Biomedical subjects

Aidong Zhang

Publications and source records attributed to Aidong Zhang.

5 recordsLinked to original sources

PubMed2025

ConceptDrift: leveraging spatial, temporal and semantic evolution of biomedical concepts for hypothesis generation.

MOTIVATION: Hypothesis generation is a fundamental problem in biomedical text mining that aims to generate ideas that are new, interesting, and plausible by discovering unexplored links between biomedical concepts. Despite significant advances made by existing approaches, they do not fully leverage the evolutionary properties of biomedical concepts. This is limiting because scientific knowledge continually evolves over time, with new facts being added and old ones becoming obsolete. Thus, it is crucial to capture the evolutionary properties of biomedical concepts from multiple perspectives (e.g. spatial, temporal, and semantic) to generate hypotheses that reflect the up-to-date information landscape of the biomedical domain. RESULTS: We introduce a novel framework, ConceptDrift, that models the hypothesis generation task as a sequence of temporal graphlets and simultaneously encodes spatial, temporal, and semantic change. Unlike existing approaches that treat these dimensions independently, ConceptDrift is the first to provide a holistic understanding of concept evolution by integrating them into a unified framework. Grounded in the theories of the Distributional Hypothesis and Conceptual Change, our method adapts these principles to the unique challenges of large-scale biomedical literature. We conduct extensive experiments across multiple datasets and demonstrate that ConceptDrift consistently outperforms state-of-the-art baselines in generating accurate and meaningful hypotheses. Our framework shows immediate practical benefits for web-based literature mining tools in life sciences and biomedicine, offering more robust and predictive feature representations. AVAILABILITY AND IMPLEMENTATION: https://github.com/amir-hassan25/ConceptDrift (DOI: 10.6084/m9.figshare.29975476).

Semantics↗

PubMed2004

ESPD: a pattern detection model underlying gene expression profiles.

MOTIVATION: DNA arrays permit rapid, large-scale screening for patterns of gene expression and simultaneously yield the expression levels of thousands of genes for samples. The number of samples is usually limited, and such datasets are very sparse in high-dimensional gene space. Furthermore, most of the genes collected may not necessarily be of interest and uncertainty about which genes are relevant makes it difficult to construct an informative gene space. Unsupervised empirical sample pattern discovery and informative genes identification of such sparse high-dimensional datasets present interesting but challenging problems. RESULTS: A new model called empirical sample pattern detection (ESPD) is proposed to delineate pattern quality with informative genes. By integrating statistical metrics, data mining and machine learning techniques, this model dynamically measures and manipulates the relationship between samples and genes while conducting an iterative detection of informative space and the empirical pattern. The performance of the proposed method with various array datasets is illustrated.

Algorithms↗

PubMed2004

VizStruct: exploratory visualization for gene expression profiling.

MOTIVATION: DNA arrays provide a broad snapshot of the state of the cell by measuring the expression levels of thousands of genes simultaneously. Visualization techniques can enable the exploration and detection of patterns and relationships in a complex data set by presenting the data in a graphical format in which the key characteristics become more apparent. The dimensionality and size of array data sets however present significant challenges to visualization. The purpose of this study is to present an interactive approach for visualizing variations in gene expression profiles and to assess its usefulness for classifying samples. RESULTS: The first Fourier harmonic projection was used to map multi-dimensional gene expression data to two dimensions in an implementation called VizStruct. The visualization method was tested using the differentially expressed genes identified in eight separate gene expression data sets. The samples were classified using the oblique decision tree (OC1) algorithm to provide a procedure for visualization-driven classification. The classifiers were evaluated by the holdout and the cross-validation techniques. The proposed method was found to achieve high accuracy. AVAILABILITY: Detailed mathematical derivation of all mapping properties as well as figures in color can be found as supplementary on the web page http://www.cse.buffalo.edu/DBGROUP/bioinformatics/supplementary/vizstruct. All programs were written in Java and Matlab and software code is available by request from the first author.

Algorithms↗

PubMed2004

Analysis of pharmacokinetics, pharmacodynamics, and pharmacogenomics data sets using VizStruct, a novel multidimensional visualization technique.

PURPOSE: Data visualization techniques for the pharmaceutical sciences have not been extensively investigated. The purpose of this study was to evaluate the usefulness of VizStruct, a multidimensional visualization tool, for applications in pharmacokinetics, pharmacodynamics, and pharmacogenomics. METHODS: The VizStruct tool uses the first harmonic of the discrete Fourier transform to map multidimensional data to two dimensions for visualization. The mapping was used to visualize several published pharmacokinetic, pharmacodynamic, and pharmacogenomic data sets. The VizStruct approach was evaluated using simulated population pharmacokinetics data sets, the data from Dalen and colleagues (Clin. PharmacoL Ther. 63:444-452, 1998) on the kinetics of nortriptyline and its 10-hydroxynortriptyline metabolite in subjects with differing number of copies of the CYP2D6, and the gene expression profiling data of Bohen and colleagues (Proc. Natl. Acad. Sci. USA 100:1926-1930, 2003) on follicular lymphoma patients responsive and nonresponsive to rituximab. RESULTS: The VizStruct mapping preserves the key characteristics of multidimensional data in two dimensions in a manner that facilitates visualization. The mapping is computationally efficient and can be used for cluster detection and class prediction in pharmaceutical data sets. The VizStruct visualization succinctly summarized the salient similarities and differences in the nortriptyline and 10-hydroxynortriptyline pharmacokinetic profiles in subjects with increasing number of CYP2D6 gene copies. In the simulated population pharmacokinetic data sets, it was capable of discriminating the subtle differences between pharmacokinetic profiles derived from 1- and 2-compartment models with the same area under the curve. The two-dimensional VizStruct mapping computed from a subset of 102 informative genes from the Bohen and colleagues data set effectively separated the rituximab responder, rituximab nonresponder, and control subject groups. CONCLUSIONS: The VizStruct approach is a computationally efficient and effective approach for visualizing complex, multidimensional data sets. It could have many useful applications in the pharmaceutical sciences.

Algorithms↗

PubMed2002

Mixed urease/amphiphile LB films and their application for biosensor development.

The optimal conditions for the formation of a stable mixed urease/octadecylamine (ODA) film at the air-water interface are determined. This film is efficiently transferred onto the hydrophobized surface of a pH-ISFET, and the features of the urea enzymatic field-effect transistor (ENFET) are determined: detection limit 0.2 mM, response time 15 s, and dynamic range 0-20 mM.

Amines↗