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Article ; Online: Exploiting Hierarchical Interactions for Protein Surface Learning.

Lin, Yiqun / Pan, Liang / Li, Yi / Liu, Ziwei / Li, Xiaomeng

IEEE journal of biomedical and health informatics

2024 Volume PP

Abstract: Predicting interactions between proteins is one of the most important yet challenging problems in structural bioinformatics. Intrinsically, potential function sites in protein surfaces are determined by both geometric and chemical features. However, ... ...

Abstract	Predicting interactions between proteins is one of the most important yet challenging problems in structural bioinformatics. Intrinsically, potential function sites in protein surfaces are determined by both geometric and chemical features. However, existing works only consider handcrafted or individually learned chemical features from the atom type and extract geometric features independently. Here, we identify two key properties of effective protein surface learning: 1) relationship among atoms: atoms are linked with each other by covalent bonds to form biomolecules instead of appearing alone, leading to the significance of modeling the relationship among atoms in chemical feature learning. 2) hierarchical feature interaction: the neighboring residue effect validates the significance of hierarchical feature interaction among atoms and between surface points and atoms (or residues). In this paper, we present a principled framework based on deep learning techniques, namely Hierarchical Chemical and Geometric Feature Interaction Network (HCGNet), for protein surface analysis by bridging chemical and geometric features with hierarchical interactions. Extensive experiments demonstrate that our method outperforms the prior state-of-the-art method by 2.3% in site prediction task and 3.2 available at https://github.com/lyqun/HCGNet.
Language	English
Publishing date	2024-01-19
Publishing country	United States
Document type	Journal Article
ZDB-ID	2695320-1
ISSN	2168-2208 ; 2168-2194
ISSN (online)	2168-2208
ISSN	2168-2194
DOI	10.1109/JBHI.2024.3356231
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: An Integration Framework of Secure Multiparty Computation and Deep Neural Network for Improving Drug-Drug Interaction Predictions.

Pan, Liang / Xiao, Xia / Liu, Shengyun / Peng, Shaoliang

Journal of computational biology : a journal of computational molecular cell biology

2023 Volume 30, Issue 9, Page(s) 1034–1045

Abstract: Drug-drug interaction (DDI) is a key concern in drug development and pharmacovigilance. It is important to improve DDI predictions by integrating multisource data from various pharmaceutical companies. Unfortunately, the data privacy and financial ... ...

Abstract	Drug-drug interaction (DDI) is a key concern in drug development and pharmacovigilance. It is important to improve DDI predictions by integrating multisource data from various pharmaceutical companies. Unfortunately, the data privacy and financial interest issues seriously influence the interinstitutional collaborations for DDI predictions. We propose multiparty computation DDI (MPCDDI), a secure MPC-based deep learning framework for DDI predictions. MPCDDI leverages the secret sharing technologies to incorporate the drug-related feature data from multiple institutions and develops a deep learning model for DDI predictions. In MPCDDI, all data transmission and deep learning operations are integrated into secure MPC frameworks to enable high-quality collaboration among pharmaceutical institutions without divulging private drug-related information. The results suggest that MPCDDI is superior to other eight baselines and achieves the similar performance to that of the corresponding plaintext collaborations. More interestingly, MPCDDI significantly outperforms methods that use private data from the single institution. In summary, MPCDDI is an effective framework for promoting collaborative and privacy-preserving drug discovery.
MeSH term(s)	Drug Development ; Drug Discovery ; Drug Interactions ; Neural Networks, Computer ; Pharmaceutical Preparations
Chemical Substances	Pharmaceutical Preparations
Language	English
Publishing date	2023-09-14
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2030900-4
ISSN	1557-8666 ; 1066-5277
ISSN (online)	1557-8666
ISSN	1066-5277
DOI	10.1089/cmb.2023.0076
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Article: Clinical rules of Acupoint selection for cancer pain opioid-induced constipation based on journal literature data mining: A systematic review.

