Bioinformatics Applications Research Articles

Role of Transcriptomics in Precision Oncology

: Transcriptome profiling is one of the most widely used approaches in the field of multiomics research. It plays a crucial role in the prognostic, diagnostic, and predictive treatment of cancer patients. Novel next-generation sequencing (NGS) technologies permit the identification of cancer biomarkers, gene signatures, and their abnormal expression, affecting oncogenic and molecular targets and novel biomarkers for cancer therapies. Multiomics studies have changed the overall understanding of cancer and opened a precise perspective for tumor diagnostics and therapy. The use of these approaches has strengthened our understanding of disease pathophysiology and classifications at the molecular level, including specific interference with drug mechanisms of action. Still, it has limited added value in the clinical setting. The omics data on precision medicine include the application of data from genes, transcripts, and proteins for diagnosis, monitoring of diseases, risk factor determination, counseling, and development of novel therapeutics. Bioinformatics applications have expanded statistics-based analysis toward deriving molecular pathways and process models for characterizing phenotypes and drug action mechanisms. In this review, we will discuss transcriptomics and interference analysis that allows the identification of predictive biomarkers at the molecular level to test drug response and analyze the molecular process interface of disease progression-relevant pathophysiology and mechanism of action to propose predictive biomarkers.

The deep learning applications in IoT-based bio- and medical informatics: a systematic literature review

AbstractNowadays, machine learning (ML) has attained a high level of achievement in many contexts. Considering the significance of ML in medical and bioinformatics owing to its accuracy, many investigators discussed multiple solutions for developing the function of medical and bioinformatics challenges using deep learning (DL) techniques. The importance of DL in Internet of Things (IoT)-based bio- and medical informatics lies in its ability to analyze and interpret large amounts of complex and diverse data in real time, providing insights that can improve healthcare outcomes and increase efficiency in the healthcare industry. Several applications of DL in IoT-based bio- and medical informatics include diagnosis, treatment recommendation, clinical decision support, image analysis, wearable monitoring, and drug discovery. The review aims to comprehensively evaluate and synthesize the existing body of the literature on applying deep learning in the intersection of the IoT with bio- and medical informatics. In this paper, we categorized the most cutting-edge DL solutions for medical and bioinformatics issues into five categories based on the DL technique utilized: convolutional neural network, recurrent neural network, generative adversarial network, multilayer perception, and hybrid methods. A systematic literature review was applied to study each one in terms of effective properties, like the main idea, benefits, drawbacks, methods, simulation environment, and datasets. After that, cutting-edge research on DL approaches and applications for bioinformatics concerns was emphasized. In addition, several challenges that contributed to DL implementation for medical and bioinformatics have been addressed, which are predicted to motivate more studies to develop medical and bioinformatics research progressively. According to the findings, most articles are evaluated using features like accuracy, sensitivity, specificity, F-score, latency, adaptability, and scalability.

BehaviorNet: A Fine-grained Behavior-aware Network for Dynamic Link Prediction

Dynamic link prediction has become a trending research subject because of its wide applications in the web, sociology, transportation, and bioinformatics. Currently, the prevailing approach for dynamic link prediction is based on graph neural networks, in which graph representation learning is the key to perform dynamic link prediction tasks. However, there are still great challenges because the structure of graphs evolves over time. A common approach is to represent a dynamic graph as a collection of discrete snapshots, in which information over a period is aggregated through summation or averaging. This way results in some fine-grained time-related information loss, which further leads to a certain degree of performance degradation. We conjecture that such fine-grained information is vital because it implies specific behavior patterns of nodes and edges in a snapshot. To verify this conjecture, we propose a novel fine-grained behavior-aware network (BehaviorNet) for dynamic network link prediction. Specifically, BehaviorNet adapts a transformer-based graph convolution network to capture the latent structural representations of nodes by adding edge behaviors as an additional attribute of edges. GRU is applied to learn the temporal features of given snapshots of a dynamic network by utilizing node behaviors as auxiliary information. Extensive experiments are conducted on several real-world dynamic graph datasets, and the results show significant performance gains for BehaviorNet over several state-of-the-art (SOTA) discrete dynamic link prediction baselines. Ablation study validates the effectiveness of modeling fine-grained edge and node behaviors.

