arXiv subjects

Wenting Ye

Publications and source records attributed to Wenting Ye.

5 recordsLinked to original sources

arXiv2023

Causal Intervention for Measuring Confidence in Drug-Target Interaction Prediction

Identifying and discovering drug-target interactions(DTIs) are vital steps in drug discovery and development. They play a crucial role in assisting scientists in finding new drugs and accelerating the drug development process. Recently, knowledge graph and knowledge graph embedding (KGE) models have made rapid advancements and demonstrated impressive performance in drug discovery. However, such models lack authenticity and accuracy in drug target identification, leading to an increased misjudgment rate and reduced drug development efficiency. To address these issues, we focus on the problem of drug-target interactions, with knowledge mapping as the core technology. Specifically, a causal intervention-based confidence measure is employed to assess the triplet score to improve the accuracy of the drug-target interaction prediction model. Experimental results demonstrate that the developed confidence measurement method based on causal intervention can significantly enhance the accuracy of DTI link prediction, particularly for high-precision models. The predicted results are more valuable in guiding the design and development of subsequent drug development experiments, thereby significantly improving the efficiency of drug development.

q-bio.QM↗

arXiv2022

A Transformer-Based Substitute Recommendation Model Incorporating Weakly Supervised Customer Behavior Data

The substitute-based recommendation is widely used in E-commerce to provide better alternatives to customers. However, existing research typically uses the customer behavior signals like co-view and view-but-purchase-another to capture the substitute relationship. Despite its intuitive soundness, we find that such an approach might ignore the functionality and characteristics of products. In this paper, we adapt substitute recommendation into language matching problem by taking product title description as model input to consider product functionality. We design a new transformation method to de-noise the signals derived from production data. In addition, we consider multilingual support from the engineering point of view. Our proposed end-to-end transformer-based model achieves both successes from offline and online experiments. The proposed model has been deployed in a large-scale E-commerce website for 11 marketplaces in 6 languages. Our proposed model is demonstrated to increase revenue by 19% based on an online A/B experiment.

cs.IR↗

arXiv2021

Design and Characterization of Self Lubricating Refractory High Entropy Alloy Based Multilayered Films

Refractory high entropy alloys (RHEA) have been proven to have excellent mechanical properties with a potential use as protective thin films. However, the combination of high hardness with low friction and wear is a major challenge in the design of self lubricating RHEA films. In this study, we show that designing of NbMoWTa/Ag multilayered films give a remarkable reduction in friction and at same time maintain high hardness. Interestingly, it's found that the bcc superlattice dominates in both layers and the interfaces are highly coherent when the individual layer thickness h is reduced below 10 nm. The film properties are then strongly dependent on h ranging from 100 to 2.5 nm, and the most promising properties are obtained when the interface structure transforms from incoherent to coherent ones. Specially, the multilayer with h = 2.5 nm exhibits superior tribological performance over monolithic NbMoWTa, due to the significant coherent strengthening along with the self-lubricating ability in the multilayer. This tailored phase transition and coherent structure between matrix and lubrication phases can also provide an optimal wear rate-coefficient of friction (COF) combination, which is higher than most of the Ag containing self lubricating films. The current work might open a new route towards the development of innovative self lubricating RHEA films with excellent tribological properties in general.

cond-mat.mtrl-sci↗

arXiv2017

Sparse Variable Selection on High Dimensional Heterogeneous Data with Tree Structured Responses

We consider the problem of sparse variable selection on high dimension heterogeneous data sets, which has been taking on renewed interest recently due to the growth of biological and medical data sets with complex, non-i.i.d. structures and huge quantities of response variables. The heterogeneity is likely to confound the association between explanatory variables and responses, resulting in enormous false discoveries when Lasso or its variants are na\"ively applied. Therefore, developing effective confounder correction methods is a growing heat point among researchers. However, ordinarily employing recent confounder correction methods will result in undesirable performance due to the ignorance of the convoluted interdependency among response variables. To fully improve current variable selection methods, we introduce a model, the tree-guided sparse linear mixed model, that can utilize the dependency information from multiple responses to explore how specifically clusters are and select the active variables from heterogeneous data. Through extensive experiments on synthetic and real data sets, we show that our proposed model outperforms the existing methods and achieves the highest ROC area.

stat.ME↗

arXiv2017

A Sparse Graph-Structured Lasso Mixed Model for Genetic Association with Confounding Correction

While linear mixed model (LMM) has shown a competitive performance in correcting spurious associations raised by population stratification, family structures, and cryptic relatedness, more challenges are still to be addressed regarding the complex structure of genotypic and phenotypic data. For example, geneticists have discovered that some clusters of phenotypes are more co-expressed than others. Hence, a joint analysis that can utilize such relatedness information in a heterogeneous data set is crucial for genetic modeling. We proposed the sparse graph-structured linear mixed model (sGLMM) that can incorporate the relatedness information from traits in a dataset with confounding correction. Our method is capable of uncovering the genetic associations of a large number of phenotypes together while considering the relatedness of these phenotypes. Through extensive simulation experiments, we show that the proposed model outperforms other existing approaches and can model correlation from both population structure and shared signals. Further, we validate the effectiveness of sGLMM in the real-world genomic dataset on two different species from plants and humans. In Arabidopsis thaliana data, sGLMM behaves better than all other baseline models for 63.4% traits. We also discuss the potential causal genetic variation of Human Alzheimer's disease discovered by our model and justify some of the most important genetic loci.

cs.LG↗