Diverse Biological Data Research Articles

Hyperdimensional computing: A fast, robust, and interpretable paradigm for biological data.

Advances in bioinformatics are primarily due to new algorithms for processing diverse biological data sources. While sophisticated alignment algorithms have been pivotal in analyzing biological sequences, deep learning has substantially transformed bioinformatics, addressing sequence, structure, and functional analyses. However, these methods are incredibly data-hungry, compute-intensive, and hard to interpret. Hyperdimensional computing (HDC) has recently emerged as an exciting alternative. The key idea is that random vectors of high dimensionality can represent concepts such as sequence identity or phylogeny. These vectors can then be combined using simple operators for learning, reasoning, or querying by exploiting the peculiar properties of high-dimensional spaces. Our work reviews and explores HDC's potential for bioinformatics, emphasizing its efficiency, interpretability, and adeptness in handling multimodal and structured data. HDC holds great potential for various omics data searching, biosignal analysis, and health applications.

Identifying patterns differing between high-dimensional datasets with generalized contrastive PCA.

High-dimensional data have become ubiquitous in the biological sciences, and it is often desirable to compare two datasets collected under different experimental conditions to extract low-dimensional patterns enriched in one condition. However, traditional dimensionality reduction techniques cannot accomplish this because they operate on only one dataset. Contrastive principal component analysis (cPCA) has been proposed to address this problem, but it has seen little adoption because it requires tuning a hyperparameter resulting in multiple solutions, with no way of knowing which is correct. Moreover, cPCA uses foreground and background conditions that are treated differently, making it ill-suited to compare two experimental conditions symmetrically. Here we describe the development of generalized contrastive PCA (gcPCA), a flexible hyperparameter-free approach that solves these problems. We first provide analyses explaining why cPCA requires a hyperparameter and how gcPCA avoids this requirement. We then describe an open-source gcPCA toolbox containing Python and MATLAB implementations of several variants of gcPCA tailored for different scenarios. Finally, we demonstrate the utility of gcPCA in analyzing diverse high-dimensional biological data, revealing unsupervised detection of hippocampal replay in neurophysiological recordings and heterogeneity of type II diabetes in single-cell RNA sequencing data. As a fast, robust, and easy-to-use comparison method, gcPCA provides a valuable resource facilitating the analysis of diverse high-dimensional datasets to gain new insights into complex biological phenomena.

The Use of Survival Dose-Rate Dependencies as Theoretical Discrimination Criteria for In-Silico Dynamic Radiobiological Models.

Cell repair dynamics are crucial in optimizing anti-cancer therapies. Various assays (eg, comet assay and γ-H2AX) assess post-radiation repair kinetics, but interpreting such data is challenging and model-based data analyses are required. However, ambiguities in parameter calibration remain an unsolved challenge. To address this, we propose combining survival dose-rate effects with computer simulations to gain knowledge about repair kinetics. After a literature review, theoretical discriminators based on common fractionation/dose-rate-related effects were defined to discard unrealistic model dynamics. The Multi-Hit Repair (MHR) model was calibrated with canine osteosarcoma Abrams cell line data to study the discriminators' efficacy in scenarios with limited survival data. Additionally, survival dose-rate-dependent data from the human SiHa cervical cancer cell line were used to illustrate the survival behavior at diverse dose-rates and the capability of the MHR to model these data. SiHa data confirmed the validity of the proposed discriminators. The discriminators filtered 99% of parameter sets, improving the calibration of Abrams cells data. Furthermore, results from both cell lines may hint universal aspects of cellular repair. Dose-rate theoretical discrimination criteria are an effective method to understand repair kinetics and improve radiobiological model calibration. Moreover, this methodology may be used to analyze diverse biological data using dynamic models in-silico.

MiRAGE: mining relationships for advanced generative evaluation in drug repositioning.