Biomedical Informatics: State of the Art, Challenges, and Opportunities

Biomedical informatics can be considered as a multidisciplinary research and educational field situated at the intersection of computational sciences (including computer science, data science, mathematics, and statistics), biology, and medicine. In recent years, there have been advances in the field of biomedical informatics. The current article highlights some interesting state-of-the-art research outcomes in these fields. These include research outcomes in areas like (i) computational biology and medicine, (ii) explainable artificial intelligence (XAI) in biomedical research and clinical practice, (iii) machine learning (including deep learning) methods and application for bioinformatics and healthcare, (iv) imaging informatics, as well as (v) medical statistics and data science. Moreover, the current article also discusses some existing challenges and potential future directions for these research areas to advance the fields of biomedical informatics.

Application of Bioinformatics in Predicting the Efficacy of Digestive Tumour Immunotherapy Target TIM-3 and its Inhibitors.

Background: Our main objective is to apply bioinformatics in predicting the efficacy of digestive tumour immunotherapy target TIM-3 and its inhibitors. Methods: Our study used the gene expression omnibus (GEO) database to identify datasets associated with digestive tumours and the action of TIM-3. The GSE427729 dataset based on the GPL10192 platform. The dataset consisted of six samples of total RNA derived from TIM-3 control and knockdown RAW 264.7 cells. We used GEO2R tool to identify DEGs before performing Gene Ontology and identifying the kyoto encyclopedia of genes and genomes (KEGG) pathways. Lastly, we determined the PPI networks to identify hub genes. Results: Our study identified 57 differentially expressed genes based on an adjusted p-value of less than 0.05 and a log2 fold change of 2.0. There were 26 down-regulated genes with 31 up-regulated genes while 22, 404 genes were non-significant. The DEGs were enriched in biological pathways such as activating leukocytes, cells, and development of the immune system. Additionally, we identified four significant KEGG pathways that were implicated in digestive tumour immunotherapy and TIM-3; pathways of pancreatic cancer, NF-Kappa B signalling pathway, Toll-like receptor signalling pathway and C-type lectin receptor signalling pathway. The PPI networks identified 10 hub genes that were implicated in digestive tumour immunotherapy target TIM-3 (Myd88, Traf6, Irf7, Cdk4, Ccnd2, Mapkap1, Prr5, Mpp3, Serpinb6b and Pvrl3). Conclusion: Targeting these biological pathways, KEGG pathways, molecular functions and cellular processes can lead to novel therapeutic treatment and management in digestive tumours based on TIM-3 immunotherapy.

Selective protease inhibitors from secondary metabolites of Philippine medicinal plants against porcine epidemic diarrhea virus: A computational veterinary drug discovery approach

Background: Porcine epidemic diarrhea virus (PEDV) is a recurring coronavirus that causes severe diarrhea in pigs with high mortality and morbidity rates, especially in neonatal pigs. Despite the availability of vaccines, their efficacy is limited owing to antigenic differences between the vaccine and field strains, which poses a challenge to infection control. Antiviral drugs targeting conserved PEDV proteins show promise for complementing vaccination strategies. PEDV Nsp3 (PL2Pro) and Nsp5 (3CLPro) are essential proteases vital for viral replication, making them attractive targets for drug development against PEDV. Aim: To address the lack of therapeutics against recurring PEDV outbreaks and bridge the gap in the application of bioinformatics in veterinary drug discovery, this study aimed to discover compounds that inhibit PEDV proteases from Philippine medicinal plants by applying a modified virtual screening methodology that considers the physiology of swine hosts. Methods: This study employed a library of 690 metabolites from Philippine medicinal plants to screen for potential protease inhibitors targeting PEDV PL2Pro and 3CLPro. This includes evaluating the binding affinity, pharmacokinetics, dynamic stability, and critical binding site residues. Compounds demonstrating high affinity underwent a modified ADMET analysis, considering the enteric localization of the virus and potential toxicity to swine hosts. Furthermore, molecular dynamics simulations assessed compound stability under physiological swine conditions. Results: The study identified Bisandrographolide from Andrographis paniculata, CID 162866964 from Euphorbia neriifolia, and betulinic acid from Vitex negundo and Ocimum basilicum as metabolites that bind favorably and selectively to PEDV 3CLPro and have excellent pharmacokinetic properties and dynamic stability. In contrast, no selective inhibitor for PL2pro passed the same criteria. Conclusion: Employing the modified virtual screening protocol tailored for swine host considerations, the compounds identified in this study are anticipated to exert inhibitory effects against PEDV without off-target binding to analogous swine proteases and receptors. CID 162866964, bisandrographolide, and betulinic acid show promise for developing potent antivirals against PEDV.

