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38 results on '"Liu, Rongjie"'

1. GP-GPT: Large Language Model for Gene-Phenotype Mapping

Author

Lyu, Yanjun, Wu, Zihao, Zhang, Lu, Zhang, Jing, Li, Yiwei, Ruan, Wei, Liu, Zhengliang, Yu, Xiaowei, Cao, Chao, Chen, Tong, Chen, Minheng, Zhuang, Yan, Li, Xiang, Liu, Rongjie, Huang, Chao, Li, Wentao, Liu, Tianming, and Zhu, Dajiang

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Pre-trained large language models(LLMs) have attracted increasing attention in biomedical domains due to their success in natural language processing. However, the complex traits and heterogeneity of multi-sources genomics data pose significant challenges when adapting these models to the bioinformatics and biomedical field. To address these challenges, we present GP-GPT, the first specialized large language model for genetic-phenotype knowledge representation and genomics relation analysis. Our model is fine-tuned in two stages on a comprehensive corpus composed of over 3,000,000 terms in genomics, proteomics, and medical genetics, derived from multiple large-scale validated datasets and scientific publications. GP-GPT demonstrates proficiency in accurately retrieving medical genetics information and performing common genomics analysis tasks, such as genomics information retrieval and relationship determination. Comparative experiments across domain-specific tasks reveal that GP-GPT outperforms state-of-the-art LLMs, including Llama2, Llama3 and GPT-4. These results highlight GP-GPT's potential to enhance genetic disease relation research and facilitate accurate and efficient analysis in the fields of genomics and medical genetics. Our investigation demonstrated the subtle changes of bio-factor entities' representations in the GP-GPT, which suggested the opportunities for the application of LLMs to advancing gene-phenotype research.

Published
2024

2. PPA: Principal Parcellation Analysis for Brain Connectomes and Multiple Traits

Author

Liu, Rongjie, Li, Meng, and Dunson, David B.

Subjects
Statistics - Applications, Statistics - Methodology
Abstract

Our understanding of the structure of the brain and its relationships with human traits is largely determined by how we represent the structural connectome. Standard practice divides the brain into regions of interest (ROIs) and represents the connectome as an adjacency matrix having cells measuring connectivity between pairs of ROIs. Statistical analyses are then heavily driven by the (largely arbitrary) choice of ROIs. In this article, we propose a novel tractography-based representation of brain connectomes, which clusters fiber endpoints to define a data adaptive parcellation targeted to explain variation among individuals and predict human traits. This representation leads to Principal Parcellation Analysis (PPA), representing individual brain connectomes by compositional vectors building on a basis system of fiber bundles that captures the connectivity at the population level. PPA reduces subjectivity and facilitates statistical analyses. We illustrate the proposed approach through applications to data from the Human Connectome Project (HCP) and show that PPA connectomes improve power in predicting human traits over state-of-the-art methods based on classical connectomes, while dramatically improving parsimony and maintaining interpretability. Our PPA package is publicly available on GitHub, and can be implemented routinely for diffusion image data.

Published
2021
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5. Efficient in-situ image and video compression through probabilistic image representation

Author

Liu, Rongjie, Li, Meng, and Ma, Li

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Statistics - Applications
Abstract

Fast and effective image compression for multi-dimensional images has become increasingly important for efficient storage and transfer of massive amounts of high-resolution images and videos. Desirable properties in compression methods include (1) high reconstruction quality at a wide range of compression rates while preserving key local details, (2) computational scalability, (3) applicability to a variety of different image/video types and of different dimensions, (4) progressive transmission, and (5) ease of tuning. We present such a method for multi-dimensional image compression called Compression via Adaptive Recursive Partitioning (CARP). CARP uses an optimal permutation of the image pixels inferred from a Bayesian probabilistic model on recursive partitions of the image to reduce its effective dimensionality, achieving a parsimonious representation that preserves information. CARP uses a multi-layer Bayesian hierarchical model to achieve in-situ compression along with self-tuning and regularization, with just one single parameter to be specified by the user to achieve the desired compression rate. Extensive numerical experiments using a variety of datasets including 2D still images, real-life YouTube videos, and surveillance videos show that CARP dominates the state-of-the-art image/video compression approaches---including JPEG, JPEG2000, BPG, MPEG4, HEVC and a neural network-based method---for all of these different image types and on nearly all of the individual images and videos over some methods., Comment: 20 pages, 11 figures

Published
2019

10. Optimal Speed Tracking Control of PMSM via Inverse Optimal Composite Controller

Author

Fan, Zhong-Xin, Li, Shihua, and Liu, Rongjie

Abstract

This brief proposes an inverse optimal composite (IOC) speed regulation controller for a permanent magnet synchronous motor (PMSM) system, simultaneously providing optimality and robustness. We regard the unknown load torque related term as a mismatched disturbance and design a disturbance observer to obtain its estimated value. Then, by constructing control Lyapunov function, inverse optimal control is obtained with consideration of disturbance estimation, avoiding solving Hamilton-Jacobi-Bellman equations. Finally, simulation results and experimental validation show the effectiveness and superiority of the proposed IOC control method in the speed tracking process of PMSMs.

