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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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visualization of Biomolecular Structures: State of the Art Revisited

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COMPUTER GRAPHICS FORUM 2017年第8期36卷 178-204页

作者： Kozlikova, B. Krone, M. Falk, M. Lindow, N. Baaden, M. Baum, D. Viola, I. Parulek, J. Hege, H-C. Masaryk Univ Fac Informat Brno Czech Republic Univ Stuttgart Visualizat Res Ctr Stuttgart Germany Linkoping Univ Sci Visualizat Grp Linkoping Sweden ZIB Dept Visual Data Anal Berlin Germany CNRS Lab Biochim Theor UPR 9080 Paris France Univ Bergen Dept Informat Bergen Norway TU Wien Inst Comp Graph & Algorithms Vienna Austria

Structural properties of molecules are of primary concern in many fields. This report provides a comprehensive overview on techniques that have been developed in the fields of molecular graphics and visualization with a focus on applications in structural biology. The field heavily relies on computerized geometric and visual representations of three-dimensional, complex, large and time-varying molecular structures. The report presents a taxonomy that demonstrates which areas of molecular visualization have already been extensively investigated and where the field is currently heading. It discusses visualizations for molecular structures, strategies for efficient display regarding image quality and frame rate, covers different aspects of level of detail and reviews visualizations illustrating the dynamic aspects of molecular simulation data. The survey concludes with an outlook on promising and important research topics to foster further success in the development of tools that help to reveal molecular secrets.

关键词： molecular visualization taxonomy bioinformatics visualization

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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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Multiscale dynamic visualization of signal transduction processes with detailing of target-genes activation in three-dimensional genome structure 6th

Multiscale dynamic visualization of signal transduction proc...

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6th Annual International Young Scientists Conference on HPC and Computational Science (YSC)

作者： Bureiko, Kseniia Semashko, Maria Mukhina, Ksenia D. Karsakov, Andrey ITMO Univ St Petersburg Russia

Given the complexity of modem biological data it is essentially crucial to accord a consistent expounding. Interpreting such data into complex networks and visualizing them can reveal understanding of various processes in a cell. A consequence mapping of signal transduction processes to the spatial genome structure can benefit new insights in interaction detection in the spatial arrangement of genes. We present an approach for multiscale dynamic visualization of signal transduction processes with detailing of target-genes activation in spatial genome structure. The usage of this approach is demonstrated for the WNT signaling pathway in a human cell. We conclude with suggesting future research questions to improve our approach by considering new available data. (C) 2018 The Authors. Published by Elsevier B.V.

关键词： bioinformatics visualization signaling pathways spatial genome structure

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iDotter - an interactive dot plot viewer 25

iDotter - an interactive dot plot viewer

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25th International Conference on Central Europe on Computer Graphics, visualization and Computer Vision (WSCG)

作者： Gerighausen, Daniel Hausdorf, Alrik Zaenker, Sebastian Zeckzer, Dirk Univ Leipzig Image & Signal Proc Grp Leipzig Germany Univ Leipzig Bioinformat Leipzig Germany

ISBN: (纸本)9788086943497

Bioinformaticians judge the likelihood of the overall RNA secondary structure based on comparing its base pair probabilities. These probabilities can be calculated by various tools and are frequently displayed using dot plots for further analysis. However, most tools produce only static dot plot images which restricts possible interactions to the capabilities of the respective viewers (mostly PostScript-viewers). Moreover, this approach does not scale well with larger RNAs since most PostScript viewers are not designed to show a huge number of elements and have only legacy support for PostScript. Therefore, we developed iDotter, an interactive tool for analyzing RNA secondary structures. iDotter overcomes the previously described limitations providing multiple interaction mechanisms facilitating the interactive analysis of the displayed data. According to the biologists and bioinformaticians that regularly use out interactive dot plot viewer, iDotter is superior to all previous approaches with respect to facilitating dot plot based analysis of RNA secondary structures.

关键词： bioinformatics visualization Tabular Data User Interfaces Dot Plots

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Generalized box-plot for root growth ensembles

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BMC bioinformatics 2017年第Sup2期18卷 65-65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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Interactive exploration of ligand transportation through protein tunnels

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BMC bioinformatics 2017年第Sup2期18卷 22-22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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Unboxing cluster heatmaps

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BMC bioinformatics 2017年第2-Sup期18卷 63-63页

作者： Engle, Sophie Whalen, Sean Joshi, Alark Pollard, Katherine S. Univ San Francisco San Francisco CA 94117 USA Gladstone Inst San Francisco CA 94158 USA Univ Calif San Francisco Inst Human Genet Div Biostat San Francisco CA 94158 USA Univ Calif San Francisco Inst Computat Hlth Sci San Francisco CA 94158 USA

Background: Cluster heatmaps are commonly used in biology and related fields to reveal hierarchical clusters in data matrices. This visualization technique has high data density and reveal clusters better than unordered heatmaps alone. However, cluster heatmaps have known issues making them both time consuming to use and prone to error. We hypothesize that visualization techniques without the rigid grid constraint of cluster heatmaps will perform better at clustering-related tasks. Results: We developed an approach to "unbox" the heatmap values and embed them directly in the hierarchical clustering results, allowing us to use standard hierarchical visualization techniques as alternatives to cluster heatmaps. We then tested our hypothesis by conducting a survey of 45 practitioners to determine how cluster heatmaps are used, prototyping alternatives to cluster heatmaps using pair analytics with a computational biologist, and evaluating those alternatives with hour-long interviews of 5 practitioners and an Amazon Mechanical Turk user study with approximately 200 participants. We found statistically significant performance differences for most clustering-related tasks, and in the number of perceived visual clusters. Visit ***/vw0t3 for our results. Conclusions: The optimal technique varied by task. However, gapmaps were preferred by the interviewed practitioners and outperformed or performed as well as cluster heatmaps for clustering-related tasks. Gapmaps are similar to cluster heatmaps, but relax the heatmap grid constraints by introducing gaps between rows and/or columns that are not closely clustered. Based on these results, we recommend users adopt gapmaps as an alternative to cluster heatmaps.

关键词： Systems biology/omics data bioinformatics visualization Hierarchy data Data clustering Qualitative evaluation Quantitative evaluation

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Multiscale dynamic visualization of signal transduction processes with detailing of target-genes activation in three-dimensional genome structure

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Procedia Computer Science 2017年 119卷 182-189页

作者： Kseniia Bureiko Maria Semashko Ksenia D. Mukhina Andrey Karsakov ITMO University Saint Petersburg Russia

Given the complexity of modern biological data it is essentially crucial to accord a consistent expounding. Interpreting such data into complex networks and visualizing them can reveal understanding of various processes in a cell. A consequence mapping of signal transduction processes to the spatial genome structure can benefit new insights in interaction detection in the spatial arrangement of genes. We present an approach for multiscale dynamic visualization of signal transduction processes with detailing of target-genes activation in spatial genome structure. The usage of this approach is demonstrated for the WNT signaling pathway in a human cell. We conclude with suggesting future research questions to improve our approach by considering new available data.

关键词： bioinformatics visualization signaling pathways spatial genome structure

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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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