8 citations · 20 across the 19 of their papers we have counts for
9 papers · 1 filter
GRAM: Correcting Frozen Time-Series Foundation Models via Graph-Retrieved Amplitude Memory
Xiaoyun Yu, Xiangfei Qiu, Yonggui Huang +7
Time-series foundation models (TSFMs) enable zero-shot forecasting through large-scale cross-domain pretraining, while retrieval augmentation further improves their performance by…
ViroBench: Benchmarking Nucleotide Foundation Models on Viral Genomics Tasks
Dongxin Ye, Fang Hu, Han Hu +6
Nucleotide sequences constitute the fundamental genetic basis of biological systems, rendering viral genomic analysis critical for biomedical advancement. Despite progress in biolo…
MEMTS: Internalizing Domain Knowledge via Parameterized Memory for Retrieval-Free Domain Adaptation of Time Series Foundation Models
Xiaoyun Yu, Li fan, Xiangfei Qiu +7
While Time Series Foundation Models (TSFMs) have demonstrated exceptional performance in generalized forecasting, their performance often degrades significantly when deployed in re…
Bidirectional Representations Augmented Autoregressive Biological Sequence Generation
Xiang Zhang, Jiaqi Wei, Zijie Qiu +5
Autoregressive (AR) models, common in sequence generation, are limited in many biological tasks such as de novo peptide sequencing and protein modeling by their unidirectional natu…
Intern-S1: A Scientific Multimodal Foundation Model
Lei Bai, Zhongrui Cai, Yuhang Cao +173
In recent years, a plethora of open-source foundation models have emerged, achieving remarkable progress in some widely attended fields, with performance being quite close to that…
From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery
Jiaqi Wei, Yuejin Yang, Xiang Zhang +24
Artificial intelligence (AI) is reshaping scientific discovery, evolving from specialized computational tools into autonomous research partners. We position Agentic Science as a pi…
PRING: Rethinking Protein-Protein Interaction Prediction from Pairs to Graphs
Xinzhe Zheng, Hao Du, Fanding Xu +9
Deep learning-based computational methods have achieved promising results in predicting protein-protein interactions (PPIs). However, existing benchmarks predominantly focus on iso…
Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing
Zijie Qiu, Jiaqi Wei, Xiang Zhang +6
De novo peptide sequencing is a critical task in proteomics. However, the performance of current deep learning-based methods is limited by the inherent complexity of mass spectrome…
An Embarrassingly Simple Approach to Enhance Transformer Performance in Genomic Selection for Crop Breeding
Renqi Chen, Wenwei Han, Haohao Zhang +6
Genomic selection (GS), as a critical crop breeding strategy, plays a key role in enhancing food production and addressing the global hunger crisis. The predominant approaches in G…