Integrative analysis of mutated genes and mutational processes reveals seven colorectal cancer subtypes

Dashti, Hamed; Dehzangi, Abdollah; Bayati, Masroor; Breen, James; Lovell, Nigel H.; Ebrahimi, Diako; Rabiee, Hamid R.; Alinejad‐Rokny, Hamid

doi:10.1101/2020.05.18.101022

Cited by 10 publications

(9 citation statements)

References 55 publications

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“…The medical and biological datasets are increasing rapidly. To analyse such big and complex data, artificial intelligence and machine learning algorithms become most popular [10][11][12][13][14][15][16]. Therefore, it is important to implement novel techniques to uncover the medical and biological patterns.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Feature Fusion and Machine Learning Approaches for Melanoma Skin Cancer Detection

Rahman¹,

Nasir²,

A-Alam³

et al. 2022

Preprint

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Skin cancer is an exquisite disease globally nowadays. Because of the poor contrast and apparent resemblance between skin and lesions, automatic identification of skin cancer is complicated. The rate of human death can be massively reduced if melanoma skin cancer can be detected quickly using dermoscopy images. In this research, an anisotropic diffusion filtering method is used on dermoscopy images to remove multiplicative speckle noise and the fast-bounding box (FBB) method is applied to segment the skin cancer region. Furthermore, the paper consists of two feature extractor parts. One of the two features extractor parts is the hybrid feature extractor (HFE) part and another is the convolutional neural network VGG19 based CNN feature extractor part. The HFE portion combines three feature extraction approaches into a single fused feature vector: Histogram-Oriented Gradient (HOG), Local Binary Pattern (LBP), and Speed Up Robust Feature (SURF). The CNN method also is used to extract additional features from test and training datasets. This two-feature vector is fused to design the classification model. This classifier performs the classification of dermoscopy images whether it is melanoma or non-melanoma skin cancer. The proposed methodology is performed on two ordinary datasets and achieved the accuracy 99.85%, sensitivity 91.65%, and specificity 95.70%, which makes it more successful than previous machine learning algorithms.

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Section: Related Workmentioning

confidence: 99%

Hybrid Feature Fusion and Machine Learning Approaches for Melanoma Skin Cancer Detection

Rahman¹,

Nasir²,

A-Alam³

et al. 2022

Preprint

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“…We used gene-motifs [23] as the feature for our analyses. For each gene, we counted the number of mutated samples.…”

Section: Feature Selectionmentioning

confidence: 99%

Whole-Genome Analysis of De Novo Somatic Point Mutations Reveals Novel Mutational Biomarkers in Pancreatic Cancer

Ghareyazi

Mohseni

Dashti

et al. 2021

Cancers

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It is now known that at least 10% of samples with pancreatic cancers (PC) contain a causative mutation in the known susceptibility genes, suggesting the importance of identifying cancer-associated genes that carry the causative mutations in high-risk individuals for early detection of PC. In this study, we develop a statistical pipeline using a new concept, called gene-motif, that utilizes both mutated genes and mutational processes to identify 4211 3-nucleotide PC-associated gene-motifs within 203 significantly mutated genes in PC. Using these gene-motifs as distinguishable features for pancreatic cancer subtyping results in identifying five PC subtypes with distinguishable phenotypes and genotypes. Our comprehensive biological characterization reveals that these PC subtypes are associated with different molecular mechanisms including unique cancer related signaling pathways, in which for most of the subtypes targeted treatment options are currently available. Some of the pathways we identified in all five PC subtypes, including cell cycle and the Axon guidance pathway are frequently seen and mutated in cancer. We also identified Protein kinase C, EGFR (epidermal growth factor receptor) signaling pathway and P53 signaling pathways as potential targets for treatment of the PC subtypes. Altogether, our results uncover the importance of considering both the mutation type and mutated genes in the identification of cancer subtypes and biomarkers.

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“…We used gene-motifs [20], as the feature for our analyses. For each gene, we counted the number of mutated samples.…”

Section: Feature Selectionmentioning

confidence: 99%

An Investigation of Point Mutations Discovers Novel Genes and Their Corresponding Motifs in Pancreatic Cancer

Ghareyazi¹,

Mohseni²,

Dashti³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

It has now known that at least 10% of samples with pancreatic cancers (PC) contain a causative mutation in the known susceptibility genes, suggesting the importance of identifying cancer-associated genes that carry the causative mutations in high-risk individuals for early detection of PC. In this study, we develop a statistical pipeline using a new concept, called gene-motif, that utilizes both mutated genes and mutational processes to identify 4,211 3-nucleotide PC-associated gene-motifs within 203 significantly mutated genes in PC. Using these gene-motifs as distinguishable features for pancreatic cancer subtyping results in identifying five PC subtypes with distinguishable phenotypes and genotypes. Our comprehensive biological characterization reveals that these PC subtypes are associated with different molecular mechanisms including unique cancer related signaling pathways, in which for most of the subtypes targeted treatment options are currently available. Some of the pathways we identified in all five PC subtypes, including cell cycle and the Axon guidance pathway are frequently seen and mutated in cancer. We also identified Protein kinase C, EGFR (epidermal growth factor receptor) signaling pathway and P53 signaling pathways as potential targets for treatment of the PC subtypes. Altogether, our results uncover the importance of considering both the mutation type and mutated genes in the identification of cancer subtypes and biomarkers.

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Integrative analysis of mutated genes and mutational processes reveals seven colorectal cancer subtypes

Cited by 10 publications

References 55 publications

Hybrid Feature Fusion and Machine Learning Approaches for Melanoma Skin Cancer Detection

Hybrid Feature Fusion and Machine Learning Approaches for Melanoma Skin Cancer Detection

Whole-Genome Analysis of De Novo Somatic Point Mutations Reveals Novel Mutational Biomarkers in Pancreatic Cancer

An Investigation of Point Mutations Discovers Novel Genes and Their Corresponding Motifs in Pancreatic Cancer

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