Synergies at the Intersection: Exploring the Role of Bioinformatics in Enhancing Physiotherapy and Nursing Practice

In contemporary healthcare, integrating bioinformatics with physiotherapy and nursing science has emerged as a promising approach to personalized patient care and clinical decision-making. This study explores the synergistic potential of this integration, aiming to enhance healthcare delivery and improve patient outcomes. The fundamental concepts and applications of bioinformatics are discussed, highlighting its role in analyzing biological data and informing evidence-based practice. Subsequently, an overview of physiotherapy and nursing roles in patient care is provided, emphasizing their holistic approach and interdisciplinary collaboration. The study examines the benefits and challenges of integrating bioinformatics into clinical practice, including personalized treatment planning, disease diagnosis, and predictive modeling. Case studies and examples illustrating successful integration efforts are presented, along with potential applications and future directions for bioinformatics-driven healthcare. Furthermore, the importance of data management and analysis in healthcare settings is explored, discussing tools, techniques, and ethical considerations related to the use of healthcare data. However, collaborative approaches and interdisciplinary teamwork strategies are discussed, emphasizing the need for education, research, policy development, and infrastructure investments to support the integration of bioinformatics into healthcare delivery. This study highlights the transformative potential of bioinformatics-driven approaches in physiotherapy and nursing practice, providing insights for healthcare professionals, researchers, policymakers, and educators.

Examination of Genetic Background of Intrahepatic Cholangiocarcinoma by Bioinformatics Applications

Examination of Genetic Background of Intrahepatic Cholangiocarcinoma by Bioinformatics Applications

Translational bioinformatics approach to combat cardiovascular disease and cancers.

Bioinformatics is an interconnected subject of science dealing with diverse fields including biology, chemistry, physics, statistics, mathematics, and computer science as the key fields to answer complicated physiological problems. Key intention of bioinformatics is to store, analyze, organize, and retrieve essential information about genome, proteome, transcriptome, metabolome, as well as organisms to investigate the biological system along with its dynamics, if any. The outcome of bioinformatics depends on the type, quantity, and quality of the raw data provided and the algorithm employed to analyze the same. Despite several approved medicines available, cardiovascular disorders (CVDs) and cancers comprises of the two leading causes of human deaths. Understanding the unknown facts of both these non-communicable disorders is inevitable to discover new pathways, find new drug targets, and eventually newer drugs to combat them successfully. Since, all these goals involve complex investigation and handling of various types of macro- and small- molecules of the human body, bioinformatics plays a key role in such processes. Results from such investigation has direct human application and thus we call this filed as translational bioinformatics. Current book chapter thus deals with diverse scope and applications of this translational bioinformatics to find cure, diagnosis, and understanding the mechanisms of CVDs and cancers. Developing complex yet small or long algorithms to address such problems is very common in translational bioinformatics. Structure-based drug discovery or AI-guided invention of novel antibodies that too with super-high accuracy, speed, and involvement of considerably low amount of investment are some of the astonishing features of the translational bioinformatics and its applications in the fields of CVDs and cancers.

Application of data science and bioinformatics in RNA therapeutics.

Nowadays, information technology (IT) has been holding a significant role in daily life worldwide. The trajectory of data science and bioinformatics promises pioneering personalized therapies, reshaping medical landscapes and patient care. For RNA therapy to reach more patients, a comprehensive understanding of the application of data science and bioinformatics to this therapy is essential. Thus, this chapter has summarized the application of data science and bioinformatics in RNA therapeutics. Data science applications in RNA therapy, such as data integration and analytics, machine learning, and drug development, have been discussed. In addition, aspects of bioinformatics such as RNA design and evaluation, drug delivery system simulation, and databases for personalized medicine have also been covered in this chapter. These insights have shed light on existing evidence and opened potential future directions. From there, scientists can elevate RNA-based therapeutics into an era of tailored treatments and revolutionary healthcare.

Study on advancement in bioinformatics for crop improvement: integrating genomics, phenomics, and machine learning

Bioinformatics has emerged as a vital tool in agriculture, performing an important role in crop improvement and precision agriculture. This review study explores the applications of bioinformatics in these areas, highlighting the importance of next-generation sequencing (NGS) and third-generation sequencing technologies like PacBio and ONT. The integration of bioinformatics and genomics has revolutionized crop improvement strategies, facilitating the creation of crop varieties that are more suitable for evolving climates. The review emphasizes the creation of integrated crop databases for managing large-scale genotype and phenotype data and discusses the role of bioinformatics in precision agriculture for optimizing resource usage and minimizing environmental impact. Multiomics technologies are highlighted for gaining insights into plant biology and facilitating informed decisions in crop improvement. Machine learning algorithms play a prominent role in interpreting diverse and complex datasets generated by imaging or sequencing, aiding in efficient plant phenotyping and the identification of associations between DNA sequences and traits. The review concludes by emphasizing the importance of collaboration, data integration, and the potential of bioinformatics in advancing agriculture.

