Recognition of Lung Adenocarcinoma-specific Gene Pair Based on Genetic Algorithm and Establishment of a Deep Learning Prediction Model.

CONCLUSION: We construct a diagnostic classification model that is capable to accurately predict normal samples and disease samples. Comparing with the traditional methods which takes ingle gene as a feature, the relative difference between gene pairs is a higher order feature, leverage high-order features to build the model can avoid instability caused by a single gene mutation, making the prediction results more reliable. PMID: 31142257 [PubMed - as supplied by publisher]
Source: Combinatorial Chemistry and High Throughput Screening - Category: Chemistry Authors: Tags: Comb Chem High Throughput Screen Source Type: research

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Lung cancer is one of the most common types of cancer, among which lung adenocarcinoma accounts for the largest proportion. Currently, accurate staging is a prerequisite for effective diagnosis and treatment o...
Source: BMC Bioinformatics - Category: Bioinformatics Authors: Tags: Research article Source Type: research
Previous genome-wide transcriptome profiling found circ_ZNF124 was highly expressed in lung adenocarcinoma, however, the role of circ_ZNF124 in non-small cell lung cancer (NSCLC) is still unknown. The purpose ...
Source: Cancer Cell International - Category: Cancer & Oncology Authors: Tags: Primary research Source Type: research
n Hua Dong Yu Yao While methods for detecting SNVs and indels in circulating tumor DNA (ctDNA) with hybridization capture-based next-generation sequencing (NGS) have been available, copy number variations (CNVs) detection is more challenging. Here, we present a method enabling CNV detection from a 150-gene panel using a very low amount of ctDNA. First, a read depth-based CNV estimation method without a paired blood sample was developed and cfDNA sequencing data from healthy people were used to build a panel of normal (PoN) model. Then, in silico and in vitro simulations were performed to define the limit of detecti...
Source: Genes - Category: Genetics & Stem Cells Authors: Tags: Article Source Type: research
Lung cancer is the most common cancer (11.6 % of total cancers) and the leading cause of death from cancers worldwide (18.4 % of total cancer deaths) [1], with adenocarcinoma being the most common histological type of lung cancer [2,3]. A new classification of lung adenocarcinoma was established in 2011, dividing lung adenocarcinoma into atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) [4], consistent with the classification of lung adenocarcinoma published by WHO in 2015 [5].
Source: Lung Cancer - Category: Cancer & Oncology Authors: Source Type: research
ConclusionSeveral radiomic features (including wavelet energies, information measures of correlation and maximum probability from co-occurrence matrix, busyness from neighborhood intensity-difference matrix, directionalities from Tamura ’s texture, and fractal dimension estimation) significantly associated with distant metastasis, nodal metastasis, and histology were discovered in this work, presenting great potential as imaging biomarkers for pathological diagnosis and target therapy decision.
Source: International Journal of Computer Assisted Radiology and Surgery - Category: Intensive Care Source Type: research
AbstractBackground.Non‐small cell lung cancer (NSCLC) is one of the most common human malignancies and the leading cause of cancer‐related death. Over the past few decades, genomic alterations of cancer driver genes have been identified in NSCLC, and molecular testing and targeted therapies have become standard care for lung cancer patients. Here we studied the unique genomic profile of driver genes in Chinese patients with NSCLC by next‐generation sequencing (NGS) assay.Materials and Methods.A total of 1,200 Chinese patients with NSCLC were enrolled in this study. The median age was 60 years (range: 26–...
Source: The Oncologist - Category: Cancer & Oncology Authors: Tags: Cancer Diagnostics and Molecular Pathology Source Type: research
Conclusion.As a noninvasive method, the CT‐based radiomics signature can be used to predict the EGFR mutation status of LADC appearing as a subsolid nodule.Implications for Practice.Lung adenocarcinoma (LADC) with epidermal growth factor receptor (EGFR) mutation is considered a subgroup of lung cancer that is sensitive to EGFR‐targeted tyrosine kinase inhibitors. However, some patients with inoperable subsolid LADC are unable to undergo tissue sampling by biopsy for molecular analysis in clinical practice. A computed tomography‐based radiomics signature may serve as a noninvasive biomarker to predict the EGFR mutatio...
Source: The Oncologist - Category: Cancer & Oncology Authors: Tags: Lung Cancer, Precision Medicine Clinic: Molecular Tumor Board, Cancer Imaging, Cancer Diagnostics and Molecular Pathology Source Type: research
In conclusion, our findings indicate that IL-4/STAT6 signaling in CD11b+ cells promotes lung cancer progression by triggering an IL-4 positive feedback loop and increasing M2 myeloid cells. STAT6 may be a new therapeutic target for the prevention and treatment of lung cancer.
Source: Frontiers in Immunology - Category: Allergy & Immunology Source Type: research
Lung cancer is a leading cause of death from cancer worldwide, especially non-small cell lung cancer (NSCLC). The marker of progression in lung adenocarcinoma, the main type of NSCLC, has been rarely studied. ...
Source: BMC Medical Genetics - Category: Genetics & Stem Cells Authors: Tags: Research article Source Type: research
ConclusionCytology, microbiology, and pathology for pericardial drainage procedures were unable to detect a diagnosis for 76% of all cases and greater than 50% of cases with the theoretically detectable disease. Pericardial drainage procedures have a clear therapeutic value, but they have limited diagnostic utility.
Source: Journal of Cardiac Surgery - Category: Cardiovascular & Thoracic Surgery Authors: Tags: ORIGINAL ARTICLE Source Type: research
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