Drug repositioning, the identification of new therapeutic uses for existing drugs, is crucial for accelerating drug discovery and reducing development costs. Some methods rely on heterogeneous networks, which may not fully capture the complex relationships between drugs and diseases. However, integrating diverse biological data sources offers promise for discovering new drug-disease associations (DDAs). Previous evidence indicates that the combination of information would be conducive to the discovery of new DDAs. However, the challenge lies in effectively integrating different biological data sources to identify the most effective drugs for a certain disease based on drug-disease coupled mechanisms. In response to this challenge, we present MiRAGE, a novel computational method for drug repositioning. MiRAGE leverages a three-step framework, comprising negative sampling using hard negative mining, classification employing random forest models, and feature selection based on feature importance. We evaluate MiRAGE on multiple benchmark datasets, demonstrating its superiority over state-of-the-art algorithms across various metrics. Notably, MiRAGE consistently outperforms other methods in uncovering novel DDAs. Case studies focusing on Parkinson's disease and schizophrenia showcase MiRAGE's ability to identify top candidate drugs supported by previous studies. Overall, our study underscores MiRAGE's efficacy and versatility as a computational tool for drug repositioning, offering valuable insights for therapeutic discoveries and addressing unmet medical needs.

GENETIC ARCHIVES AND LIFELINE: UNRAVELING THE UMBILICAL CORD’S BIOLOGICAL DATABASE

Biological databases are critical components of the field of bioinformatics, which is dedicated to managing, organising, and making sense of the vast and diverse biological data generated by scientific research. These databases play a fundamental role in modern life sciences, allowing researchers, clinicians, and scientists to access, analyse, and interpret a wide range of biological information. These databases serve as invaluable resources, enabling researchers to access, manage, and analyse biological data, accelerating scientific discoveries and enhancing our understanding of life's complexities. There are various types of biological databases that serve as critical resources for researchers and scientists in the field of life sciences. These databases are categorised based on the types of biological data they store and manage. Keywords: Bioinformatics, biological database, genetics, scientific research, genomics, medical advancements, data repositories

Likelihood-based feature representation learning combined with neighborhood information for predicting circRNA-miRNA associations.

Connections between circular RNAs (circRNAs) and microRNAs (miRNAs) assume a pivotal position in the onset, evolution, diagnosis and treatment of diseases and tumors. Selecting the most potential circRNA-related miRNAs and taking advantage of them as the biological markers or drug targets could be conducive to dealing with complex human diseases through preventive strategies, diagnostic procedures and therapeutic approaches. Compared to traditional biological experiments, leveraging computational models to integrate diverse biological data in order to infer potential associations proves to be a more efficient and cost-effective approach. This paper developed a model of Convolutional Autoencoder for CircRNA-MiRNA Associations (CA-CMA) prediction. Initially, this model merged the natural language characteristics of the circRNA and miRNA sequence with the features of circRNA-miRNA interactions. Subsequently, it utilized all circRNA-miRNA pairs to construct a molecular association network, which was then fine-tuned by labeled samples to optimize the network parameters. Finally, the prediction outcome is obtained by utilizing the deep neural networks classifier. This model innovatively combines the likelihood objective that preserves the neighborhood through optimization, to learn the continuous feature representation of words and preserve the spatial information of two-dimensional signals. During the process of 5-fold cross-validation, CA-CMA exhibited exceptional performance compared to numerous prior computational approaches, as evidenced by its mean area under the receiver operating characteristic curve of 0.9138 and a minimal SD of 0.0024. Furthermore, recent literature has confirmed the accuracy of 25 out of the top 30 circRNA-miRNA pairs identified with the highest CA-CMA scores during case studies. The results of these experiments highlight the robustness and versatility of our model.

Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis.

In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADDs historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADDs predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open-Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. This paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery.

Deep neural network learning biological condition information refines gene-expression-based cell subtypes.

