Dataset Information

ACP-ADA: A Boosting Method with Data Augmentation for Improved Prediction of Anticancer Peptides.

ABSTRACT: Cancer is the second-leading cause of death worldwide, and therapeutic peptides that target and destroy cancer cells have received a great deal of interest in recent years. Traditional wet experiments are expensive and inefficient for identifying novel anticancer peptides; therefore, the development of an effective computational approach is essential to recognize ACP candidates before experimental methods are used. In this study, we proposed an Ada-boosting algorithm with the base learner random forest called ACP-ADA, which integrates binary profile feature, amino acid index, and amino acid composition with a 210-dimensional feature space vector to represent the peptides. Training samples in the feature space were augmented to increase the sample size and further improve the performance of the model in the case of insufficient samples. Furthermore, we used five-fold cross-validation to find model parameters, and the cross-validation results showed that ACP-ADA outperforms existing methods for this feature combination with data augmentation in terms of performance metrics. Specifically, ACP-ADA recorded an average accuracy of 86.4% and a Mathew's correlation coefficient of 74.01% for dataset ACP740 and 90.83% and 81.65% for dataset ACP240; consequently, it can be a very useful tool in drug development and biomedical research.

SUBMITTER: Bhattarai S

PROVIDER: S-EPMC9603247 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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ACP-ADA: A Boosting Method with Data Augmentation for Improved Prediction of Anticancer Peptides.

Bhattarai Sadik S Kim Kyu-Sik KS Tayara Hilal H Chong Kil To KT

International journal of molecular sciences 20221013 20

Cancer is the second-leading cause of death worldwide, and therapeutic peptides that target and destroy cancer cells have received a great deal of interest in recent years. Traditional wet experiments are expensive and inefficient for identifying novel anticancer peptides; therefore, the development of an effective computational approach is essential to recognize ACP candidates before experimental methods are used. In this study, we proposed an Ada-boosting algorithm with the base learner random ...[more]

PMID: 36293050

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Project description:As anticancer peptides (ACPs) have attracted great interest for cancer treatment, several approaches based on machine learning have been proposed for ACP identification. Although existing methods have afforded high prediction accuracies, however such models are using a large number of descriptors together with complex ensemble approaches that consequently leads to low interpretability and thus poses a challenge for biologists and biochemists. Therefore, it is desirable to develop a simple, interpretable and efficient predictor for accurate ACP identification as well as providing the means for the rational design of new anticancer peptides with promising potential for clinical application. Herein, we propose a novel flexible scoring card method (FSCM) making use of propensity scores of local and global sequential information for the development of a sequence-based ACP predictor (named iACP-FSCM) for improving the prediction accuracy and model interpretability. To the best of our knowledge, iACP-FSCM represents the first sequence-based ACP predictor for rationalizing an in-depth understanding into the molecular basis for the enhancement of anticancer activities of peptides via the use of FSCM-derived propensity scores. The independent testing results showed that the iACP-FSCM provided accuracies of 0.825 and 0.910 as evaluated on the main and alternative datasets, respectively. Results from comparative benchmarking demonstrated that iACP-FSCM could outperform seven other existing ACP predictors with marked improvements of 7% and 17% for accuracy and MCC, respectively, on the main dataset. Furthermore, the iACP-FSCM (0.910) achieved very comparable results to that of the state-of-the-art ensemble model AntiCP2.0 (0.920) as evaluated on the alternative dataset. Comparative results demonstrated that iACP-FSCM was the most suitable choice for ACP identification and characterization considering its simplicity, interpretability and generalizability. It is highly anticipated that the iACP-FSCM may be a robust tool for the rapid screening and identification of promising ACPs for clinical use.

Project description:IntroductionThe purpose of this study was to determine whether data preprocessing and augmentation could improve visual field (VF) prediction of recurrent neural network (RNN) with multi-central datasets.MethodsThis retrospective study collected data from five glaucoma services between June 2004 and January 2021. From an initial dataset of 331,691 VFs, we considered reliable VF tests with fixed intervals. Since the VF monitoring interval is very variable, we applied data augmentation using multiple sets of data for patients with more than eight VFs. We obtained 5,430 VFs from 463 patients and 13,747 VFs from 1,076 patients by setting the fixed test interval to 365 ± 60 days (D = 365) and 180 ± 60 days (D = 180), respectively. Five consecutive VFs were provided to the constructed RNN as input and the 6th VF was compared with the output of the RNN. The performance of the periodic RNN (D = 365) was compared to that of an aperiodic RNN. The performance of the RNN with 6 long- and short-term memory (LSTM) cells (D = 180) was compared with that of the RNN with 5-LSTM cells. To compare the prediction performance, the root mean square error (RMSE) and mean absolute error (MAE) of the total deviation value (TDV) were calculated as accuracy metrics.ResultsThe performance of the periodic model (D = 365) improved significantly over aperiodic model. Overall prediction error (MAE) was 2.56 ± 0.46 dB versus 3.26 ± 0.41 dB (periodic vs. aperiodic) (p < 0.001). A higher perimetric frequency was better for predicting future VF. The overall prediction error (RMSE) was 3.15 ± 2.29 dB versus 3.42 ± 2.25 dB (D = 180 vs. D = 365). Increasing the number of input VFs improved the performance of VF prediction in D = 180 periodic model (3.15 ± 2.29 dB vs. 3.18 ± 2.34 dB, p < 0.001). The 6-LSTM in the D = 180 periodic model was more robust to worsening of VF reliability and disease severity. The prediction accuracy worsened as the false-negative rate increased and the mean deviation decreased.ConclusionData preprocessing with augmentation improved the VF prediction of the RNN model using multi-center datasets. The periodic RNN model predicted the future VF significantly better than the aperiodic RNN model.

