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Ensemble learning based on bi-directional gated recurrent unit and convolutional neural network with word embedding module for bioactive peptide prediction

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FOOD CHEMISTRY 2025年 468卷 142464页

作者： Lai, Zhenghui Hu, Wenxing Wu, Yan Zhu, Jihong Xie, Xiaojun Guan, Lixin Li, Mengshan Gannan Normal Univ Coll Phys & Elect Informat Ganzhou 341000 Jiangxi Peoples R China

Bioactive peptides, as small protein fragments, are essential mediators of diverse physiological activities, such as antimicrobial, anti-inflammatory, anticancer, antioxidant, and immunomodulatory functions. Despite their substantial potential in pharmaceuticals and the food industry, conventional methods for peptide classification and activity prediction are limited by high costs, time-intensive procedures, and extensive data processing requirements. Here, we present BioPepPred-DLEmb, a novel computational model integrating Convolutional Neural Networks (CNNs) and Bidirectional Gated Recurrent Units (BiGRUs), augmented with natural language processing to encode amino acids into information-dense vectors. Evaluated across nine bioactive peptide datasets, BioPepPred-DLEmb demonstrates superior predictive accuracy (0.909) and sensitivity (0.911) compared to traditional methods. Through UMAP visualization and Kplogo analysis, the model effectively differentiates peptide activity states and identifies key biomarkers. The predicted antimicrobial peptides (PredAMPs) exhibit potent efficacy in vitro, achieving low micromolar inhibitory concentrations (2-16 mu mol/L) against pathogens such as Escherichia coli and Acinetobacter baumannii. These findings establish a robust foundation for bioactive peptide development, with implications for advancements in precision medicine, personalized therapies, and functional food innovations.

关键词： Bioactive peptide CNN BiGRU Word embedding bioinformatics visualization

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Comparative Analysis of Multidimensional, Quantitative Data

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2010年第6期16卷 1027-1035页

作者： Lex, Alexander Streit, Marc Partl, Christian Kashofer, Karl Schmalstieg, Dieter Graz Univ Technol Inst Comp Graph & Vis A-8010 Graz Austria Med Univ Graz Graz Austria

When analyzing multidimensional, quantitative data, the comparison of two or more groups of dimensions is a common task. Typical sources of such data are experiments in biology, physics or engineering, which are conducted in different configurations and use replicates to ensure statistically significant results. One common way to analyze this data is to filter it using statistical methods and then run clustering algorithms to group similar values. The clustering results can be visualized using heat maps, which show differences between groups as changes in color. However, in cases where groups of dimensions have an a priori meaning, it is not desirable to cluster all dimensions combined, since a clustering algorithm can fragment continuous blocks of records. Furthermore, identifying relevant elements in heat maps becomes more difficult as the number of dimensions increases. To aid in such situations, we have developed Matchmaker, a visualization technique that allows researchers to arbitrarily arrange and compare multiple groups of dimensions at the same time. We create separate groups of dimensions which can be clustered individually, and place them in an arrangement of heat maps reminiscent of parallel coordinates. To identify relations, we render bundled curves and ribbons between related records in different groups. We then allow interactive drill-downs using enlarged detail views of the data, which enable in-depth comparisons of clusters between groups. To reduce visual clutter, we minimize crossings between the views. This paper concludes with two case studies. The first demonstrates the value of our technique for the comparison of clustering algorithms. In the second, biologists use our system to investigate why certain strains of mice develop liver disease while others remain healthy, informally showing the efficacy of our system when analyzing multidimensional data containing distinct groups of dimensions.

关键词： Multidimensional data cluster comparison bioinformatics visualization

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Generalized box-plot for root growth ensembles

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BMC bioinformatics 2017年第SUPPL 2期18卷 1-15页

作者： Vad, Viktor Cedrim, Douglas Busch, Wolfgang Filzmoser, Peter Viola, Ivan TU WIEN Karlspl 13 A-1040 Vienna Austria Univ Sao Paulo ICMC BR-15260 Sao Carlos SP Brazil Gregor Mendel Inst Mol Plant Biol GmbH Dr Bohr Gasse 3 A-1030 Vienna Austria

Background: In the field of root biology there has been a remarkable progress in root phenotyping, which is the efficient acquisition and quantitative description of root morphology. What is currently missing are means to efficiently explore, exchange and present the massive amount of acquired, and often time dependent root phenotypes. Results: In this work, we present visual summaries of root ensembles by aggregating root images with identical genetic characteristics. We use the generalized box plot concept with a new formulation of data depth. In addition to spatial distributions, we created a visual representation to encode temporal distributions associated with the development of root individuals. Conclusions: The new formulation of data depth allows for much faster implementation close to interactive frame rates. This allows us to present the statistics from bootstrapping that characterize the root sample set quality. As a positive side effect of the new data-depth formulation we are able to define the geometric median for the curve ensemble, which was well received by the domain experts.

