2 citations · 4 across the 11 of their papers we have counts for
12 papers
DeepEvidence: Empowering Biomedical Discovery with Deep Knowledge Graph Research
Zifeng Wang, Zheng Chen, Ziwei Yang +5
Biomedical knowledge graphs (KGs) encode vast, heterogeneous information spanning literature, genes, pathways, drugs, diseases, and clinical trials, but leveraging them collectivel…
ExPath: Targeted Pathway Inference for Biological Knowledge Bases via Graph Learning and Explanation
Rikuto Kotoge, Ziwei Yang, Zheng Chen +4
Retrieving targeted pathways in biological knowledge bases, particularly when incorporating wet-lab experimental data, remains a challenging task and often requires downstream anal…
Can Large Language Models Replace Data Scientists in Biomedical Research?
Zifeng Wang, Benjamin Danek, Ziwei Yang +2
Data science plays a critical role in biomedical research, but it requires professionals with expertise in coding and medical data analysis. Large language models (LLMs) have shown…
GeSubNet: Gene Interaction Inference for Disease Subtype Network Generation
Ziwei Yang, Zheng Chen, Xin Liu +5
Retrieving gene functional networks from knowledge databases presents a challenge due to the mismatch between disease networks and subtype-specific variations. Current solutions, i…
MLOmics: Cancer Multi-Omics Database for Machine Learning
Ziwei Yang, Rikuto Kotoge, Xihao Piao +6
Framing the investigation of diverse cancers as a machine learning problem has recently shown significant potential in multi-omics analysis and cancer research. Empowering these su…
MoCLIM: Towards Accurate Cancer Subtyping via Multi-Omics Contrastive Learning with Omics-Inference Modeling
Ziwei Yang, Zheng Chen, Yasuko Matsubara +1
Precision medicine fundamentally aims to establish causality between dysregulated biochemical mechanisms and cancer subtypes. Omics-based cancer subtyping has emerged as a revoluti…
Cancer Subtyping by Improved Transcriptomic Features Using Vector Quantized Variational Autoencoder
Zheng Chen, Ziwei Yang, Lingwei Zhu +5
Defining and separating cancer subtypes is essential for facilitating personalized therapy modality and prognosis of patients. The definition of subtypes has been constantly recali…
Automated Cancer Subtyping via Vector Quantization Mutual Information Maximization
Zheng Chen, Lingwei Zhu, Ziwei Yang +1
Cancer subtyping is crucial for understanding the nature of tumors and providing suitable therapy. However, existing labelling methods are medically controversial, and have driven…
Multi-Tier Platform for Cognizing Massive Electroencephalogram
Zheng Chen, Lingwei Zhu, Ziwei Yang +1
An end-to-end platform assembling multiple tiers is built for precisely cognizing brain activities. Being fed massive electroencephalogram (EEG) data, the time-frequency spectrogra…
Adaptive Spike-Like Representation of EEG Signals for Sleep Stages Scoring
Lingwei Zhu, Koki Odani, Ziwei Yang +4
Recently there has seen promising results on automatic stage scoring by extracting spatio-temporal features from electroencephalogram (EEG). Such methods entail laborious manual fe…
Cancer Subtyping via Embedded Unsupervised Learning on Transcriptomics Data
Ziwei Yang, Lingwei Zhu, Zheng Chen +4
Cancer is one of the deadliest diseases worldwide. Accurate diagnosis and classification of cancer subtypes are indispensable for effective clinical treatment. Promising results on…
Automated Sleep Staging via Parallel Frequency-Cut Attention
Zheng Chen, Ziwei Yang, Lingwei Zhu +6
This paper proposes a novel framework for automatically capturing the time-frequency nature of electroencephalogram (EEG) signals of human sleep based on the authoritative sleep me…