Browsing by Author "Liu, Lizhi"

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    Correct Identification of the Core-Shell Structure of Cell Membrane-Coated Polymeric Nanoparticles
    (WILEY-VCH VERLAG, 2022-12-06) Liu, Lizhi; Yu, Wei; Seitsonen, Jani; Xu, Wujun; Lehto, Vesa Pekka; OtaNano; Department of Applied Physics; University of Eastern Finland; Ganjiang Chinese Medicine Innovation Center
    Transmission electron microscopy (TEM) observations of negatively stained cell membrane (CM)-coated polymeric nanoparticles (NPs) reveal a characteristic core-shell structure. However, negative staining agents can create artifacts that complicate the determination of the actual NP structure. Herein, it is demonstrated with various bare polymeric core NPs, such as poly(lactic-co-glycolic acid) (PLGA), poly(ethylene glycol) methyl ether-block-PLGA, and poly(caprolactone), that certain observed core-shell structures are actually artifacts caused by the staining process. To address this issue, fluorescence quenching was applied to quantify the proportion of fully coated NPs and statistical TEM analysis was used to identify and differentiate whether the observed core-shell structures of CM-coated PLGA (CM−PLGA) NPs are due to artifacts or to the CM coating. Integrated shells in TEM images of negatively stained CM−PLGA NPs are identified as artifacts. The present results challenge current understanding of the structure of CM-coated polymeric NPs and encourage researchers to use the proposed characterization approach to avoid misinterpretations.
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    HPOAnnotator: improving large-scale prediction of HPO annotations by low-rank approximation with HPO semantic similarities and multiple PPI networks
    (BioMed Central, 2019-12-23) Gao, Junning; Liu, Lizhi; Yao, Shuwei; Huang, Xiaodi; Mamitsuka, Hiroshi; Zhu, Shanfeng; Department of Computer Science; Probabilistic Machine Learning; Helsinki Institute for Information Technology (HIIT); Professorship Kaski Samuel; Fudan University; Charles Sturt University
    Background: As a standardized vocabulary of phenotypic abnormalities associated with human diseases, the Human Phenotype Ontology (HPO) has been widely used by researchers to annotate phenotypes of genes/proteins. For saving the cost and time spent on experiments, many computational approaches have been proposed. They are able to alleviate the problem to some extent, but their performances are still far from satisfactory. Method: For inferring large-scale protein-phenotype associations, we propose HPOAnnotator that incorporates multiple Protein-Protein Interaction (PPI) information and the hierarchical structure of HPO. Specifically, we use a dual graph to regularize Non-negative Matrix Factorization (NMF) in a way that the information from different sources can be seamlessly integrated. In essence, HPOAnnotator solves the sparsity problem of a protein-phenotype association matrix by using a low-rank approximation. Results: By combining the hierarchical structure of HPO and co-annotations of proteins, our model can well capture the HPO semantic similarities. Moreover, graph Laplacian regularizations are imposed in the latent space so as to utilize multiple PPI networks. The performance of HPOAnnotator has been validated under cross-validation and independent test. Experimental results have shown that HPOAnnotator outperforms the competing methods significantly. Conclusions: Through extensive comparisons with the state-of-the-art methods, we conclude that the proposed HPOAnnotator is able to achieve the superior performance as a result of using a low-rank approximation with a graph regularization. It is promising in that our approach can be considered as a starting point to study more efficient matrix factorization-based algorithms.
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    HPODNets: deep graph convolutional networks for predicting human protein–phenotype associations
    (OXFORD UNIV PRESS INC, 2022-02-01) Liu, Lizhi; Mamitsuka, Hiroshi; Zhu, Shanfeng; Department of Computer Science; Probabilistic Machine Learning; Helsinki Institute for Information Technology (HIIT); Professorship Kaski Samuel; Fudan University
    Motivation: Deciphering the relationship between human genes/proteins and abnormal phenotypes is of great importance in the prevention, diagnosis and treatment against diseases. The Human Phenotype Ontology (HPO) is a standardized vocabulary that describes the phenotype abnormalities encountered in human disorders. However, the current HPO annotations are still incomplete. Thus, it is necessary to computationally predict human protein-phenotype associations. In terms of current, cutting-edge computational methods for annotating proteins (such as functional annotation), three important features are (i) multiple network input, (ii) semi-supervised learning and (iii) deep graph convolutional network (GCN), whereas there are no methods with all these features for predicting HPO annotations of human protein. Results: We develop HPODNets with all above three features for predicting human protein-phenotype associations. HPODNets adopts a deep GCN with eight layers which allows to capture high-order topological information from multiple interaction networks. Empirical results with both cross-validation and temporal validation demonstrate that HPODNets outperforms seven competing state-of-the-art methods for protein function prediction. HPODNets with the architecture of deep GCNs is confirmed to be effective for predicting HPO annotations of human protein and, more generally, node label ranking problem with multiple biomolecular networks input in bioinformatics.