Bioinformatics and bioactive peptides from foods: Do they work together?

We live in the Big Data Era which affects many aspects of science, including research on bioactive peptides derived from foods, which during the last few decades have been a focus of interest for scientists. These two issues, i.e., the development of computer technologies and progress in the discovery of novel peptides with health-beneficial properties, are closely interrelated. This Chapter presents the example applications of bioinformatics for studying biopeptides, focusing on main aspects of peptide analysis as the starting point, including: (i) the role of peptide databases; (ii) aspects of bioactivity prediction; (iii) simulation of peptide release from proteins. Bioinformatics can also be used for predicting other features of peptides, including ADMET, QSAR, structure, and taste. To answer the question asked "bioinformatics and bioactive peptides from foods: do they work together?", currently it is almost impossible to find examples of peptide research with no bioinformatics involved. However, theoretical predictions are not equivalent to experimental work and always require critical scrutiny. The aspects of compatibility of in silico and in vitro results are also summarized herein.

Optimal Structured Matrix Approximation for Robustness to Incomplete Biosequence Data.

We propose a general method for optimally approximating an arbitrary matrix M by a structured matrix T (circulant, Toeplitz/Hankel, etc.) and examine its use for estimating the spectra of genomic linkage disequilibrium matrices. This application is prototypical of a variety of genomic and proteomic problems that demand robustness to incomplete biosequence information. We perform a simulation study and corroborative test of our method using real genomic data from the Mouse Genome Database [1]. The results confirm the predicted utility of the method and provide strong evidence of its potential value to a wide range of bioinformatics applications. Our optimal general matrix approximation method is expected to be of independent interest to an even broader range of applications in applied mathematics and engineering.

Integrating Biotechnology and Bioinformatics: A Case Report on Advancements in Personalized Medicine

In recent years, the integration of biotechnology and bioinformatics has revolutionized personalized medicine, leading to significant advancements in disease diagnosis, treatment, and drug development. This case report highlights the application of biotechnology and bioinformatics in a personalized medicine approach for the management of a patient with a complex medical condition. The integration of genomic data analysis, molecular profiling, and targeted therapy resulted in a tailored treatment regimen that significantly improved patient outcomes. This case report underscores the potential of biotechnology and bioinformatics in shaping the future of personalized medicine and precision healthcare.

The Application of Genomics and Bioinformatics to Recognize Soybean Pathogen Interaction in a Changing Climate

The changing climate and continuously increasing global population has posed immense pressure on plant scientists to ensure per capita availability of food under scarcity of natural resources. The present circumstances also enforce to understand how rising CO2 level caused greenhouse effect in the environment which causes rise in the temperature and consequently influence the development of plant disease epidemic, to predict the genomic-based models under changing environmental conditions and to ensure continued food production. The application of genomics and bioinformatics tools are the innovative approaches to understand plant pathogen interaction and prediction that offers unique opportunities to enhance plant yield, stability in quality and production, boost the resilience of soybean to integrate resistance genes in present cultivars to sustain yield under epidemic disease conditions in abruptly changing climate. However, genomics data can be used for genomic based selections and advanced bioinformatics tools can be used to make predictions for genotype by pathogen interaction whereas it had ever been remained a great challenge to homogenize the diverse genomic data to which advanced skills and expertise are required in handling bioinformatics tools. Bioinformatics and genomics have the potential to sustain yield for food security by selection of tolerant or resistant genetic combination for disease sensitive environments as well as to facilitate for maintaining food security and mitigate the effect of inversely changing climatic conditions.

AN IMPROVED INDEXING METHOD FOR QUERYING BIG XML FILES

The exponential growth of bioinformatics in the healthcare domain has revolutionized our understanding of DNA, proteins, and other biomolecular entities. This remarkable progress has generated an overwhelming volume of data, necessitating big data technologies for efficient storage and indexing. While big data technologies like Hadoop offer substantial support for big XML file storage, the challenges of indexing data sizes and XPath query performance persist. To enhance the efficiency of XPath queries and address the data size problem, a novel approach that is derived from the spatial indexing method of the R-tre family. The proposed method is to modify the structure of leaf nodes in the indexing tree to preserve XML-sibling connections. Then, new algorithms for constructing the new tree structure and processing sibling queries better are introduced. Experimental results demonstrate the superior efficiency of sibling XPath queries with reduced data sizes for indexing, while other XPath queries exhibit notable performance improvements. This research contributes to the development of more effective indexing methods for managing and querying large XML datasets in bioinformatics applications, ultimately advancing biomedical research and healthcare initiatives.