Xie, Yuan / Li, Yuanyuan / Liu, Di / Zou, Yi / Wang, Haiying / Pan, Liang

Heliyon

2024 Volume 10, Issue 5, Page(s) e26170

Abstract: Objective: To analyse and summarise the regularity of acupoint selection in the treatment of opioid-induced constipation (OIC) in patients with cancer pain using a data mining technique and provide a reference for clinical practice and more valuable ... ...

Abstract	Objective: To analyse and summarise the regularity of acupoint selection in the treatment of opioid-induced constipation (OIC) in patients with cancer pain using a data mining technique and provide a reference for clinical practice and more valuable treatment options. Methods: The acupoint prescription database for the treatment of OIC-related cancer pain was established by searching the relevant literature on randomised controlled trials involving acupoint therapy for OIC-related cancer pain in seven major databases, including the China National Knowledge Infrastructure, Wanfang and VIP Chinese scientific journal databases, from database establishment to December 31, 2022. The main therapeutic measures of acupoint prescription, frequency of acupoint use and its subordinate meridians and subordinate sites were then analysed. Through systematic clustering and association rule analysis, the core acupoint prescriptions and most commonly used acupoint compatibility of acupoint therapy for OIC-related cancer pain were obtained. Results: A total of 649 articles were retrieved, with 72 articles included after screening. The treatment measures were found to be mainly acupoint applications involving 28 acupoints, with a total frequency of 234. The three most frequently used acupoints were Shenque, Tianshu and Zusanli. The number of points used in the Foot-Yangming stomach meridian was the highest. Commonly used acupoints were mainly distributed in the abdomen. The compatibility of two commonly used acupoints was obtained through systematic clustering. Through association rule analysis, it was found that in the compatibility of acupoints, the strongest correlation was between Tianshu and Zusanli, and their frequency of application was the highest. Conclusion: Tianshu and Zusanli are the core acupoints for acupoint therapy in the treatment of OIC-related cancer pain, and the Shangjuxu-Zhigou-Zusanli, Qihai-Guanyuan and Zhongwan-Tianshu acupoints exhibit the highest compatibility. This study provides a reference for the clinical acupoint selection programme of acupuncture and moxibustion in the treatment of OIC-related cancer pain.
Language	English
Publishing date	2024-02-14
Publishing country	England
Document type	Journal Article
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2024.e26170
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: MotionDiffuse: Text-Driven Human Motion Generation With Diffusion Model.

Zhang, Mingyuan / Cai, Zhongang / Pan, Liang / Hong, Fangzhou / Guo, Xinying / Yang, Lei / Liu, Ziwei

IEEE transactions on pattern analysis and machine intelligence

2024 Volume 46, Issue 6, Page(s) 4115–4128

Abstract: Human motion modeling is important for many modern graphics applications, which typically require professional skills. In order to remove the skill barriers for laymen, recent motion generation methods can directly generate human motions conditioned on ... ...

Abstract	Human motion modeling is important for many modern graphics applications, which typically require professional skills. In order to remove the skill barriers for laymen, recent motion generation methods can directly generate human motions conditioned on natural languages. However, it remains challenging to achieve diverse and fine-grained motion generation with various text inputs. To address this problem, we propose MotionDiffuse, one of the first diffusion model-based text-driven motion generation frameworks, which demonstrates several desired properties over existing methods. 1) Probabilistic Mapping. Instead of a deterministic language-motion mapping, MotionDiffuse generates motions through a series of denoising steps in which variations are injected. 2) Realistic Synthesis. MotionDiffuse excels at modeling complicated data distribution and generating vivid motion sequences. 3) Multi-Level Manipulation. MotionDiffuse responds to fine-grained instructions on body parts, and arbitrary-length motion synthesis with time-varied text prompts. Our experiments show MotionDiffuse outperforms existing SoTA methods by convincing margins on text-driven motion generation and action-conditioned motion generation. A qualitative analysis further demonstrates MotionDiffuse's controllability for comprehensive motion generation.
MeSH term(s)	Humans ; Movement/physiology ; Algorithms ; Image Processing, Computer-Assisted/methods ; Computer Graphics ; Motion
Language	English
Publishing date	2024-05-07
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1939-3539
ISSN (online)	1939-3539
DOI	10.1109/TPAMI.2024.3355414
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Thrombin-Induced Microglia Activation Modulated through Aryl Hydrocarbon Receptors.

