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Shaokai Wang

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4 papers

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JBHI Journal 2025 Journal Article

AGPred: An End-to-End Deep Learning Model for Predicting Drug Approvals in Clinical Trials Based on Molecular Features

Haochen Zhao
Xiao Liang
Chenliang Xie
Shaokai Wang

One of the major challenges in drug development is maintaining acceptable levels of efficacy and safety throughout the various stages of clinical trials and successfully bringing the drug to market. However, clinical trials are time-consuming and expensive. While there are computational methods designed to predict the likelihood of a drug passing clinical trials and reaching the market, these methods heavily rely on manual feature engineering and cannot automatically learn drug molecular representations, resulting in relatively low model performance. In this study, we propose AGPred, an attention-based deep Graph Neural Network (GNN) designed to predict drug approval rates in clinical trials accurately. Unlike the few existing studies on drug approval prediction, which only use predicted targets of compounds, our novel approach employs a GNN module to extract high-potential features of compounds based on their molecular graphs. Additionally, a cross-attention-based fusion module is utilized to learn molecular fingerprint features, enhancing the model's representation of chemical structures. Meanwhile, AGPred integrates the physicochemical properties of drugs to provide a comprehensive description of the molecules. Experimental results indicate that AGPred outperforms four state-of-the-art models on both benchmark and independent datasets. The study also includes several ablation experiments and visual analyses to demonstrate the effectiveness of our method in predicting drug approval during clinical trials.

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JBHI Journal 2025 Journal Article

Anti-Cancer Peptides Identification and Activity Type Classification With Protein Sequence Pre-Training

Shaokai Wang
Bin Ma

Cancer remains a significant global health challenge, responsible for millions of deaths annually. Addressing this issue necessitates the discovery of novel anti-cancer drugs. Anti-cancer peptides (ACPs), with their unique ability to selectively target cancer cells, offer new hope in discovering low side-effect anti-cancer drugs. However, the process of discovering novel ACPs is both time-consuming and costly. Therefore, there is an urgent need for a computational method that can predict whether a given peptide is an ACP and classify its specific functional types. In this paper, we introduce DUO-ACP, a model serving dual roles in ACP prediction: identification and functional type classification. DUO-ACP employs two embedding modules to acquire knowledge about global protein features and local ACP characteristics, complemented by a prediction module. When assessed on two publicly available datasets for each task, DUO-ACP surpasses all existing methods, achieving outstanding results: an ACP identification accuracy of 89. 5% and a Macro-averaged AUC of 88. 6% in ACP functional type classification. We further interpret the contribution of each part of our model, including the two types of embeddings as well as ensemble learning. On a new curated dataset, the prediction results of DUO-ACP closely match existing literature, highlighting DUO-ACP's generalization capabilities on previously unseen data and displaying the potential capability of discovering novel ACP.

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TIST Journal 2023 Journal Article

Prior Knowledge Constrained Adaptive Graph Framework for Partial Label Learning

Gengyu Lyu
Songhe Feng
Shaokai Wang
Zhen Yang

Partial label learning (PLL) aims to learn a robust multi-class classifier from the ambiguous data, where each instance is given with several candidate labels, among which only one label is real. Most existing methods usually cope with such problem by utilizing a feature similarity graph to conduct label disambiguation. However, these methods construct the feature graph by only employing original features, while the influences of latent outliers and the contributions of label space are regrettably ignored. To tackle these issues, in this article, we propose a P rior Kn O wledge Cons T rained A daptive G raph Fram E work ( POTAGE ) for partial label learning, which utilizes an adaptive graph fused with label information to accurately describe the instance relationship and guide the desired model training. Compared with the feature-induced fixed graph, the adaptive graph is deemed to be more robust and accurate to reveal the intrinsic manifold structure within the data, and the embedding label information is expected to effectively alleviate the label ambiguities and enlarge the gap of label confidences between two instances from different classes. Extensive experiments demonstrate that POTAGE achieves state-of-the-art performance.

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TCS Journal 2018 Journal Article

Parameterized counting matching and packing: A family of hard problems that admit FPTRAS

Yunlong Liu
Shaokai Wang
Jianxin Wang

In the field of parameterized counting complexity, the problems that are #W[1]-hard and admit fixed-parameter tractable randomized approximation scheme (FPTRAS) have attracted much attention in recent years. In this paper, we focus on the problems on parameterized counting matching and packing. These problems include counting set packing, counting matching, and counting subgraph packing (including both vertex-disjoint and edge-disjoint versions). We study the parameterized complexity on these problems. On the basis of some results for counting graph matchings, we show that a series of problems are #W[1]-hard. Furthermore, by extending the previous algorithm for counting 3-d matching, we obtain FPTRAS for each considered problem, respectively. Our results indicate that the problems on parameterized counting matching and packing form a large family of problems that are #W[1]-hard and admit FPTRAS.

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