ILGBMSH: an interpretable classification model for the shRNA target prediction with ensemble learning algorithm

Abstract

Short hairpin RNA (shRNA)-mediated gene silencing is an important technology to achieve RNA interference, in which the design of potent and reliable shRNA molecules plays a crucial role. However, efficient shRNA target selection through biological technology is expensive and time consuming. Hence, it is crucial to develop a more precise and efficient computational method to design potent and reliable shRNA molecules. In this work, we present an interpretable classification model for the shRNA target prediction using the Light Gradient Boosting Machine algorithm called ILGBMSH. Rather than utilizing only the shRNA sequence feature, we extracted 554 biological and deep learning features, which were not considered in previous shRNA prediction research. We evaluated the performance of our model compared with the state-of-the-art shRNA target prediction models. Besides, we investigated the feature explanation from the model’s parameters and interpretable method called Shapley Additive Explanations, which provided us with biological insights from the model. We used independent shRNA experiment data from other resources to prove the predictive ability and robustness of our model. Finally, we used our model to design the miR30-shRNA sequences and conducted a gene knockdown experiment. The experimental result was perfectly in correspondence with our expectation with a Pearson’s coefficient correlation of 0.985. In summary, the ILGBMSH model can achieve state-of-the-art shRNA prediction performance and give biological insights from the machine learning model parameters.

PD-1 downregulation enhances CAR-T cell antitumor efficiency by preserving a cell memory phenotype and reducing exhaustion

“…Our previously established computational method was adopted to optimize the sequences of shRNAs for accuracy and efficiency purposes. 30 Based on the AI technology guiding, 31 we selected and confirmed an optimal PD-1-specific shRNA sequence. OX-40 was incorporated into the CAR construct as a costimulatory signal to achieve greater proliferation and enhanced immune memory development in a repeated stimulation assay using BCMA-expressing target cells.…”

A score-based method of immune status evaluation for healthy individuals with complete blood cell counts

“…LightGBM is another framework that implements the GBDT algorithm, which supports efficient parallel training, and has faster training speed, lower memory consumption and better accuracy [ 34 ]. This method has been applied to the interpretability of classification, as evidenced by previous studies [ 35 ]. The dataset was also split into a training set accounting for 80% of all and a test set of 20%, and tenfold cross-validation was used to adjust hyperparameters to build the best model [ 36 ].…”

TROLLOPE: A novel sequence-based stacked approach for the accelerated discovery of linear T-cell epitopes of hepatitis C virus

“…Therefore, we employed an interpretable approach, named the Shapley Additive exPlanations (SHAP), to rank and evaluate the feature importance for TROLLOPE and its constituent base-classifiers. Until now, the SHAP method has been successfully used in various bioinformatics tasks [68][69][70][71]. Firstly, the top-six informative probabilistic features of TROLLOPE were assessed for their importance in TCE-HCV identification.…”