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Articles by Yansen Su
Systematic modeling predicts synergistic and safe drug combinations for parasitic diseases
Loading metrics Open Access Peer-reviewed Research Article ? This is an uncorrected proof. Citation: Su Y, Zhang H, Du Y, Li L, Lv G, Jiang H (2026) Systematic modeling predicts synergistic and safe drug combinations for parasitic diseases. PLoS Negl Trop Dis 20(2): e0013991. https://doi.org/10.1371/journal.pntd.0013991 Editor: Murtala Isah, Umaru Musa Yar’Adua University, NIGERIA Received: September 16, 2025; Accepted: February 2, 2026; Published: February 19, 2026 Copyright: © 2026 Su et al.
The SynMall resource for characterizing the functional impact of synonymous variation
1 1Information Materials and Intelligent Sensing Laboratory of Anhui Province and School of Life Sciences and Medical Engineering, Anhui University, Hefei, Anhui 230601, China; 2School of Biomedical Engineering, Anhui Medical University, Hefei, Anhui 230032, China; 3School of Artificial Intelligence, Anhui University, Hefei, Anhui 245041, China Corresponding authors: jfxia{at}ahu.edu.cn, lixiaoyan{at}ahu.edu.cn Footnotes [Supplemental material is available for this article.] Article published...
LM-DTI: a tool of predicting drug-target interactions using the node2vec and network path score methods
1 Introduction Identifying new drug-target interactions (DTIs) is a crucial step in a variety of biomedical and multi-pharmacological applications, such as drug discovery, drug relocation (Jarada et al., 2020), drug resistance, and side effect prediction (Masoudi-Nejad et al., 2013).
scDSSC: Deep Sparse Subspace Clustering for scRNA-seq Data
Loading metrics Open Access Peer-reviewed Research Article ? This is an uncorrected proof. Abstract Single cell RNA sequencing (scRNA-seq) enables researchers to characterize transcriptomic profiles at the single-cell resolution with increasingly high throughput. Clustering is a crucial step in single cell analysis. Clustering analysis of transcriptome profiled by scRNA-seq can reveal the heterogeneity and diversity of cells.
Machine learning prediction of network dynamics with privacy protection
Predicting network dynamics based on data, a problem with broad applications, has been studied extensively in the past, but most existing approaches assume that the complete set of historical data from the whole network is available. This requirement presents a great challenge in applications, especially for large, distributed networks in the real world, where data collection is accomplished by many clients in a parallel fashion.
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