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    HPOFiller: identifying missing protein–phenotype associations by graph convolutional network
    (OXFORD UNIV PRESS INC, 2021-09-15) Liu, Lizhi; Mamitsuka, Hiroshi; Zhu, Shanfeng; Department of Computer Science; Probabilistic Machine Learning; Helsinki Institute for Information Technology (HIIT); Professorship Kaski Samuel; Fudan University
    Motivation: Exploring the relationship between human proteins and abnormal phenotypes is of great importance in the prevention, diagnosis and treatment of diseases. The human phenotype ontology (HPO) is a standardized vocabulary that describes the phenotype abnormalities encountered in human diseases. However, the current HPO annotations of proteins are not complete. Thus, it is important to identify missing protein-phenotype associations. Results: We propose HPOFiller, a graph convolutional network (GCN)-based approach, for predicting missing HPO annotations. HPOFiller has two key GCN components for capturing embeddings from complex network structures: (i) S-GCN for both protein-protein interaction network and HPO semantic similarity network to utilize network weights; (ii) Bi-GCN for the protein-phenotype bipartite graph to conduct message passing between proteins and phenotypes. The core idea of HPOFiller is to repeat run these two GCN modules consecutively over the three networks, to refine the embeddings. Empirical results of extremely stringent evaluation avoiding potential information leakage including cross-validation and temporal validation demonstrates that HPOFiller significantly outperforms all other state-of-the-art methods. In particular, the ablation study shows that batch normalization contributes the most to the performance. The further examination offers literature evidence for highly ranked predictions. Finally using known disease-HPO term associations, HPOFiller could suggest promising, unknown disease-gene associations, presenting possible genetic causes of human disorders.
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    HPOLabeler: improving prediction of human protein-phenotype associations by learning to rank
    (OXFORD UNIV PRESS INC, 2020-07-15) Liu, Lizhi; Huang, Xiaodi; Mamitsuka, Hiroshi; Zhu, Shanfeng; Department of Computer Science; Probabilistic Machine Learning; Helsinki Institute for Information Technology (HIIT); Professorship Kaski Samuel; Fudan University; Charles Sturt University
    MOTIVATION: Annotating human proteins by abnormal phenotypes has become an important topic. Human Phenotype Ontology (HPO) is a standardized vocabulary of phenotypic abnormalities encountered in human diseases. As of November 2019, only <4000 proteins have been annotated with HPO. Thus, a computational approach for accurately predicting protein-HPO associations would be important, whereas no methods have outperformed a simple Naive approach in the second Critical Assessment of Functional Annotation, 2013-2014 (CAFA2). RESULTS: We present HPOLabeler, which is able to use a wide variety of evidence, such as protein-protein interaction (PPI) networks, Gene Ontology, InterPro, trigram frequency and HPO term frequency, in the framework of learning to rank (LTR). LTR has been proved to be powerful for solving large-scale, multi-label ranking problems in bioinformatics. Given an input protein, LTR outputs the ranked list of HPO terms from a series of input scores given to the candidate HPO terms by component learning models (logistic regression, nearest neighbor and a Naive method), which are trained from given multiple evidence. We empirically evaluate HPOLabeler extensively through mainly two experiments of cross validation and temporal validation, for which HPOLabeler significantly outperformed all component models and competing methods including the current state-of-the-art method. We further found that (i) PPI is most informative for prediction among diverse data sources and (ii) low prediction performance of temporal validation might be caused by incomplete annotation of new proteins. AVAILABILITY AND IMPLEMENTATION: http://issubmission.sjtu.edu.cn/hpolabeler/. CONTACT: zhusf@fudan.edu.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.