Sheu, Meei-Ling / Pan, Liang-Yi / Yang, Cheng-Ning / Sheehan, Jason / Pan, Liang-Yu / You, Weir-Chiang / Wang, Chien-Chia / Pan, Hung-Chuan

International journal of molecular sciences

2023 Volume 24, Issue 14

Abstract: Thrombin is a multifunctional serine protein which is closely related to neurodegenerative disorders. The Aryl hydrocarbon receptor (AhR) is well expressed in microglia cells involving inflammatory disorders of the brain. However, it remains unclear as ... ...

Abstract	Thrombin is a multifunctional serine protein which is closely related to neurodegenerative disorders. The Aryl hydrocarbon receptor (AhR) is well expressed in microglia cells involving inflammatory disorders of the brain. However, it remains unclear as to how modulation of AhR expression by thrombin is related to the development of neurodegeneration disorders. In this study, we investigated the role of AhR in the development of thrombin-induced neurodegenerative processes, especially those concerning microglia. The primary culture of either wild type or AhR deleted microglia, as well as BV-2 cell lines, was used for an in vitro study. Hippocampal slice culture and animals with either wild type or with AhR deleted were used for the ex vivo and in vivo studies. Simulations of ligand protein docking showed a strong integration between the thrombin and AhR. In thrombin-triggered microglia cells, deleting AhR escalated both the NO release and iNOS expression. Such effects were abolished by the administration of the AhR agonist. In thrombin-activated microglia cells, downregulating AhR increased the following: vascular permeability, pro-inflammatory genetic expression, MMP-9 activity, and the ratio of M1/M2 phenotype. In the in vivo study, thrombin induced the activation of microglia and their volume, thereby contributing to the deterioration of neurobehavior. Deleting AhR furthermore aggravated the response in terms of impaired neurobehavior, increasing brain edema, aggregating microglia, and increasing neuronal death. In conclusion, thrombin caused the activation of microglia through increased vessel permeability, expression of inflammatory response, and phenotype of M1 microglia, as well the MMP activity. Deleting AhR augmented the above detrimental effects. These findings indicate that the modulation of AhR is essential for the regulation of thrombin-induced brain damages and that the AhR agonist may harbor the potentially therapeutic effect in thrombin-induced neurodegenerative disorder.
MeSH term(s)	Animals ; Mice ; Cell Line ; Macrophages/metabolism ; Microglia/metabolism ; Receptors, Aryl Hydrocarbon/metabolism ; Thrombin/pharmacology
Chemical Substances	Receptors, Aryl Hydrocarbon ; Thrombin (EC 3.4.21.5)
Language	English
Publishing date	2023-07-13
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2019364-6
ISSN	1422-0067 ; 1422-0067 ; 1661-6596
ISSN (online)	1422-0067
ISSN	1422-0067 ; 1661-6596
DOI	10.3390/ijms241411416
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Article ; Online: PM

Yu, Zhongqi / Ma, Jinghui / Qu, Yuanhao / Pan, Liang / Wan, Shiquan

The Science of the total environment

2023 Volume 880, Page(s) 163358

Abstract: We developed an extended-range fine particulate matter ( ... ...