With the recent advent of single-cell level biological understanding, a growing interest is in identifying cell states or subtypes that are homogeneous in terms of gene expression and are also enriched in certain biological conditions, including disease samples versus normal samples (condition-specific cell subtype). Despite the importance of identifying condition-specific cell subtypes, existing methods have the following limitations: since they train models separately between gene expression and the biological condition information, (1) they do not consider potential interactions between them, and (2) the weights from both types of information are not properly controlled. Also, (3) they do not consider non-linear relationships in the gene expression and the biological condition. To address the limitations and accurately identify such condition-specific cell subtypes, we develop scDeepJointClust, the first method that jointly trains both types of information via a deep neural network. scDeepJointClust incorporates results from the power of state-of-the-art gene-expression-based clustering methods as an input, incorporating their sophistication and accuracy. We evaluated scDeepJointClust on both simulation data in diverse scenarios and biological data of different diseases (melanoma and non-small-cell lung cancer) and showed that scDeepJointClust outperforms existing methods in terms of sensitivity and specificity. scDeepJointClust exhibits significant promise in advancing our understanding of cellular states and their implications in complex biological systems.

English

Drugs for medical purpose aims to save a person’s life and improve the quality of life. They enhance one’s mental or physical fitness and treat disease. However, drugs might cause adverse reactions or even unexpected effects on patients, known as Adverse Drug Reactions (ADRs). For preventing ADRs, drug trials were carried out to evaluate and analyze the potential danger in the procedure of drug development. Deep learning (DL) is a type of Machine Learning (ML) in Artificial intelligence (AI) that has developed as a highly effective and promising method that can interrogate and combine diverse biological data kinds to produce new hypotheses. DL is extensively used in drug repurposing and discovery, but its application in ADR prediction employing gene expression data is limited. Therefore, this study introduces a Deer Hunting Optimization Driven Deep Learning Model for Robust Adverse Drug Reaction Recognition and Classification (DHODL-ADRRC) technique. The DHODL-ADRRC technique involves diverse phases of data preprocessing to normalize the data. In addition, the Binary Fire Hawks Optimization (BFHO) based feature selection (FS) method is used to elect an optimum set of features. Moreover, the Attention-based Convolutional Bidirectional Long Short-Term Memory (ACBLSTM) algorithm is utilized for ADR identification. Furthermore, the DHO model is exploited for the tuning process adjusting of the ACBLSTM model, advancing the classification outputs. The simulation output of the DHODL-ADRRC algorithm was investigated on the ADR dataset, and the extensive outputs highlighted the advanced achievement of the DHODL-ADRRC method over other current methods in terms of different measures.

Xenbase: key features and resources of the Xenopus model organism knowledgebase.

Xenbase (https://www.xenbase.org/), the Xenopus model organism knowledgebase, is a web-accessible resource that integrates the diverse genomic and biological data from research on the laboratory frogs Xenopus laevis and Xenopus tropicalis. The goal of Xenbase is to accelerate discovery and empower Xenopus research, to enhance the impact of Xenopus research data, and to facilitate the dissemination of these data. Xenbase also enhances the value of Xenopus data through high-quality curation, data integration, providing bioinformatics tools optimized for Xenopus experiments, and linking Xenopus data to human data, and other model organisms. Xenbase also plays an indispensable role in making Xenopus data interoperable and accessible to the broader biomedical community in accordance with FAIR principles. Xenbase provides annotated data updates to organizations such as NCBI, UniProtKB, Ensembl, the Gene Ontology consortium, and most recently, the Alliance of Genomic Resources, a common clearing house for data from humans and model organisms. This article provides a brief overview of key and recently added features of Xenbase. New features include processing of Xenopus high-throughput sequencing data from the NCBI Gene Expression Omnibus; curation of anatomical, physiological, and expression phenotypes with the newly created Xenopus Phenotype Ontology; Xenopus Gene Ontology annotations; new anatomical drawings of the Normal Table of Xenopus development; and integration of the latest Xenopus laevis v10.1 genome annotations. Finally, we highlight areas for future development at Xenbase as we continue to support the Xenopus research community.

