Using Machine Learning Classifiers to Identify the Critical Proteins in Down Syndrome

Kulan, Handan; Dağ, Tamer

doi:10.1145/3290818.3290831

Cited by 5 publications

(8 citation statements)

References 18 publications

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“…The first problem is multi class classification of all the data. Our results will be to compared with Kulan et al 24 and B.Feng et al 23 for this problem. They have reduced the dimension of the data from 77 proteins (excluding the categorical values) to 30 proteins.…”

Section: Feature Selectionmentioning

confidence: 80%

“…As mentioned above, for this problem we are going to compare our results with Kulan et al [25] and B.Feng et al [24]. For classification technique 10-fold cross validation result.…”

Section: Multiclass Classification Problemmentioning

confidence: 94%

“…In 23 B. Feng used adaptive boosted Decision Tree (AdaBoost) method as forward feature selection to identify the most correlated proteins, then they used Random Forest (RF), SVM, and Decision Tree (DT) algorithms for classification. On their paper, Kulan et al 24 have used the same procedure steps as B.Feng et al by only changing the feature selection method. They thought that Naïve Bayes would discriminate the protein better than AdaBoost, and they were right, since they got a higher accuracy than the previous work.…”

Section: Introductionmentioning

confidence: 99%

“…They thought that Naïve Bayes would discriminate the protein better than AdaBoost, and they were right, since they got a higher accuracy than the previous work. On their recent paper, Kulan et al 24 compared their results with the ones obtained from Higuera et al related to finding the critical proteins that affect three types of different learning, such as successful, rescued and failed learning. They used again Naïve Bayes for feature selection in order to identify the correlated proteins, and their results were higher than the previous ones.…”

Section: Introductionmentioning

confidence: 99%

“…After feature selection, RF, SVM, Neural network (NN), and K Nearest Neighbour (KNN) classification algorithms were applied in order to find the critical proteins in two different problems: multiclass classification and learning process. For multiclass classification, we compared our results with B. Feng et al and Kulan et al 24 works in which AdaBoost and Naïve Bayes were used for feature selection. Regarding the learning process, we compared our results with Higuera et al work with Kulan et al 25 that used SOM and supervised learning respectively.…”

Section: Introductionmentioning

confidence: 99%

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Discovering critical proteins in the learning process in a Down Syndrome model of mouse through machine learning

Polisi

Topal

Uka

2021

Preprint

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Caused by an extra copy of the human chromosome21 (Hsa21), Down syndrome produces an intellectual disability that is still unknown and requires further research in order to have a better perception. One research conducted in this area of study has analysed different protein levels of the Ts65Dn mouse model of DS. Many researchers are trying to find the critical proteins that categorize the mice classes accurately by using machine learning. In this study, we expand the problem by trying to find the critical proteins that affect different types of learning. The protein subsets are found using forward feature selection method, ReliefF respectively and four different supervised learning algorithms are used. The experimental results are compared with previous related work, and demonstrated that the proposed method outperforms, or is comparable to, its competitors in term of accuracy. Then, a thorough analysis is done to identify the critical proteins for each learning case, by lowering the number to 9 critical proteins that can help in a better categorization of the mice. We hope that our work withhelp the scientists on their further research on finding a treatment that may help the learning process and ease the intellectual disability caused by Down Syndrome.

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Section: Feature Selectionmentioning

confidence: 80%

“…As mentioned above, for this problem we are going to compare our results with Kulan et al [25] and B.Feng et al [24]. For classification technique 10-fold cross validation result.…”

Section: Multiclass Classification Problemmentioning

confidence: 94%