Abstract	We developed an extended-range fine particulate matter (PM
Language	English
Publishing date	2023-04-06
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	121506-1
ISSN	1879-1026 ; 0048-9697
ISSN (online)	1879-1026
ISSN	0048-9697
DOI	10.1016/j.scitotenv.2023.163358
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Jg. 1995 - 2021: Lesesall (1.OG)
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Article ; Online: Development and external validation of a radiomics model for assessment of HER2 positivity in men and women presenting with gastric cancer

Huiping Zhao / Pan Liang / Liuliang Yong / Ming Cheng / Yan Zhang / Minggang Huang / Jianbo Gao

Insights into Imaging, Vol 14, Iss 1, Pp 1-

2023 Volume 13

Abstract: Key points Radiomics model holds promise in identifying HER2 positivity in GCs. The conventional CT-based HER2-specific radiomics model might generalize to DECT. This study provides an imaging surrogate for stratifying GC by HER2 expression. ...

Abstract	Key points Radiomics model holds promise in identifying HER2 positivity in GCs. The conventional CT-based HER2-specific radiomics model might generalize to DECT. This study provides an imaging surrogate for stratifying GC by HER2 expression.
Keywords	Computed tomography ; Radiomics ; Nomogram ; HER2 testing ; Gastric cancer ; Medical physics. Medical radiology. Nuclear medicine ; R895-920
Language	English
Publishing date	2023-02-01T00:00:00Z
Publisher	SpringerOpen
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Exploiting Hierarchical Interactions for Protein Surface Learning

Lin, Yiqun / Pan, Liang / Li, Yi / Liu, Ziwei / Li, Xiaomeng

2024

Abstract	Predicting interactions between proteins is one of the most important yet challenging problems in structural bioinformatics. Intrinsically, potential function sites in protein surfaces are determined by both geometric and chemical features. However, existing works only consider handcrafted or individually learned chemical features from the atom type and extract geometric features independently. Here, we identify two key properties of effective protein surface learning: 1) relationship among atoms: atoms are linked with each other by covalent bonds to form biomolecules instead of appearing alone, leading to the significance of modeling the relationship among atoms in chemical feature learning. 2) hierarchical feature interaction: the neighboring residue effect validates the significance of hierarchical feature interaction among atoms and between surface points and atoms (or residues). In this paper, we present a principled framework based on deep learning techniques, namely Hierarchical Chemical and Geometric Feature Interaction Network (HCGNet), for protein surface analysis by bridging chemical and geometric features with hierarchical interactions. Extensive experiments demonstrate that our method outperforms the prior state-of-the-art method by 2.3% in site prediction task and 3.2% in interaction matching task, respectively. Our code is available at https://github.com/xmed-lab/HCGNet. Comment: Accepted to J-BHI
Keywords	Quantitative Biology - Biomolecules ; Computer Science - Machine Learning
Subject code	004
Publishing date	2024-01-17
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Implicit high-order gas-kinetic schemes for compressible flows on three-dimensional unstructured meshes

Yang, Yaqing / Pan, Liang / Xu, Kun

2023

Abstract: In the previous studies, the high-order gas-kinetic schemes (HGKS) have achieved successes for unsteady flows on three-dimensional unstructured meshes. In this paper, to accelerate the rate of convergence for steady flows, the implicit non-compact and ... ...

Abstract	In the previous studies, the high-order gas-kinetic schemes (HGKS) have achieved successes for unsteady flows on three-dimensional unstructured meshes. In this paper, to accelerate the rate of convergence for steady flows, the implicit non-compact and compact HGKSs are developed. For non-compact scheme, the simple weighted essentially non-oscillatory (WENO) reconstruction is used to achieve the spatial accuracy, where the stencils for reconstruction contain two levels of neighboring cells. Incorporate with the nonlinear generalized minimal residual (GMRES) method, the implicit non-compact HGKS is developed. In order to improve the resolution and parallelism of non-compact HGKS, the implicit compact HGKS is developed with Hermite WENO (HWENO) reconstruction, in which the reconstruction stencils only contain one level of neighboring cells. The cell averaged conservative variable is also updated with GMRES method. Simultaneously, a simple strategy is used to update the cell averaged gradient by the time evolution of spatial-temporal coupled gas distribution function. To accelerate the computation, the implicit non-compact and compact HGKSs are implemented with the graphics processing unit (GPU) using compute unified device architecture (CUDA). A variety of numerical examples, from the subsonic to supersonic flows, are presented to validate the accuracy, robustness and efficiency of both inviscid and viscous flows. Comment: arXiv admin note: text overlap with arXiv:2203.09047
Keywords	Mathematics - Numerical Analysis
Subject code	621 ; 518
Publishing date	2023-04-19
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: PM2.5 extended-range forecast based on MJO and S2S using LightGBM

