2020
DOI: 10.1109/access.2020.3003999
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Abstract: Based on complex networks and machine learning methods, this paper studies the mining of colorectal cancer treatment genes, and innovatively combines a variety of feature extraction and comparative analysis methods, from gene network features, gene attribute features, network and attribute integration The three aspects of characteristics comprehensively excavate the genetic characteristics, and demonstrate the feasibility of the study through comparative analysis from different perspectives. Constructing a col… Show more

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Cited by 9 publications
(5 citation statements)
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References 49 publications
(49 reference statements)
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“…J-48, on the other hand, performed best across the board. Yanke et al [15] mined 7 colorectal cancer related datasets using their new technique that combined NB (Naïve Bayes), RF and DT. After analysis the final result are optimized using an appropriate optimization technique.…”
Section: Related Research Workmentioning
confidence: 99%
See 1 more Smart Citation
“…J-48, on the other hand, performed best across the board. Yanke et al [15] mined 7 colorectal cancer related datasets using their new technique that combined NB (Naïve Bayes), RF and DT. After analysis the final result are optimized using an appropriate optimization technique.…”
Section: Related Research Workmentioning
confidence: 99%
“…It was found that all the three algorithms were performing well but kNN outperforms the other two by a difference of around 0.4 percent in accuracy. ROC [11], [14], [15], [18] 4 AUC [11], [13], [15], [18] 4 F-Measure [13], [14], [18], [19] 4…”
Section: Related Research Workmentioning
confidence: 99%
“…Although numerous strategies have been tried to discuss cancer's complicated mutational heterogeneity, driver genes discovery continues to remain a difficulty owing to the restricted capacity to integrate other genomic elements for integrated studies [4]. Other genomic elements, including as nonsense mutations, penetrations, and omissions, as well as germ line variations, have been previously overlooked but now have recently been discovered to play an important effect on cancer formation [9] [21] [22]. When genomic components, such as somatic mutations, germ line variations, insertions and deletions, are investigated jointly, particularly inside an interaction term, modeling intricacy as well as investigation strength are obstacles [12].…”
Section: Introductionmentioning
confidence: 99%
“…These traditional teaching and learning algorithms are often created by domain specialists and depend significantly on data models. The human genome's sophistication as well as the degree of exercise requisite makes it tough to derive important information, whereas deep learning can gain knowledge a good feature representation automatically [22]. With power of parallel processing and advanced algorithms, deep learning has recently emerged as a result of advances in big data.…”
Section: Introductionmentioning
confidence: 99%
“…In Colorectal Cancer (CRC), the morphology of intestinal gland includes gland formation whereas architectural appearance is a primary criterion for cancer grading. Glands are significant histological structures in almost all organs of the body with a primary purpose to achieve i.e., to secret carbohydrates and proteins [6]. Human colon contains masses of glands in it.…”
Section: Introductionmentioning
confidence: 99%