouping problem. It has been applied for the classification of an unbalanced database of 801 samples of gene expression RNA-Seq data in 5 types of cancer. The sample...
Neoplasms RNA-Seq Gene Expression Regulation, Neoplastic Unsupervised Machine Learning Humans
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Four transcription profile-based models identify novel prognostic signatures in oesophageal cancer.

Journal of cellular and molecular medicine Nov 19, 2019
Oesophageal cancer (ESCA) is a clinically challenging disease with poor prognosis and health-related quality of life. Here, we investigated the transcriptome of ESCA to identify high risk-related signatures. A total of 159 ESCA patients of The Cancer...
Humans Transcriptome Survival Rate Gene Expression Regulation, Neoplastic Biomarkers, Tumor Tumor Cells, Cultured Cell Proliferation Machine Learning Male Algorithms Apoptosis Esophageal Neoplasms Middle Aged Prognosis Esophageal Squamous Cell Carcinoma Female
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Gene expression based survival prediction for cancer patients-A topic modeling approach.

PloS one Nov 15, 2019
Cancer is one of the leading cause of death, worldwide. Many believe that genomic data will enable us to better predict the survival time of these patients, which will lead to better, more personalized treatment options and patient care. As standard ...
Kaplan-Meier Estimate Gene Expression Regulation, Neoplastic Humans Oligonucleotide Array Sequence Analysis Natural Language Processing Prognosis Models, Biological Neoplasms Datasets as Topic Gene Expression Profiling
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