Yu, Zhongqi / Ma, Jinghui / Qu, Yuanhao / Pan, Liang / Wan, Shiquan

Science of the Total Environment. 2023 July, v. 880 p.163358-

2023

Abstract: We developed an extended-range fine particulate matter (PM₂.₅) prediction model in Shanghai using the light gradient-boosting machine (LightGBM) algorithm based on PM₂.₅ historical data, meteorological observational data, Subseasonal-to-Seasonal ... ...

Abstract	We developed an extended-range fine particulate matter (PM₂.₅) prediction model in Shanghai using the light gradient-boosting machine (LightGBM) algorithm based on PM₂.₅ historical data, meteorological observational data, Subseasonal-to-Seasonal Prediction Project (S2S) forecasts and Madden-Julian Oscillation (MJO) monitoring data. The analysis and prediction results demonstrated that the MJO improved the predictive skill of the extended-range PM₂.₅ forecast. The MJO indexes, namely, real-time multivariate MJO series 1 (RMM1) and real-time multivariate MJO series 2 (RMM2), ranked the first, and seventh, respectively, in terms of the predictive contribution of all meteorological predictors. When the MJO was not introduced, the correlation coefficients for the forecasts on lead times of 11–40 days ranged from 0.27 to 0.55, and the root mean square errors (RMSEs) ranged from 23.4 to 31.8 μg/m³. After the MJO was introduced, the correlation coefficients for the 11–40 day forecast ranged from 0.31 to 0.56, among which those for the 16–40 day forecast substantially improved, and the RMSEs ranged from 23.2 to 28.7 μg/m³. When comparing the prediction scores, such as percent correct (PC), critical success index (CSI), and equitable threat score (ETS), the forecast model was more accurate when it introduced the MJO. A novel aspect of this study is to investigate the effects of the MJO mechanism on the meteorological conditions of air pollution in eastern China through advanced regression analysis. The MJO indexes RMM1 and RMM2 considerably impacted the geopotential height field of 28°–40° at 300–250 hPa 45 days in advance. When RMM1 increased and RMM2 decreased 45 days in advance, the 500 hPa geopotential height field weakened accordingly, and the bottom of the 500 hPa trough moved southward; thus cold air was more easily transported southward and the upstream air pollutants were transported to eastern China. With a weak ground pressure field and dry air at low altitudes, the westerly wind component increased, which led to the easier formation of a weather configuration favorable for the accumulation and transport of air pollution, thus resulting in an increase in PM₂.₅ concentration in the region. These findings can guide forecasters regarding the utility of MJO and S2S for subseasonal air pollution outlooks.
Keywords	Madden-Julian Oscillation ; air ; air pollution ; algorithms ; cold ; environment ; models ; observational studies ; particulates ; prediction ; regression analysis ; wind direction ; China ; LightGBM ; S2S ; PM2.5 ; Extended-range forecast ; MJO
Language	English
Dates of publication	2023-07
Publishing place	Elsevier B.V.
Document type	Article ; Online
ZDB-ID	121506-1
ISSN	1879-1026 ; 0048-9697
ISSN (online)	1879-1026
ISSN	0048-9697
DOI	10.1016/j.scitotenv.2023.163358
Database	NAL-Catalogue (AGRICOLA)

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