Published
2024
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15. Unprecedent green macroalgae bloom: mechanism and implication to disaster prediction and prevention

Author

Cao, Mengmeng, primary, Li, Xuyan, additional, Cui, Tingwei, additional, Pan, Xinliang, additional, Li, Yan, additional, Chen, Yanlong, additional, Wang, Ning, additional, Xiao, Yanfang, additional, Song, Xingai, additional, Xu, Yuzhu, additional, A, Runa, additional, Mu, Bing, additional, Qing, Song, additional, Liu, Rongjie, additional, Zhao, Wenjing, additional, Bao, Yuhai, additional, Zhang, Jie, additional, and Wei, Lan, additional

Published
2023
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18. LMFE: Learning-Based Multiscale Feature Engineering in Partial Discharge Detection

Author

Huang, Chao, Ding, Shengxian, Li, Shihua, and Liu, Rongjie

Abstract

The partial discharge (PD) detection is of critical importance in the stability and continuity of power distribution operations. Although several feature engineering methods have been developed to refine and improve PD detection accuracy, they can be suboptimal due to several major issues: 1) failure in identifying fault-related pulses; 2) the lack of inner-phase temporal representation; and 3) multiscale feature integration. The aim of this article is to develop a learning-based multiscale feature engineering (LMFE) framework for PD detection of each signal in a three-phase power system, while addressing the above issues. The three-phase measurements are first preprocessed to identify the pulses together with the surrounded waveforms. Next, our feature engineering is conducted to extract the global-scale features, i.e., phase-level and measurement-level aggregations of the pulse-level information, and the local-scale features focusing on waveforms and their inner-phase temporal information. A recurrent neural network (RNN) model is trained, and intermediate features are extracted from this trained RNN model. Furthermore, these multiscale features are merged and fed into a classifier to distinguish the different patterns between faulty and nonfaulty signals. Finally, our LMFE is evaluated by analyzing the VSB ENET dataset, which shows that LMFE outperforms existing approaches and provides the state-of-the-art solution in PD detection.

Published
2024
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29. A predictive model of radiation-related fibrosis based on the radiomic features of magnetic resonance imaging and computed tomography

Author

Wang, Jian, primary, Liu, Rongjie, additional, Zhao, Yu, additional, Nantavithya, Chonnipa, additional, Elhalawani, Hesham, additional, Zhu, Hongtu, additional, Mohamed, Abdallah Sherif Radwan, additional, Fuller, Clifton David, additional, Kannarunimit, Danita, additional, Yang, Pei, additional, and Zhu, Hong, additional

Published
2020
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33. Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges

Author

Elhalawani, Hesham, primary, Lin, Timothy A., additional, Volpe, Stefania, additional, Mohamed, Abdallah S. R., additional, White, Aubrey L., additional, Zafereo, James, additional, Wong, Andrew J., additional, Berends, Joel E., additional, AboHashem, Shady, additional, Williams, Bowman, additional, Aymard, Jeremy M., additional, Kanwar, Aasheesh, additional, Perni, Subha, additional, Rock, Crosby D., additional, Cooksey, Luke, additional, Campbell, Shauna, additional, Yang, Pei, additional, Nguyen, Khahn, additional, Ger, Rachel B., additional, Cardenas, Carlos E., additional, Fave, Xenia J., additional, Sansone, Carlo, additional, Piantadosi, Gabriele, additional, Marrone, Stefano, additional, Liu, Rongjie, additional, Huang, Chao, additional, Yu, Kaixian, additional, Li, Tengfei, additional, Yu, Yang, additional, Zhang, Youyi, additional, Zhu, Hongtu, additional, Morris, Jeffrey S., additional, Baladandayuthapani, Veerabhadran, additional, Shumway, John W., additional, Ghosh, Alakonanda, additional, Pöhlmann, Andrei, additional, Phoulady, Hady A., additional, Goyal, Vibhas, additional, Canahuate, Guadalupe, additional, Marai, G. Elisabeta, additional, Vock, David, additional, Lai, Stephen Y., additional, Mackin, Dennis S., additional, Court, Laurence E., additional, Freymann, John, additional, Farahani, Keyvan, additional, Kaplathy-Cramer, Jayashree, additional, and Fuller, Clifton D., additional

Published
2018
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37. Red Tide Detection Method for HY−1D Coastal Zone Imager Based on U−Net Convolutional Neural Network.