Innovation in Bioinformatics: Recent tools, Database and Involvement of Artificial Intelligence

Bioinformatics has evolved in recent years into a crucial subject and a well-liked research area that is interconnected with many approaches and disciplines. The capacity of bioinformatics and its approaches to tackle challenging biological problems and promote research and development. There are various tools and database which are used in bioinformatics. AI is the capacity of a computational system to carry out various activities associated with intellectual beings and as a computer system's imitation of human intelligence processes. The bioinformatics applications with artificial intelligence have the capacity to annotate the data in the direction of logical conclusions. By combining AI and bioinformatics molecular dynamic simulations, molecular docking studies, annotations of biological sequences, computational drug design, and gene prediction can be analyzed effectively. The structural bioinformatics tools with artificial intelligence (AI) are effective approaches for designing novel active chemicals to treat neurological diseases and cancer. Immunoinformatics, vaccinology, health informatics, medical informatics, medical science, and pharmaceutical sciences are just a few of the health sciences that have benefited greatly from advances in AI and bioinformatics. Future developments in omics and other fields are predicted to generate large amounts of data quickly, and bioinformatics will be essential in managing, analyzing, and discovering new uses for this data. Bioinformatics will be crucial in saving time and costs by applying AI to examine the massive data sets. Additionally, it will hasten biological discoveries, particularly those related to health, biomedical research, and robotic surgery.

Algorithms for the Uniqueness of the Longest Common Subsequence.

Given several number sequences, determining the longest common subsequence is a classical problem in computer science. This problem has applications in bioinformatics, especially determining transposable genes. Nevertheless, related works only consider how to find one longest common subsequence. In this paper, we consider how to determine the uniqueness of the longest common subsequence. If there are multiple longest common subsequences, we also determine which number appears in all/some/none of the longest common subsequences. We focus on four scenarios: (1) linear sequences without duplicated numbers; (2) circular sequences without duplicated numbers; (3) linear sequences with duplicated numbers; (4) circular sequences with duplicated numbers. We develop corresponding algorithms and apply them to gene sequencing data.

OMG: Towards Effective Graph Classification Against Label Noise

Graph classification is a fundamental problem with diverse applications in bioinformatics and chemistry. Due to the intricate procedures of manual annotations in graphical domains, there may be abundant noisy labels of graphs in practice, resulting in poor performance for existing supervised methods. Thus, it is necessary and urgent to study the problem of graph classification with label noise. However, this problem is challenging due to the overfitting of noisy data as well as complicated relational structures of graphs. To handle this problem, we present a simple but effective approach called c <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</u> upled <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</u> ix for <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</u> raph Contrast (OMG), which combines coupled Mixup with graph contrastive learning in the feature space. On the one hand, to improve the model generalization, we take convex combination of sample pairs in the feature space for positive pair construction. On the other hand, to accomplish effective optimization, we offer challenging negatives by multiple sample Mixup with different emphasis. To further reduce the impact of noisy data, we develop a neighbour-aware noise removal strategy, which promotes the smoothness in the neighbourhood of samples following the principle of curriculum learning. Extensive experiments on a range of benchmark datasets demonstrate the superiority of our proposed OMG.

SAKE: Strobemer-assisted k-mer extraction.

K-mer-based analysis plays an important role in many bioinformatics applications, such as de novo assembly, sequencing error correction, and genotyping. To take full advantage of such methods, the k-mer content of a read set must be captured as accurately as possible. Often the use of long k-mers is preferred because they can be uniquely associated with a specific genomic region. Unfortunately, it is not possible to reliably extract long k-mers in high error rate reads with standard exact k-mer counting methods. We propose SAKE, a method to extract long k-mers from high error rate reads by utilizing strobemers and consensus k-mer generation through partial order alignment. Our experiments show that on simulated data with up to 6% error rate, SAKE can extract 97-mers with over 90% recall. Conversely, the recall of DSK, an exact k-mer counter, drops to less than 20%. Furthermore, the precision of SAKE remains similar to DSK. On real bacterial data, SAKE retrieves 97-mers with a recall of over 90% and slightly lower precision than DSK, while the recall of DSK already drops to 50%. We show that SAKE can extract more k-mers from uncorrected high error rate reads compared to exact k-mer counting. However, exact k-mer counters run on corrected reads can extract slightly more k-mers than SAKE run on uncorrected reads.