Identification of cancerlectin proteins using hyperparameter optimization in deep learning and DDE profiles
This study focuses on the development, metastasis, and spread of cancer diseases. It is therefore very desirable to establish deep learning method that classify cancerlectin proteins function efficiently and effectively. We used feature extraction model for physicochemical properties, such as Cancer...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Mehran University of Engineering and Technology
2023-09-01
|
| Series: | Mehran University Research Journal of Engineering and Technology |
| Online Access: | https://publications.muet.edu.pk/index.php/muetrj/article/view/2793 |
| _version_ | 1827798048699318272 |
|---|---|
| author | Rahu Sikander Ali Ghulam Jawad Hassan Laiba Rehman Nida Jabeen Natasha Iqbal |
| author_facet | Rahu Sikander Ali Ghulam Jawad Hassan Laiba Rehman Nida Jabeen Natasha Iqbal |
| author_sort | Rahu Sikander |
| collection | DOAJ |
| description | This study focuses on the development, metastasis, and spread of cancer diseases. It is therefore very desirable to establish deep learning method that classify cancerlectin proteins function efficiently and effectively. We used feature extraction model for physicochemical properties, such as Cancerlectins protein structure, functions, and other compounds. We propose a computational method, namely, cancerlectin two-dimensional convolutional neural networks (Lectin2D-CNN), for predicting cancerlectin proteins. Additionally, we conduct the cross-validation experiments. In addition to this approach, our paper proposes using cancerlectin two-dimensional convolutional neural networks (Lectin2D-CNN) to do image-based classification. The results indicate the proposed method Lectin2D-CNN achieved high accuracy and satisfactory specificity for comparison data sets and was superior to the compared methods. Various classifiers were used to predict cancerlectin protein functions. We developed a prediction model based on the 2D-CNN architecture to increase the recognition sensitivity and accuracy for cancerlectins. Results provide a basis for the estimation of cancer lectins and demonstrate deep learning approaches in in computational proteomics. When the Cross-validation using 2D-CNN random number generator has produced accuracy score obtain 0.7169%, Sensitivity score obtain 0.7012%, Specificity score obtain 0.7326%, MCC score obtain 0.4428%, ROC-AUC score is 0.76%, respectively, then we know we've attained a reliable result. When the Independent datasets using 2D-CNN random number generator has produced accuracy score obtain 0.6375%, Sensitivity score obtain 0.6160%, Specificity score obtain 0.6589%, MCC score obtain 0.2851%, and ROC (auc) score is 0.76%, respectively. |
| first_indexed | 2024-03-11T19:31:28Z |
| format | Article |
| id | doaj.art-1c481d65628b457d85832bcfc4ee7214 |
| institution | Directory Open Access Journal |
| issn | 0254-7821 2413-7219 |
| language | English |
| last_indexed | 2024-03-11T19:31:28Z |
| publishDate | 2023-09-01 |
| publisher | Mehran University of Engineering and Technology |
| record_format | Article |
| series | Mehran University Research Journal of Engineering and Technology |
| spelling | doaj.art-1c481d65628b457d85832bcfc4ee72142023-10-06T13:16:52ZengMehran University of Engineering and TechnologyMehran University Research Journal of Engineering and Technology0254-78212413-72192023-09-01424284010.22581/muet1982.2304.27932793Identification of cancerlectin proteins using hyperparameter optimization in deep learning and DDE profilesRahu Sikander0Ali Ghulam1Jawad Hassan2Laiba Rehman3Nida Jabeen4Natasha Iqbal5Center for Computing Research, Department of Computer Science and Software Engineering, Jinnah University for Women, Karachi PakistanInformation Technology Centre, Sindh Agriculture University, Tandojam, Sindh PakistanDepartment of Computer Science and Engineering, Air University Multan Campus, Multan, Punjab PakistanWomen university MultanCollege of information and compute, Taiyuan university of Technology 030024, Shanxi Taiyuan ChinaDepartment of Botany, Government College University of Faisalabad, 37000, Faisalabad PakistanThis study focuses on the development, metastasis, and spread of cancer diseases. It is therefore very desirable to establish deep learning method that classify cancerlectin proteins function efficiently and effectively. We used feature extraction model for physicochemical properties, such as Cancerlectins protein structure, functions, and other compounds. We propose a computational method, namely, cancerlectin two-dimensional convolutional neural networks (Lectin2D-CNN), for predicting cancerlectin proteins. Additionally, we conduct the cross-validation experiments. In addition to this approach, our paper proposes using cancerlectin two-dimensional convolutional neural networks (Lectin2D-CNN) to do image-based classification. The results indicate the proposed method Lectin2D-CNN achieved high accuracy and satisfactory specificity for comparison data sets and was superior to the compared methods. Various classifiers were used to predict cancerlectin protein functions. We developed a prediction model based on the 2D-CNN architecture to increase the recognition sensitivity and accuracy for cancerlectins. Results provide a basis for the estimation of cancer lectins and demonstrate deep learning approaches in in computational proteomics. When the Cross-validation using 2D-CNN random number generator has produced accuracy score obtain 0.7169%, Sensitivity score obtain 0.7012%, Specificity score obtain 0.7326%, MCC score obtain 0.4428%, ROC-AUC score is 0.76%, respectively, then we know we've attained a reliable result. When the Independent datasets using 2D-CNN random number generator has produced accuracy score obtain 0.6375%, Sensitivity score obtain 0.6160%, Specificity score obtain 0.6589%, MCC score obtain 0.2851%, and ROC (auc) score is 0.76%, respectively.https://publications.muet.edu.pk/index.php/muetrj/article/view/2793 |
| spellingShingle | Rahu Sikander Ali Ghulam Jawad Hassan Laiba Rehman Nida Jabeen Natasha Iqbal Identification of cancerlectin proteins using hyperparameter optimization in deep learning and DDE profiles Mehran University Research Journal of Engineering and Technology |
| title | Identification of cancerlectin proteins using hyperparameter optimization in deep learning and DDE profiles |
| title_full | Identification of cancerlectin proteins using hyperparameter optimization in deep learning and DDE profiles |
| title_fullStr | Identification of cancerlectin proteins using hyperparameter optimization in deep learning and DDE profiles |
| title_full_unstemmed | Identification of cancerlectin proteins using hyperparameter optimization in deep learning and DDE profiles |
| title_short | Identification of cancerlectin proteins using hyperparameter optimization in deep learning and DDE profiles |
| title_sort | identification of cancerlectin proteins using hyperparameter optimization in deep learning and dde profiles |
| url | https://publications.muet.edu.pk/index.php/muetrj/article/view/2793 |
| work_keys_str_mv | AT rahusikander identificationofcancerlectinproteinsusinghyperparameteroptimizationindeeplearningandddeprofiles AT alighulam identificationofcancerlectinproteinsusinghyperparameteroptimizationindeeplearningandddeprofiles AT jawadhassan identificationofcancerlectinproteinsusinghyperparameteroptimizationindeeplearningandddeprofiles AT laibarehman identificationofcancerlectinproteinsusinghyperparameteroptimizationindeeplearningandddeprofiles AT nidajabeen identificationofcancerlectinproteinsusinghyperparameteroptimizationindeeplearningandddeprofiles AT natashaiqbal identificationofcancerlectinproteinsusinghyperparameteroptimizationindeeplearningandddeprofiles |