Author

Zhao, Xin, Liu, Rongjie, Ma, Yi, Xiao, Yanfang, Ding, Jing, Liu, Jianqiang, and Wang, Quanbin

Subjects
*RED tide, *CONVOLUTIONAL neural networks, *COASTS, *NORMALIZED difference vegetation index, *FOG, *OCEAN color
Abstract

Existing red tide detection methods have mainly been developed for ocean color satellite data with low spatial resolution and high spectral resolution. Higher spatial resolution satellite images are required for red tides with fine scale and scattered distribution. However, red tide detection methods for ocean color satellite data cannot be directly applied to medium–high spatial resolution satellite data owing to the shortage of red tide responsive bands. Therefore, a new red tide detection method for medium–high spatial resolution satellite data is required. This study proposes the red tide detection U−Net (RDU−Net) model by considering the HY−1D Coastal Zone Imager (HY−1D CZI) as an example. RDU−Net employs the channel attention model to derive the inter−channel relationship of red tide information in order to reduce the influence of the marine environment on red tide detection. Moreover, the boundary and binary cross entropy (BBCE) loss function, which incorporates the boundary loss, is used to obtain clear and accurate red tide boundaries. In addition, a multi−feature dataset including the HY−1D CZI radiance and Normalized Difference Vegetation Index (NDVI) is employed to enhance the spectral difference between red tides and seawater and thus improve the accuracy of red tide detection. Experimental results show that RDU−Net can detect red tides accurately without a precedent threshold. Precision and Recall of 87.47% and 86.62%, respectively, are achieved, while the F1−score and Kappa are 0.87. Compared with the existing method, the F1−score is improved by 0.07–0.21. Furthermore, the proposed method can detect red tides accurately even under interference from clouds and fog, and it shows good performance in the case of red tide edges and scattered distribution areas. Moreover, it shows good applicability and can be successfully applied to other satellite data with high spatial resolution and large bandwidth, such as GF−1 Wide Field of View 2 (WFV2) images. [ABSTRACT FROM AUTHOR]

Published
2022
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38. Stability analysis of CT radiomic features with respect to segmentation variation in oropharyngeal cancer.

Author

Liu R, Elhalawani H, Radwan Mohamed AS, Elgohari B, Court L, Zhu H, and Fuller CD

Abstract

Introduction: Accurate segmentation of tumors and quantification of tumor features are important for cancer detection, diagnosis, monitoring, and planning therapeutic intervention. Due to inherent noise components in multi-parametric imaging and inter-observer and intra-observer variations, it is common that various segmentation methods may produce large segmentation errors in tumor volumes and their associated radiomic features. The purpose of this study is to carry out the stability analysis for radiomic features with respect to segmentation variation in oropharyngeal cancer (OPC)., Methods: In this study, 436 contrast-enhanced computed tomography (CT) axial images were collected from patients with OPC. In order to derive various segmentations of tumor volumes, two additional segmentations were obtained via resizing the original segmented regions of interest (ROIs) based on their geometric information on the boundary. For three ROI image groups, we calculated 109 radiomic features. Then, a logistic regression model was built to investigate the correlation between the radiomic features extracted from GTVp and the response to chemotherapy and radiation in terms of overall survival (OS). Finally, in order to evaluate the stability of each feature with respect to segmentation results, based on the prediction probabilities, we assessed the inter-rater reliability and reproducibility by calculating the intra-class correlation coefficients (ICC) and concordance correlation coefficients (CCC)., Results: Most radiomic features in this study varied a lot when the ROIs were not well segmented. For both the representation agreement and predictive agreement, the ICC and CCC were below 0.5 for all the features. We still found some robust features with relatively high ICC and CCC compared to most features. For example, 25percentile (ICC = 0.38, CCC = 0.37 in representation agreement and ICC = CCC = 0.27 in predictive agreement) is a quantile based feature, which is robust to the extremely high or low values; and Hu_1_std (ICC = 0.31, CCC = 0.31 in representation agreement) is a feature calculated based on the first Hu moment, which is invariant to the transformation of ROIs., Conclusion: In OPC studies, the tumor segmentation variation affects the radiomic features from CT images in terms of both representation and prediction. Some features that are robust to the extreme values or invariant to the transformation of ROIs may be treated as radiomic markers to assist with OPC treatment monitoring and prognostic prediction., (© 2019 Published by Elsevier B.V. on behalf of European Society for Radiotherapy and Oncology.)

Published
2019
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