40 citations · 131 across the 17 of their papers we have counts for
17 papers
A Multi-Modal AI Copilot for Single-Cell Analysis with Instruction Following
Yin Fang, Xinle Deng, Kangwei Liu +5
Large language models excel at interpreting complex natural language instructions, enabling them to perform a wide range of tasks. In the life sciences, single-cell RNA sequencing…
DRAK: Unlocking Molecular Insights with Domain-Specific Retrieval-Augmented Knowledge in LLMs
Jinzhe Liu, Xiangsheng Huang, Zhuo Chen +1
Large Language Models (LLMs) encounter challenges with the unique syntax of specific domains, such as biomolecules. Existing fine-tuning or modality alignment techniques struggle t…
Noise-powered Multi-modal Knowledge Graph Representation Framework
Zhuo Chen, Yin Fang, Yichi Zhang +5
The rise of Multi-modal Pre-training highlights the necessity for a unified Multi-Modal Knowledge Graph (MMKG) representation learning framework. Such a framework is essential for…
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
Zhuo Chen, Yichi Zhang, Yin Fang +12
Knowledge Graphs (KGs) play a pivotal role in advancing various AI applications, with the semantic web community's exploration into multi-modal dimensions unlocking new avenues for…
ChatCell: Facilitating Single-Cell Analysis with Natural Language
Yin Fang, Kangwei Liu, Ningyu Zhang +7
As Large Language Models (LLMs) rapidly evolve, their influence in science is becoming increasingly prominent. The emerging capabilities of LLMs in task generalization and free-for…
Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering
Yichi Zhang, Zhuo Chen, Yin Fang +4
Deploying large language models (LLMs) to real scenarios for domain-specific question answering (QA) is a key thrust for LLM applications, which poses numerous challenges, especial…
Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignment
Zhuo Chen, Lingbing Guo, Yin Fang +6
As a crucial extension of entity alignment (EA), multi-modal entity alignment (MMEA) aims to identify identical entities across disparate knowledge graphs (KGs) by exploiting assoc…
Graph Sampling-based Meta-Learning for Molecular Property Prediction
Xiang Zhuang, Qiang Zhang, Bin Wu +3
Molecular property is usually observed with a limited number of samples, and researchers have considered property prediction as a few-shot problem. One important fact that has been…
Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models
Yin Fang, Xiaozhuan Liang, Ningyu Zhang +5
Large Language Models (LLMs), with their remarkable task-handling capabilities and innovative outputs, have catalyzed significant advancements across a spectrum of fields. However,…
Domain-Agnostic Molecular Generation with Chemical Feedback
Yin Fang, Ningyu Zhang, Zhuo Chen +3
The generation of molecules with desired properties has become increasingly popular, revolutionizing the way scientists design molecular structures and providing valuable support f…
MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid
Zhuo Chen, Jiaoyan Chen, Wen Zhang +8
Multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs (KGs) whose entities are associated with relevant images. However, current…
LaKo: Knowledge-driven Visual Question Answering via Late Knowledge-to-Text Injection
Zhuo Chen, Yufeng Huang, Jiaoyan Chen +5
Visual question answering (VQA) often requires an understanding of visual concepts and language semantics, which relies on external knowledge. Most existing methods exploit pre-tra…
DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot Learning
Zhuo Chen, Yufeng Huang, Jiaoyan Chen +5
Zero-shot learning (ZSL) aims to predict unseen classes whose samples have never appeared during training. One of the most effective and widely used semantic information for zero-s…
Knowledge-informed Molecular Learning: A Survey on Paradigm Transfer
Yin Fang, Zhuo Chen, Xiaohui Fan +1
Machine learning, notably deep learning, has significantly propelled molecular investigations within the biochemical sphere. Traditionally, modeling for such research has centered…
Molecular Contrastive Learning with Chemical Element Knowledge Graph
Yin Fang, Qiang Zhang, Haihong Yang +7
Molecular representation learning contributes to multiple downstream tasks such as molecular property prediction and drug design. To properly represent molecules, graph contrastive…
Knowledge-aware Contrastive Molecular Graph Learning
Yin Fang, Haihong Yang, Xiang Zhuang +3
Leveraging domain knowledge including fingerprints and functional groups in molecular representation learning is crucial for chemical property prediction and drug discovery. When m…
Distributed Representations of Entities in Open-World Knowledge Graphs
Lingbing Guo, Zhuo Chen, Jiaoyan Chen +7
Graph neural network (GNN)-based methods have demonstrated remarkable performance in various knowledge graph (KG) tasks. However, most existing approaches rely on observing all ent…