Deep gene selection method to select genes from microarray datasets for cancer classification

Alanni, Russul; Hou, Jingyu; Azzawi, Hasseeb; Xiang, Yong

doi:10.1186/s12859-019-3161-2

Cited by 24 publications

(14 citation statements)

References 48 publications

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“…For lung cancer diagnostic classifications, Podolsky et al 3 used support vector machine learning algorithms to perform lung cancer morphology classification. Alanni et al 4 developed a deep gene selection method to select genes from microarray datasets for cancer classification. Their experimental results showed that an average sensitivity of 95.22% and an average specificity of 77.39%.…”

Section: Introductionmentioning

confidence: 99%

Functional Effects of Four or Fewer Critical Genes Linked to Lung Cancers and New Subtypes Detected by A New Machine Learning Classifier

Zhang¹

2021

Preprint

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Finding genes biologically directly or indirectly related to lung cancer has been drawing much attention, and many genes directly related to lung cancer have been reported. However, it has not been confirmed whether those published 'key' genes are truly critical to lung cancer formation, i.e., they may be with very limited useful information. As a result, finding essential genes remains a challenging lung cancer research problem. Using a recently developed competing linear factor analysis method in differentially expressed gene detection, we advance the study of lung cancer critical genes detection to a uniformly informative level. A set of common four genes and their functional effects are detected to be differentially expressed in tumor and non-tumor samples with 100% sensitivity and 100% specificity in one study of lung adenocarcinoma (LUAD) and one study of squamous cell lung cancers (LUSC) (two North American cohorts with 20429 genes, 576 and 552 samples respectively). Two additional analyses also gain accuracy of 97.8% sensitivity and 100% specificity in one study of non-small cell lung carcinomas (NSCLC, a European cohort with 20356 genes and 156 samples), and an accuracy of 100% sensitivity and 95% specificity (1 out of 20 non-tumor samples) in one study of ALK-positive and EGFR/KRAS/ALK-negative lung adenocarcinomas (LUAD, a Japanese cohort with 20356 genes and 224 samples). There are some common genes, but different functional effects, within each set of four genes among two North American cohorts and a European cohort and among North American cohorts and the Japanese cohort. These results show the four-gene-based classifiers are robust with different types of lung cancers and different race cohorts and accurate. The functional effects of four genes disclose significantly other mechanisms (mysteries) between LUAD and LUSC. These sets of four genes and their functional effects are considered to be essential for lung cancer studies and practice. These genes' functional effects naturally classify patients into different groups (more than seven subtypes). Subtype information is useful for personalized therapies. The new findings can motivate new lung cancer research in more focused and targeted directions to save lives, protect people, and reduce enormous economic costs in research and lung cancer treatments.

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Section: Introductionmentioning

confidence: 99%

Functional Effects of Four or Fewer Critical Genes Linked to Lung Cancers and New Subtypes Detected by A New Machine Learning Classifier

Zhang¹

2021

Preprint

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“…Here, some tools are presented which are best matches with our task. Later in this research, some basics are covered to understand the work properly as depicted by the figure 1 from source [12].…”

Section: Motivationmentioning

confidence: 99%

Information Retrieval for Cancer Cell Detection Based on Advanced Machine Learning Techniques

Shaker¹,

Ahmed²

2022

MJS

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In this research paper, we focus on designing and developing a fully automated gene regulation from cancerous cell heterogeneity using advanced machine learning techniques. There are several modern technologies developed to make DNA sequencing easier and cheaper. Among them, gene regulation produces the longest read sequences and the lengths of the reads are growing day by day. Machine learning technique like Support Vector Machine (SVM) is developed to align these gene sequences. Every technique faced some challenges, but facing the desired challenges introduce some new challenges on the other side. So, no one tool is perfect for every work. The SVM technique is a new aligner tool that does a tradeoff and performs better from different aspects. For the model with the best generator loss, an average maximum validation accuracy of 91.29% was achieved. The gene regulation with SVM is like a mini-map that takes a few times more memory to index the whole genome of a reference sequence. The single-cell data are the main target of SVM. It is shown that it would help the SVM and similar techniques to align better with long insertions and deletions of single-cell gene regulation. Single-cell data is run against the well-known reference sequence and a randomly generated synthetic reference.

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“…In [4] RussulAlanni et al proposed an efficient gene selection algorithm. It can select relevant genes and significantly subtle to the samples in the few genes and less cost time by the algorithm achieved much high prediction accuracy on several public microarray data, it in turn proves the efficiency and effectiveness of the systems.…”

Section: Literature Surveymentioning

confidence: 99%

“…Carcinoma of the lungs, most commonly known as lung cancer is triggered due to the uninhibited growth of cell nodules in the lung tissues. [4] The survival rates of lung cancer are a very low percentage as compared to the other forms of cancer. It is considered a potentially life-threatening disease with a really high deathrate.…”

Section: Introductionmentioning

confidence: 99%

Identification and Classification of Lung Nodules Using Neural Networks

Gnanavel¹

2021

TURCOMAT

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Lung cancer is a serious health concern, which is also one of the major types of cancer that has a profound impact on the overall cancer mortality rates. The detection of lung cancer nodules is quite a challenge as the major challenge is the structure of the cancer nodules; here the cells are imbricated with each other. The prediction and classification of lung cancer is done by applying digital image processing techniques to the acquired input images of the nodules. This methodology also aids early detection which in turns reduces the criticality of the condition and provides scope for early intervention and treatment. The prediction methodology involves extracting several features of the lung cancer cell and then applying pattern-based prediction techniques. In recent times, owing to the fact that the time and execution parameters are very important aspects to detect the abnormality of the fast-spreading cancer cells, digital image processing techniques are being widely deployed. The fundamental factors of this research are the quality of image assessment and the precision of feature extraction. Following our proposed methodology, a clear picture of the region of interest is obtained which acts as a basis for the feature extraction process. Here an overall evaluation of the digital image processing techniques used by previous scholars for the finding and classification of lung cancer nodules have also been emphasised.

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Deep gene selection method to select genes from microarray datasets for cancer classification

Cited by 24 publications

References 48 publications

Functional Effects of Four or Fewer Critical Genes Linked to Lung Cancers and New Subtypes Detected by A New Machine Learning Classifier

Functional Effects of Four or Fewer Critical Genes Linked to Lung Cancers and New Subtypes Detected by A New Machine Learning Classifier

Information Retrieval for Cancer Cell Detection Based on Advanced Machine Learning Techniques

Identification and Classification of Lung Nodules Using Neural Networks

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