PeNGaRoo, a combined gradient boosting and ensemble learning framework for predicting non-classical secreted proteins

Abstract

Motivation Gram-positive bacteria have developed secretion systems to transport proteins across their cell wall, a process that plays an important role during host infection. These secretion mechanisms have also been harnessed for therapeutic purposes in many biotechnology applications. Accordingly, the identification of features that select a protein for efficient secretion from these microorganisms has become an important task. Among all the secreted proteins, ‘non-classical’ secreted proteins are difficult to identify as they lack discernable signal peptide sequences and can make use of diverse secretion pathways. Currently, several computational methods have been developed to facilitate the discovery of such non-classical secreted proteins; however, the existing methods are based on either simulated or limited experimental datasets. In addition, they often employ basic features to train the models in a simple and coarse-grained manner. The availability of more experimentally validated datasets, advanced feature engineering techniques and novel machine learning approaches creates new opportunities for the development of improved predictors of ‘non-classical’ secreted proteins from sequence data. Results In this work, we first constructed a high-quality dataset of experimentally verified ‘non-classical’ secreted proteins, which we then used to create benchmark datasets. Using these benchmark datasets, we comprehensively analyzed a wide range of features and assessed their individual performance. Subsequently, we developed a two-layer Light Gradient Boosting Machine (LightGBM) ensemble model that integrates several single feature-based models into an overall prediction framework. At this stage, LightGBM, a gradient boosting machine, was used as a machine learning approach and the necessary parameter optimization was performed by a particle swarm optimization strategy. All single feature-based LightGBM models were then integrated into a unified ensemble model to further improve the predictive performance. Consequently, the final ensemble model achieved a superior performance with an accuracy of 0.900, an F-value of 0.903, Matthew’s correlation coefficient of 0.803 and an area under the curve value of 0.963, and outperforming previous state-of-the-art predictors on the independent test. Based on our proposed optimal ensemble model, we further developed an accessible online predictor, PeNGaRoo, to serve users’ demands. We believe this online web server, together with our proposed methodology, will expedite the discovery of non-classically secreted effector proteins in Gram-positive bacteria and further inspire the development of next-generation predictors. Availability and implementation http://pengaroo.erc.monash.edu/. Supplementary information Supplementary data are available at Bioinformatics online.

Extremely-randomized-tree-based Prediction of N6-methyladenosine Sites inSaccharomyces cerevisiae

“…Although our proposed method showed improved performance over other methods, it still has room for improvement. Recently, several novel computational approaches have been proposed in computational biology [68,78,[80][81][82][83][84][85] to identify function from the sequence. Hence, developing a novel prediction model by utilizing such approaches may improve the prediction performance.…”

NonClasGP-Pred: robust and efficient prediction of non-classically secreted proteins by integrating subset-specific optimal models of imbalanced data

“…We used these datasets for the following reasons. First, the proteins of the positive dataset, NCSPs of grampositive bacterial proteins, were experimentally verified, and each protein was confirmed by at least three different research groups in at least three different bacterial species [5,15]. Second, the sequence identity was reduced to 80 % to avoid potential redundancy.…”

“…An independent test dataset containing 34 positive samples and 34 negative samples was used for further performance evaluation and comparison. For more details regarding the benchmark datasets, see Zhang et al [15].…”

“…To improve the performance of the NonClasGP model, an ensemble learning model was built in this study, which used majority voting to integrate the prediction results of the above ten individual models, each of which was built on the optimal feature combinations. The performance of the ensemble model NonClasGP-Pred was evaluated by five commonly used metrics [15,40,[46][47][48][49][50][51][52][53][54][55][56][57][58][59]…”

AI-Enabled Diagnosis of Spontaneous Rupture of Ovarian Endometriomas: A PSO Enhanced Random Forest Approach

“…In this study, we leverage particle swarm optimization [34], which is a metaheuristic algorithm simulating the behavior of birds catching food, to tune the essential parameters of the RF model to further improve the accuracy. The essence of PSO is to use the current position, global extremum and individual extremum information to guide the next iteration position of particles, which enables it to approach the optimal solution with a fast convergence speed, and hence effectively optimize the parameters of the model [16], [35]. Another significant advantage of PSO is that it can adjust the maximum step size at each iteration, making it possible to find an approximate optimal solution in a wide range of possible parameters [36].…”