Project description:BackgroundCancer remains one of the leading causes of mortality globally, with conventional chemotherapy often resulting in severe side effects and limited effectiveness. Recent advancements in bioinformatics and machine learning, particularly deep learning, offer promising new avenues for cancer treatment through the prediction and identification of anticancer peptides.ObjectiveThis study aimed to develop and evaluate a deep learning model utilizing a two-dimensional convolutional neural network (2D CNN) to enhance the prediction accuracy of anticancer peptides, addressing the complexities and limitations of current prediction methods.MethodsA diverse dataset of peptide sequences with annotated anticancer activity labels was compiled from various public databases and experimental studies. The sequences were preprocessed and encoded using one-hot encoding and additional physicochemical properties. The 2D CNN model was trained and optimized using this dataset, with performance evaluated through metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC).ResultsThe proposed 2D CNN model achieved superior performance compared to existing methods, with an accuracy of 0.87, precision of 0.85, recall of 0.89, F1-score of 0.87, and an AUC-ROC value of 0.91. These results indicate the model's effectiveness in accurately predicting anticancer peptides and capturing intricate spatial patterns within peptide sequences.ConclusionThe findings demonstrate the potential of deep learning, specifically 2D CNNs, in advancing the prediction of anticancer peptides. The proposed model significantly improves prediction accuracy, offering a valuable tool for identifying effective peptide candidates for cancer treatment.Future workFurther research should focus on expanding the dataset, exploring alternative deep learning architectures, and validating the model's predictions through experimental studies. Efforts should also aim at optimizing computational efficiency and translating these predictions into clinical applications.

Project description:Pertrochanteric fractures (TF) due to osteoporosis constitute nearly half of all proximal femur fractures. TFs are treated with a surgical approach and fracture fixation is achieved using metallic fixation devices. Poor quality cancellous bone in osteoporotic patients makes anchorage of a fixation device challenging, which can lead to failure of the fracture fixation. Methods to reinforce the bone-implant interface using bone cement (PMMA) and other calcium phosphate cements in TFs have been described earlier but a clear evidence on the advantage of using such biomaterials for augmentation is weak. Furthermore, there is no standardized technique for delivering these biomaterials at the bone-implant interface. In this study, we firstly describe a method to deliver a calcium sulphate/hydroxyapatite (CaS/HA) based biomaterial for the augmentation of a lag-screw commonly used for TF fixation. We then used an osteoporotic Sawbones model to study the consequence of CaS/HA augmentation on the immediate mechanical anchorage of the lag-screw to osteoporotic bone. Finally, as a proof-of-concept, the method of delivering the CaS/HA biomaterial at the bone-implant interface as well as spreading of the CaS/HA material at this interface was tested in patients undergoing treatment for TF as well as in donated femoral heads. The mechanical testing results indicated that the CaS/HA based biomaterial increased the peak extraction force of the lag-screw by 4 times compared with un-augmented lag-screws and the results were at par with PMMA. The X-ray images from the patient series showed that it was possible to inject the CaS/HA material at the bone-implant interface without applying additional pressure and the CaS/HA material spreading was observed at the interface of the lag-screw threads and the bone. Finally, the spreading of the CaS/HA material was also verified on donated femoral heads and micro-CT imaging indicated that the entire length of the lag-screw threads was covered with the CaS/HA biomaterial. In conclusion, we present a novel method for augmenting a lag-screw in TFs, which could potentially reduce the risk of fracture fixation failure and reoperation in fragile osteoporotic patients.

Dataset Information

ACP-ADA: A Boosting Method with Data Augmentation for Improved Prediction of Anticancer Peptides.

Publications

ACP-ADA: A Boosting Method with Data Augmentation for Improved Prediction of Anticancer Peptides.

Similar Datasets

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