Biomedical subjects

Haiyun Wang

Publications and source records attributed to Haiyun Wang.

4 recordsLinked to original sources

PubMed2026

Dissecting spatial patterning and signaling with directional diffusion in spatial multi-omics.

Spatial multi-omics sequencing enables the simultaneous profiling of transcriptomics, proteomics, and epigenomics at a spatial resolution, offering insights into complex tissue organization and molecular regulation. However, the effective integration of multiple omics modalities in a spatial context remains a major challenge. Here, we present SpaDDM, a spatial multi-omics integration framework based on directional diffusion models (DDMs), which supports spatial pattern identification, cross-omics alignment, and inter-and intracellular signaling flow analysis. SpaDDM employs DDM-based graph networks to learn omics-specific representations by jointly incorporating spatial coordinates and molecular measurements within each modality, followed by an attention mechanism to align features across modalities. We benchmarked SpaDDM on diverse spatial multi-omics datasets, including transcriptomics-epigenomics and transcriptomics-proteomics combinations across multiple tissues and species. SpaDDM consistently outperformed existing methods by more accurately deciphering spatial tissue patterns and effectively reducing the boundary noise between spatial regions. Moreover, the learned low-dimensional coembedded representations of individual cells serve as integral mediators for inferring the signaling flows that underlie spatial patterning. Finally, we demonstrated that SpaDDM alignment of complementary information across multi-omics layers facilitates cross-omics translation and significantly improves the prediction of cell state alignments.

Multiomics↗

PubMed2006

Effect of construct design on MAPKAP kinase-2 activity, thermodynamic stability and ligand-binding affinity.

MAPK-activated protein kinase-2 (MAPKAPK2) regulates the synthesis of tumor necrosis factor and other cytokines and is a potential drug target for inflammatory diseases. Five protein constructs were produced in 4-10mg quantities per liter of culture media using baculovirus-infected insect cells and characterized for kinase activity, thermal stability, and ligand-binding affinity. Compared to construct 1-370, removal of the C-terminal autoinhibitory peptide in 1-338 resulted in a destabilized but partially active nonphosphorylated enzyme; phosphorylation of 1-338 by p38alpha further increased activity 12-fold. A putative constitutively active mutant, 1-370/T222E/T334E, was 6.3-fold less active than phosphorylated 1-370. ThermoFluor, an equilibrium ligand-binding assay, was used to measure nucleotide analogue affinity for various constructs. Binding of phosphorylated nucleotides was Mg(2+)-dependent. Residues 1-40 were required for high-affinity binding of ADP, ATPgammaS, staurosporine, and K252a. A mutation M138A rendered 1-370 susceptible to p38-inhibitors SB-203580 and SB-202190 with IC50 values of 17.4 and 14.1 microM, respectively. Taken together, these studies provide information on the mechanism of ligand-binding to MAPKAPK2 that can be used in the search for selective small-molecule inhibitors.

Binding Sites↗

PubMed2005

Towards precise classification of cancers based on robust gene functional expression profiles.

BACKGROUND: Development of robust and efficient methods for analyzing and interpreting high dimension gene expression profiles continues to be a focus in computational biology. The accumulated experiment evidence supports the assumption that genes express and perform their functions in modular fashions in cells. Therefore, there is an open space for development of the timely and relevant computational algorithms that use robust functional expression profiles towards precise classification of complex human diseases at the modular level. RESULTS: Inspired by the insight that genes act as a module to carry out a highly integrated cellular function, we thus define a low dimension functional expression profile for data reduction. After annotating each individual gene to functional categories defined in a proper gene function classification system such as Gene Ontology applied in this study, we identify those functional categories enriched with differentially expressed genes. For each functional category or functional module, we compute a summary measure (s) for the raw expression values of the annotated genes to capture the overall activity level of the module. In this way, we can treat the gene expressions within a functional module as an integrative data point to replace the multiple values of individual genes. We compare the classification performance of decision trees based on functional expression profiles with the conventional gene expression profiles using four publicly available datasets, which indicates that precise classification of tumour types and improved interpretation can be achieved with the reduced functional expression profiles. CONCLUSION: This modular approach is demonstrated to be a powerful alternative approach to analyzing high dimension microarray data and is robust to high measurement noise and intrinsic biological variance inherent in microarray data. Furthermore, efficient integration with current biological knowledge has facilitated the interpretation of the underlying molecular mechanisms for complex human diseases at the modular level.

Algorithms↗

PubMed2005

[Research on pattern classification methods using gene expression data].

One of the applications of cDNA microarrays is to recognize the class and subclass of diseases such as cancers on the basis of statistical pattern classification methods using gene expression data. In this paper, we apply 2000 genes expression dataset provided by Affymatrix Company: 40 samples of intestine cancer tissue and 22 samples of normal tissue. We compare the performance of four pattern classification methods based on different feature selection methods. These pattern classification methods include: Fisher linear discriminate, Logit nonlinear discriminate, the least distance and K-nearest neighbor classifier. The results show firstly that four pattern classifiers based on the feature selection methods of t-test and classification tree all have better performance than those based on the stochastic feature selection methods, secondly that K-nearest neighbor classifier has the best performance, thirdly that both the least distance classifier and K-nearest neighbor classifier have better generalization, fourthly that four classifiers are less sensitive to the composition of samples.

Gene Expression↗