arXiv subjects

Yize Zhou

Publications and source records attributed to Yize Zhou.

6 recordsLinked to original sources

arXiv2025

BMDetect: A Multimodal Deep Learning Framework for Comprehensive Biomedical Misconduct Detection

Academic misconduct detection in biomedical research remains challenging due to algorithmic narrowness in existing methods and fragmented analytical pipelines. We present BMDetect, a multimodal deep learning framework that integrates journal metadata (SJR, institutional data), semantic embeddings (PubMedBERT), and GPT-4o-mined textual attributes (methodological statistics, data anomalies) for holistic manuscript evaluation. Key innovations include: (1) multimodal fusion of domain-specific features to reduce detection bias; (2) quantitative evaluation of feature importance, identifying journal authority metrics (e.g., SJR-index) and textual anomalies (e.g., statistical outliers) as dominant predictors; and (3) the BioMCD dataset, a large-scale benchmark with 13,160 retracted articles and 53,411 controls. BMDetect achieves 74.33% AUC, outperforming single-modality baselines by 8.6%, and demonstrates transferability across biomedical subfields. This work advances scalable, interpretable tools for safeguarding research integrity.

cs.LG↗

arXiv2024

A polarity-aware multi-relational model for the signed interaction prediction in biological networks

Predicting signed interactions in biological networks is crucial for understanding drug mechanisms and facilitating drug repurposing. While deep graph models have demonstrated success in modeling complex biological systems, existing approaches often fail to distinguish between positive and negative interactions, limiting their utility for precise pharmacological predictions. In this study, we propose a novel deep graph model, PAMR (polarity-aware multi-relational model), designed to predict both polar (e.g., activation, inhibition) and non-polar (e.g., binding, affect) chemical-gene interactions. Our model integrates graph convolutional networks with tensor decomposition to enhance feature representation and incorporates a conflict-aware sampling strategy to resolve polarity ambiguities. We introduce new evaluation metrics, polarity discrimination score (PDS) and CP@100, to assess the model's ability to differentiate interaction types. Experimental results demonstrate that PAMR outperforms baseline models, achieving superior classification accuracy and improved discrimination of polar edges. Specifically, PAMR-CL attains a Macro AUROC of 0.9072 and CP@100 of 0.974, surpassing RGCN, GraphSAGE, TransE, and BioNet baselines. A case study on nicotine further identifies two novel chemical-gene suppression links, S100A6 and SPP1, that are corroborated by independent experimental literature. Furthermore, we analyze the impact of subgraph components on predictive performance, revealing that additional network structures do not always enhance accuracy. These findings highlight the importance of polarity-aware modeling in drug discovery and network pharmacology, providing a scalable computational framework for polarity-aware chemical-gene interaction prediction and network pharmacology analysis.

cs.LG↗

arXiv2021

Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges

Federated learning plays an important role in the process of smart cities. With the development of big data and artificial intelligence, there is a problem of data privacy protection in this process. Federated learning is capable of solving this problem. This paper starts with the current developments of federated learning and its applications in various fields. We conduct a comprehensive investigation. This paper summarize the latest research on the application of federated learning in various fields of smart cities. In-depth understanding of the current development of federated learning from the Internet of Things, transportation, communications, finance, medical and other fields. Before that, we introduce the background, definition and key technologies of federated learning. Further more, we review the key technologies and the latest results. Finally, we discuss the future applications and research directions of federated learning in smart cities.

cs.LG↗

arXiv2020

FedCM: A Real-time Contribution Measurement Method for Participants in Federated Learning

Federated Learning (FL) creates an ecosystem for multiple agents to collaborate on building models with data privacy consideration. The method for contribution measurement of each agent in the FL system is critical for fair credits allocation but few are proposed. In this paper, we develop a real-time contribution measurement method FedCM that is simple but powerful. The method defines the impact of each agent, comprehensively considers the current round and the previous round to obtain the contribution rate of each agent with attention aggregation. Moreover, FedCM updates contribution every round, which enable it to perform in real-time. Real-time is not considered by the existing approaches, but it is critical for FL systems to allocate computing power, communication resources, etc. Compared to the state-of-the-art method, the experimental results show that FedCM is more sensitive to data quantity and data quality under the premise of real-time. Furthermore, we developed federated learning open-source software based on FedCM. The software has been applied to identify COVID-19 based on medical images.

cs.LG↗

arXiv2020

Experiments of Federated Learning for COVID-19 Chest X-ray Images

AI plays an important role in COVID-19 identification. Computer vision and deep learning techniques can assist in determining COVID-19 infection with Chest X-ray Images. However, for the protection and respect of the privacy of patients, the hospital's specific medical-related data did not allow leakage and sharing without permission. Collecting such training data was a major challenge. To a certain extent, this has caused a lack of sufficient data samples when performing deep learning approaches to detect COVID-19. Federated Learning is an available way to address this issue. It can effectively address the issue of data silos and get a shared model without obtaining local data. In the work, we propose the use of federated learning for COVID-19 data training and deploy experiments to verify the effectiveness. And we also compare performances of four popular models (MobileNet, ResNet18, MoblieNet, and COVID-Net) with the federated learning framework and without the framework. This work aims to inspire more researches on federated learning about COVID-19.

eess.IV↗

arXiv2020

An Improved Method for the Fitting and Prediction of the Number of COVID-19 Confirmed Cases Based on LSTM

New coronavirus disease (COVID-19) has constituted a global pandemic and has spread to most countries and regions in the world. By understanding the development trend of a regional epidemic, the epidemic can be controlled using the development policy. The common traditional mathematical differential equations and population prediction models have limitations for time series population prediction, and even have large estimation errors. To address this issue, we propose an improved method for predicting confirmed cases based on LSTM (Long-Short Term Memory) neural network. This work compared the deviation between the experimental results of the improved LSTM prediction model and the digital prediction models (such as Logistic and Hill equations) with the real data as reference. And this work uses the goodness of fitting to evaluate the fitting effect of the improvement. Experiments show that the proposed approach has a smaller prediction deviation and a better fitting effect. Compared with the previous forecasting methods, the contributions of our proposed improvement methods are mainly in the following aspects: 1) we have fully considered the spatiotemporal characteristics of the data, rather than single standardized data; 2) the improved parameter settings and evaluation indicators are more accurate for fitting and forecasting. 3) we consider the impact of the epidemic stage and conduct reasonable data processing for different stage.

physics.soc-ph↗