关键词： Uncertainty visualization bioinformatics visualization Curve ensembles

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Dynamic visualization of coexpression in systems genetics data

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2008年第5期14卷 1081-1094页

作者： New, Joshua Kendall, Wesley Huang, Jian Chesler, Elissa Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA Oak Ridge Natl Lab Mammalian Genet & Genom Biosci Div Oak Ridge TN 37831 USA

Biologists hope to address grand scientific challenges by exploring the abundance of data made available through microarray analysis and other high-throughput techniques. However, the impact of this large volume of data is limited unless researchers can effectively assimilate the entirety of this complex information and integrate it into their daily research;interactive visualization tools are called for to support the effort. Specifically, typical studies of gene coexpression can make use of novel visualization tools that enable the dynamic formulation and fine-tuning of hypotheses to aid the process of evaluating sensitivity of key parameters and achieving data reduction. These tools should allow biologists to develop an intuitive understanding of the structure of biological networks and discover genes that reside in critical positions in networks and pathways. By using a graph as a universal data representation of correlation in gene expression data, our novel visualization tool employs several techniques that when used in an integrated manner provide innovative analytical capabilities. Our tool for interacting with gene coexpression data integrates techniques such as graph layout, qualitative subgraph extraction through a novel 2D user interface, quantitative subgraph extraction using graph-theoretic algorithms or by querying an optimized B-tree, dynamic level-of-detail graph abstraction, and template-based fuzzy classification using neural networks. We demonstrate our system using a real-world workflow from a large-scale systems genetics study of mammalian gene coexpression.

关键词： visualization in physical sciences life sciences and engineering graph and network visualization bioinformatics visualization focus plus context techniques

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GeneTerrain: Visual exploration of differential gene expression profiles organized in native biomolecular interaction networks

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INFORMATION visualization 2010年第1期9卷 1-12页

作者： You, Qian Fang, Shiaofen Chen, Jake Yue Purdue Univ Sch Sci Dept Comp & Informat Sci Indianapolis IN 46202 USA Purdue Univ Sch Informat Indianapolis IN 46202 USA Indiana Univ Sch Med Ctr Computat Biol & Bioinformat Indianapolis IN USA Indiana Univ Purdue Univ Ctr Biocomp Indianapolis IN 46202 USA

We propose a new network visualization technique using scattered data interpolation and surface rendering, based upon a foundation layout of a scalar field. Contours of the interpolated surfaces are generated to support multi-scale visual interaction for data exploration. Our framework visualizes quantitative attributes of nodes in a network as a continuous surface by interpolating the scalar field, therefore avoiding scalability issues typical in conventional network visualizations while also maintaining the topological properties of the original network. We applied this technique to the study of a bio-molecular interaction network integrated with gene expression data for Alzheimer's Disease (AD). In this application, differential gene expression profiles obtained from the human brain are rendered for AD patients with differing degrees of severity and compared to healthy individuals. We show that this alternative visualization technique is effective in revealing several types of molecular biomarkers, which are traditionally difficult to detect due to 'noises' in data derived from DNA microarray experiments. Information visualization (2010) 9, 1-12. doi: 10.1057/ivs.2008.3

关键词： graph and network visualization bioinformatics visualization visual analytics

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VisExpress: Visual exploration of differential gene expression data

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INFORMATION visualization 2017年第1期16卷 48-73页

作者： Simon, Svenja Mittelstaedt, Sebastian Kwon, Bum Chul Stoffel, Andreas Landstorfer, Richard Neuhaus, Klaus Muehlig, Anna Scherer, Siegfried Keim, Daniel A. Univ Konstanz Dept Comp & Informat Sci Box D78 D-78457 Constance Germany Tech Univ Munich Lehrstuhl Mikrobielle Okol Freising Weihenstephan Germany

Biologists are keen to understand how processes in cells react to environmental changes. Differential gene expression analysis allows biologists to explore functions of genes with data generated from different environments. However, these data and analysis lead to unique challenges since tasks are ill-defined, require implicit domain knowledge, comprise large volumes of data, and are, therefore, of explanatory nature. To investigate a scalable visualization-based solution, we conducted a design study with three biologists specialized in differential gene expression analysis. We stress our contributions in three aspects: first, we characterize the problem domain for exploring differential gene expression data and derive task abstractions and design requirements. Second, we investigate the design space and present an interactive visualization system, called VisExpress. Third, we evaluate the usefulness of VisExpress via a Pair Analytics study with real users and real data and report on insights that were gained by our experts with VisExpress.

关键词： Information visualization design study bioinformatics visualization interactive visual exploration biological data expression data gene expression profiling differential gene expression

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Pan-Tetris: an interactive visualisation for Pan-genomes

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BMC bioinformatics 2015年第11期16卷 1-11页

作者： Hennig, Andre Bernhardt, Joerg Nieselt, Kay Univ Tubingen Ctr Bioinformat Tubingen D-72076 Tubingen Germany Ernst Moritz Arndt Univ Greifswald Inst Microbiol D-17487 Greifswald Germany