Retracted] Heterogeneous Network‐Based Inductive Matrix Methods for Predicting Biomedical Gene Disease

Prediction of gene‐disease associations has grown in popularity in recent biomedical research. However, positive and unlabeled (PU) issues and limited gene‐disease association data are common concerns with present association prediction algorithms. A gene‐disease association prediction approach based on Katz‐enhanced inductive matrix completion is suggested in light of the abovementioned flaws. Preestimate based on the Katz technique and refined estimation based on the inductive matrix completion approach makes the model. The Katz technique is utilized to preestimate the gene‐disease association on the basis of gene‐disease heterogeneous network to mitigate the effects of association data‐sparse and PU issues. The Katz technique, however, necessarily introduces some noise when predicting gene‐disease connections due to the similarity network’s quality limitations. Therefore, the elastic net regularization approach is utilized to increase the resilience of the conventional inductive matrix completion model. As a result, the prediction effect of gene‐disease connections is increased using robustness and a better inductive matrix completion model. The experimental findings demonstrate that the proposed model has dramatically increased recall and precision compared to widely used gene‐disease association prediction approaches. It can also resolve the typical cold‐start issue in association prediction. The proposed KIMC method may consider integrating more diverse biological data sources in the future and also aid in the effective extraction of the feature data of genes and diseases with higher correlation from this biological data to improve the prediction effect.

Integrated Analysis of Multiomics Data Identified Molecular Subtypes and Oxidative Stress-Related Prognostic Biomarkers in Glioblastoma Multiforme.

Glioblastoma multiforme (GBM) is a glioma in IV stage, which is one of the most common primary malignant brain tumors in adults. GBM has the characters of high invasiveness, high recurrence rate, and low survival rate and with a poor prognosis. GBM implicates various genetic changes and epigenetic and gene transcription disorders, which are crucial in developing GBM. With the progression and enhancement of high-throughput sequencing technologies, the acquirement and administering approaches of diverse biological omics data on distinctive levels are developing more advanced. However, the research of GBM with multiomics remains largely unknown. We identified GBM-related molecular subtypes by integrated multiomics data and exploring the connections of gene copy number variation (CNV) and methylation gene (MET) change data. The expression of CNV and MET genes was examined through cluster integration analysis. The present study confirmed three clusters (iC1, iC2, and iC3) with distinctive prognosis and molecule peculiarities. We also recognized three oxidative stress protecting molecules (OSMR, IGFBP6, and MYBPH) by contrasting gene expression, MET, and CNV in the three subtypes. OSMR, IGFBP6, and MYBPH were differentially expressed in the clusters, suggesting they might be recognized as characteristic markers for the three clusters in GBM. Through integrative investigation of genomics, epigenomics, and transcriptomics, we offer novel visions into the multilayered molecules of GBM and facilitate the accuracy remedy for GBM sufferers.

An Analysis of Different Distance-Linkage Methods for Clustering Gene Expression Data and Observing Pleiotropy: Empirical Study

Background Large amounts of biological data have been generated over the last few decades, encouraging scientists to look for connections between genes that cause various diseases. Clustering illustrates such a relationship between numerous species and genes. Finding an appropriate distance-linkage metric to construct clusters from diverse biological data sets has thus become critical. Pleiotropy is also important for a gene’s expression to vary and create varied consequences in living things. Finding the pleiotropy of genes responsible for various diseases has become a major research challenge. Objective Our goal was to establish the optimal distance-linkage strategy for creating reliable clusters from diverse data sets and identifying the common genes that cause various tumors to observe genes with pleiotropic effect. Methods We considered 4 linking methods—single, complete, average, and ward—and 3 distance metrics—Euclidean, maximum, and Manhattan distance. For assessing the quality of different sets of clusters, we used a fitness function that combines silhouette width and within-cluster distance. Results According to our findings, the maximum distance measure produces the highest-quality clusters. Moreover, for medium data set, the average linkage method, and for large data set, the ward linkage method works best. The outcome is not improved by using ensemble clustering. We also discovered genes that cause 3 different cancers and used gene enrichment to confirm our findings. Conclusions Accuracy is crucial in clustering, and we investigated the accuracy of numerous clustering techniques in our research. Other studies may aid related works if the data set is similar to ours.

