3 citations · 7 across the 13 of their papers we have counts for
13 papers
CliffRank: A Dual-Branch Framework for Activity-Cliff Ranking Prediction
Kewei Li, Rongying Zhang, Peiyu Yang +4
Activity-cliff ranking remains difficult because local structural changes can cause large activity differences, while high-quality data that resolve the underlying mechanisms remai…
FLaG: Frequency-Domain Latent-attention Gated Pooling for Token Aggregation
Kewei Li, Rongying Zhang, Xueli Wang +6
Token aggregation converts token-level representations into fixed-dimensional sample representations, but most pooling methods operate only in the original token space. We introduc…
DISTAL: Distillation and Self-Supervised Pretraining for Structure-Agnostic Materials Property Prediction
Weiran Wang, Xintong Huo, Yueying Wang +7
Materials property prediction remains difficult in low-data settings, where many target properties are supported by only a limited number of labeled samples. Models with the strong…
CMCNet: Aligning Ultrasound Image Embeddings with Textual TI-RADS Representations for Fine-Grained Thyroid Classification
Bingxin Yu, Xueli Wang, Jerry Zhou +6
Ultrasound is the primary imaging modality for assessing thyroid nodules, and the ACR TI-RADS framework standardizes diagnosis through five ultrasound feature categories that are a…
Frequency-Domain Latent Attention Gating for Cross-Domain Token Aggregation
Kewei Li, Rongying Zhang, Xueli Wang +5
Token aggregation is a common bottleneck in models that map token representations to sample-level predictions, yet most pooling methods operate only in the original token domain. W…
GrifFinNet: A Graph-Relation Integrated Transformer for Financial Predictions
Chenlanhui Dai, Wenyan Wang, Yusi Fan +4
Predicting stock returns remains a central challenge in quantitative finance, transitioning from traditional statistical methods to contemporary deep learning techniques. However,…
A transformer-BiGRU-based framework with data augmentation and confident learning for network intrusion detection
Jiale Zhang, Pengfei He, Fei Li +5
In today's fast-paced digital communication, the surge in network traffic data and frequency demands robust and precise network intrusion solutions. Conventional machine learning m…
DeepSelective: Interpretable Prognosis Prediction via Feature Selection and Compression in EHR Data
Ruochi Zhang, Qian Yang, Xiaoyang Wang +10
The rapid accumulation of Electronic Health Records (EHRs) has transformed healthcare by providing valuable data that enhance clinical predictions and diagnoses. While conventional…
Information Entropy Invariance: Enhancing Length Extrapolation in Attention Mechanisms
Kewei Li, Yanwen Kong, Yiping Xu +4
Since the emergence of research on improving the length extrapolation capabilities of large language models in 2021, some studies have made modifications to the scaling factor in t…
TemporalPaD: a reinforcement-learning framework for temporal feature representation and dimension reduction
Xuechen Mu, Zhenyu Huang, Kewei Li +5
Recent advancements in feature representation and dimension reduction have highlighted their crucial role in enhancing the efficacy of predictive modeling. This work introduces Tem…
AMPCliff: quantitative definition and benchmarking of activity cliffs in antimicrobial peptides
Kewei Li, Yuqian Wu, Yinheng Li +7
Since the mechanism of action of drug molecules in the human body is difficult to reproduce in the in vitro environment, it becomes difficult to reveal the causes of the activity c…
PepHarmony: A Multi-View Contrastive Learning Framework for Integrated Sequence and Structure-Based Peptide Encoding
Ruochi Zhang, Haoran Wu, Chang Liu +8
Recent advances in protein language models have catalyzed significant progress in peptide sequence representation. Despite extensive exploration in this field, pre-trained models t…
PepLand: a large-scale pre-trained peptide representation model for a comprehensive landscape of both canonical and non-canonical amino acids
Ruochi Zhang, Haoran Wu, Yuting Xiu +6
In recent years, the scientific community has become increasingly interested on peptides with non-canonical amino acids due to their superior stability and resistance to proteolyti…