Background: Large-scale genome projects have paved the way to microbial pan-genome analyses. Pan-genomes describe the union of all genes shared by all members of the species or taxon under investigation. They offer a framework to assess the genomic diversity of a given collection of individual genomes and moreover they help to consolidate gene predictions and annotations. The computation of pan-genomes is often a challenge, and many techniques that use a global alignment-independent approach run the risk of not separating paralogs from orthologs. Also alignment-based approaches which take the gene neighbourhood into account often need additional manual curation of the results. This is quite time consuming and so far there is no visualisation tool available that offers an interactive GUI for the pan-genome to support curating pan-genomic computations or annotations of orthologous genes. Results: We introduce Pan-Tetris, a Java based interactive software tool that provides a clearly structured and suitable way for the visual inspection of gene occurrences in a pan-genome table. The main features of Pan-Tetris are a standard coordinate based presentation of multiple genomes complemented by easy to use tools compensating for algorithmic weaknesses in the pan-genome generation workflow. We demonstrate an application of Pan-Tetris to the pan-genome of Staphylococcus aureus. Conclusions: Pan-Tetris is currently the only interactive pan-genome visualisation tool. Pan-Tetris is available from http://***/1vVxYZT

关键词： Gene Sequence analysis Genomics bioinformatics visualization Software visualization Data Aggregation

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Interactive exploration of ligand transportation through protein tunnels

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BMC bioinformatics 2017年第SUPPL 2期18卷 1-16页

作者： Furmanova, Katarina Jaresova, Miroslava Byska, Jan Jurcik, Adam Parulek, Julius Hauser, Helwig Kozlikova, Barbora Masaryk Univ Brno Czech Republic Univ Bergen Bergen Norway

Background: Protein structures and their interaction with ligands have been in the focus of biochemistry and structural biology research for decades. The transportation of ligand into the protein active site is often complex process, driven by geometric and physico-chemical properties, which renders the ligand path full of jitter and impasses. This prevents understanding of the ligand transportation and reasoning behind its behavior along the path. Results: To address the needs of the domain experts we design an explorative visualization solution based on a multi-scale simplification model. It helps to navigate the user to the most interesting parts of the ligand trajectory by exploring different attributes of the ligand and its movement, such as its distance to the active site, changes of amino acids lining the ligand, or ligand "stuckness". The process is supported by three linked views - 3D representation of the simplified trajectory, scatterplot matrix, and bar charts with line representation of ligand-lining amino acids. Conclusions: The usage of our tool is demonstrated on molecular dynamics simulations provided by the domain experts. The tool was tested by the domain experts from protein engineering and the results confirm that it helps to navigate the user to the most interesting parts of the ligand trajectory and to understand the ligand behavior.

关键词： Molecular visualization bioinformatics visualization Computational proteomics

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Sequence Surveyor: Leveraging Overview for Scalable Genomic Alignment visualization

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2011年第12期17卷 2392-2401页

作者： Albers, Danielle Dewey, Colin Gleicher, Michael Univ Wisconsin Madison WI 53706 USA

In this paper, we introduce overview visualization tools for large-scale multiple genome alignment data. Genome alignment visualization and, more generally, sequence alignment visualization are an important tool for understanding genomic sequence data. As sequencing techniques improve and more data become available, greater demand is being placed on visualization tools to scale to the size of these new datasets. When viewing such large data, we necessarily cannot convey details, rather we specifically design overview tools to help elucidate large-scale patterns. Perceptual science, signal processing theory, and generality provide a framework for the design of such visualizations that can scale well beyond current approaches. We present Sequence Surveyor, a prototype that embodies these ideas for scalable multiple whole-genome alignment overview visualization. Sequence Surveyor visualizes sequences in parallel, displaying data using variable color, position, and aggregation encodings. We demonstrate how perceptual science can inform the design of visualization techniques that remain visually manageable at scale and how signal processing concepts can inform aggregation schemes that highlight global trends, outliers, and overall data distributions as the problem scales. These techniques allow us to visualize alignments with over 100 whole bacterial-sized genomes.

关键词： bioinformatics visualization Perception Theory Scalability Issues Visual Design

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Taxonomy-Based Glyph Design-with a Case Study on Visualizing Workflows of Biological Experiments

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2012年第12期18卷 2603-2612页

作者： Maguire, Eamonn Rocca-Serra, Philippe Sansone, Susanna-Assunta Davies, Jim Chen, Min Univ Oxford Oxford E Res Ctr Oxford OX1 2JD England Univ Oxford Dept Comp Sci Oxford OX1 2JD England

Glyph-based visualization can offer elegant and concise presentation of multivariate information while enhancing speed and ease in visual search experienced by users. As with icon designs, glyphs are usually created based on the designers' experience and intuition, often in a spontaneous manner. Such a process does not scale well with the requirements of applications where a large number of concepts are to be encoded using glyphs. To alleviate such limitations, we propose a new systematic process for glyph design by exploring the parallel between the hierarchy of concept categorization and the ordering of discriminative capacity of visual channels. We examine the feasibility of this approach in an application where there is a pressing need for an efficient and effective means to visualize workflows of biological experiments. By processing thousands of workflow records in a public archive of biological experiments, we demonstrate that a cost-effective glyph design can be obtained by following a process of formulating a taxonomy with the aid of computation, identifying visual channels hierarchically, and defining application-specific abstraction and metaphors.

关键词： Glyph-based techniques taxonomies design methodologies bioinformatics visualization

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