Data sharing by biologists: A comparative study of genome sequence data and lab experiment data

Whether biologists share data depends on data type. This study employed an integrated theoretical framework combining theory of planned behavior and institutional theory to investigate individual motivations, technological resources, and institutional pressures affecting data sharing intentions between biologists depending on whether they were sharing genome sequence data or regular lab experiment data. A series of statistical analyses including principal component factor analysis, hierarchical regression, and independent sample t-test were conducted on a total of 342 survey responses from biologists. The results suggest that biologists' data sharing intentions are influenced by individual motivations, technological resources, and institutional pressures, and that the intensities of those factors are considerably different for genome sequence data and lab experiment data. This study suggests that data sharing by biologists varies depending on the type of data they produce, and that different policies and technological supports should be applied to facilitate the sharing of diverse biological data types.

QSAR-Co-X: an open source toolkit for multitarget QSAR modelling

Quantitative structure activity relationships (QSAR) modelling is a well-known computational tool, often used in a wide variety of applications. Yet one of the major drawbacks of conventional QSAR modelling is that models are set up based on a limited number of experimental and/or theoretical conditions. To overcome this, the so-called multitasking or multitarget QSAR (mt-QSAR) approaches have emerged as new computational tools able to integrate diverse chemical and biological data into a single model equation, thus extending and improving the reliability of this type of modelling. We have developed QSAR-Co-X, an open source python–based toolkit (available to download at https://github.com/ncordeirfcup/QSAR-Co-X) for supporting mt-QSAR modelling following the Box-Jenkins moving average approach. The new toolkit embodies several functionalities for dataset selection and curation plus computation of descriptors, for setting up linear and non-linear models, as well as for a comprehensive results analysis. The workflow within this toolkit is guided by a cohort of multiple statistical parameters and graphical outputs onwards assessing both the predictivity and the robustness of the derived mt-QSAR models. To monitor and demonstrate the functionalities of the designed toolkit, four case-studies pertaining to previously reported datasets are examined here. We believe that this new toolkit, along with our previously launched QSAR-Co code, will significantly contribute to make mt-QSAR modelling widely and routinely applicable.

DDBJ update: streamlining submission and access of human data.

The Bioinformation and DDBJ Center (DDBJ Center, https://www.ddbj.nig.ac.jp) provides databases that capture, preserve and disseminate diverse biological data to support research in the life sciences. This center collects nucleotide sequences with annotations, raw sequencing data, and alignment information from high-throughput sequencing platforms, and study and sample information, in collaboration with the National Center for Biotechnology Information (NCBI) and the European Bioinformatics Institute (EBI). This collaborative framework is known as the International Nucleotide Sequence Database Collaboration (INSDC). In collaboration with the National Bioscience Database Center (NBDC), the DDBJ Center also provides a controlled-access database, the Japanese Genotype–phenotype Archive (JGA), which archives and distributes human genotype and phenotype data, requiring authorized access. The NBDC formulates guidelines and policies for sharing human data and reviews data submission and use applications. To streamline all of the processes at NBDC and JGA, we have integrated the two systems by introducing a unified login platform with a group structure in September 2020. In addition to the public databases, the DDBJ Center provides a computer resource, the NIG supercomputer, for domestic researchers to analyze large-scale genomic data. This report describes updates to the services of the DDBJ Center, focusing on the NBDC and JGA system enhancements.

PTML Multi-Label Algorithms: Models, Software, and Applications.

By combining Machine Learning (ML) methods with Perturbation Theory (PT), it is possible to develop predictive models for a variety of response targets. Such combination often known as Perturbation Theory Machine Learning (PTML) modeling comprises a set of techniques that can handle various physical, and chemical properties of different organisms, complex biological or material systems under multiple input conditions. In so doing, these techniques effectively integrate a manifold of diverse chemical and biological data into a single computational framework that can then be applied for screening lead chemicals as well as to find clues for improving the targeted response(s). PTML models have thus been extremely helpful in drug or material design efforts and found to be predictive and applicable across a broad space of systems. After a brief outline of the applied methodology, this work reviews the different uses of PTML in Medicinal Chemistry, as well as in other applications. Finally, we cover the development of software available nowadays for setting up PTML models from large datasets.

