implementation of Sparse Representation classifier on Anticancer piptides
Data_set used for different models scraped from below given links
1_for acp_344:
2_for acp_740 and acp_240 :
a. https://raw.githubusercontent.com/haichengyi/ACP-DL/master/acp740.txt
b. https://raw.githubusercontent.com/haichengyi/ACP-DL/master/acp240.txt
NOTE:Due to any reason if you cannot download data set from above link then data set also available in our Data_set.zip folder.
No of ACP and NON-ACP in data set are given below:
| DATA_SET | ACP | NON_ACP |
|---|---|---|
| ACP_740 | 376 | 364 |
| COMBINED_DATA_SET | 505 | 475 |
| ACP_344 | 138 | 206 |
Performance Results:
| model | SEN | SPEC | F1_SCORE | B_ACC | Y_I | MCC |
|---|---|---|---|---|---|---|
| ACP_Kernel-SRC_740 | 86.23 | 81.62 | 0.84 | 83.94 | 0.67 | 67.11 |
| ACP_Kernel-SRC_344 | 97.07 | 86.97 | 94.11 | 91.89 | 0.84 | 0.85 |
we have implemented our Kernel-SRC model on acp_740 data_set ,Combine both acp_740 and acp_240 data set and then implement our kernel-SRC model and compare with LSTM model from ACP_DA paper[1]. we have also implemented our Kernel-SRC model on ACP_344 data set and compare our result with paper[2]. Our model perfomance on both data_Set is better than papers model. All figures of the Kernel-SRC model inlcuded in Figures folder
Refrences:
1.https://www.frontiersin.org/articles/10.3389/fgene.2021.698477/full
2.https://www.frontiersin.org/articles/10.3389/fbioe.2020.00892/full