The QSAR Paradigm in Fragment-Based Drug Discovery: From the Virtual Generation of Target Inhibitors to Multi-Scale Modeling.

Fragment-Based Drug Design (FBDD) has established itself as a promising approach in modern drug discovery, accelerating and improving lead optimization, while playing a crucial role in diminishing the high attrition rates at all stages in the drug development process. On the other hand, FBDD has benefited from the application of computational methodologies, where the models derived from the Quantitative Structure-Activity Relationships (QSAR) have become consolidated tools. This mini-review focuses on the evolution and main applications of the QSAR paradigm in the context of FBDD in the last five years. This report places particular emphasis on the QSAR models derived from fragment-based topological approaches to extract physicochemical and/or structural information, allowing to design potentially novel mono- or multi-target inhibitors from relatively large and heterogeneous databases. Here, we also discuss the need to apply multi-scale modeling, to exemplify how different datasets based on target inhibition can be simultaneously integrated and predicted together with other relevant endpoints such as the biological activity against non-biomolecular targets, as well as in vitro and in vivo toxicity and pharmacokinetic properties. In this context, seminal papers are briefly analyzed. As huge amounts of data continue to accumulate in the domains of the chemical, biological and biomedical sciences, it has become clear that drug discovery must be viewed as a multi-scale optimization process. An ideal multi-scale approach should integrate diverse chemical and biological data and also serve as a knowledge generator, enabling the design of potentially optimal chemicals that may become therapeutic agents.

Heterogeneous Multi-Layered Network Model for Omics Data Integration and Analysis.

Advances in next-generation sequencing and high-throughput techniques have enabled the generation of vast amounts of diverse omics data. These big data provide an unprecedented opportunity in biology, but impose great challenges in data integration, data mining, and knowledge discovery due to the complexity, heterogeneity, dynamics, uncertainty, and high-dimensionality inherited in the omics data. Network has been widely used to represent relations between entities in biological system, such as protein-protein interaction, gene regulation, and brain connectivity (i.e. network construction) as well as to infer novel relations given a reconstructed network (aka link prediction). Particularly, heterogeneous multi-layered network (HMLN) has proven successful in integrating diverse biological data for the representation of the hierarchy of biological system. The HMLN provides unparalleled opportunities but imposes new computational challenges on establishing causal genotype-phenotype associations and understanding environmental impact on organisms. In this review, we focus on the recent advances in developing novel computational methods for the inference of novel biological relations from the HMLN. We first discuss the properties of biological HMLN. Then we survey four categories of state-of-the-art methods (matrix factorization, random walk, knowledge graph, and deep learning). Thirdly, we demonstrate their applications to omics data integration and analysis. Finally, we outline strategies for future directions in the development of new HMLN models.

A Protocol for the Construction and Curation of Genome-Scale Integrated Metabolic and Regulatory Network Models.

Genome-scale metabolic network models have been widely used over the last decade and have been shown to successfully predict the metabolic behavior of many organisms. Yet the complexity of metabolic regulation often limits the accuracy of these models. Integrative modeling approaches have recently been developed that combine metabolic and regulatory networks, thereby expanding the capabilities and accuracy of genome-scale modeling. This chapter provides a guide to reconstruct and curate such integrated network models. Specifically, this protocol describes the PROM (Probabilistic Regulation of Metabolism) and GEMINI (Gene Expression and Metabolism Integrated for Network Inference) approaches. PROM is an automated method for the construction of integrated metabolic and transcriptional regulatory network models, while the GEMINI approach curates the integrated network models using transcriptomics and phenomics data. GEMINI represents the first attempt at applying well-established curation tools that exist for metabolic networks to be applied for curating regulatory networks. The integrated network models generated by these approaches enable the mechanistic integration of diverse biological data and can identify novel strategies to engineer cellular metabolism.