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69 articles for “Genomic data”
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GenChrome-ML: A Machine Learning Framework for Early Detection of Chromosomal Disorders Using Genomic Data
Abstract: The increasing burden of chronic disease and cancer demands innovative, more rapid and effective diagnostic tools in the field of healthcare. The majority of current diagnostic tools are dependent upon clinical symptomology and manual evaluation, leading to delays in early detection and treatment. The development of artificial intelligence (AI) and machine learning (ML), in recent years, has offered opportunities for the enhancement of disease prediction, diagnosis and personalization of treatment …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 2, 2026 Read article
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Bioinformatics Tools and Databases for Functional Annotation of Genomes
Abstract: Functional annotation of genomes plays a crucial role in understanding the functions and characteristics of genes. Bioinformatics tools and databases have greatly contributed to this field by providing valuable resources for gene function prediction, pathway analysis, and comparative genomics. This abstract provides an overview of these tools and databases and highlights their significance in functional annotation. Ensembl is a popular bioinformatics tool that offers comprehensive genome annotation and browsing capabilities. …
Published in Research and Reviews : Journal of Computational Biology · Vol. 12, Issue 1, 2023 · pp. 31–35 Read article
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The Basics of Genomics, Proteogenomics: The Demands and Roles that Proteomics can Fulfil in Genome Annotation
Abstract: The science of genomes: scientists have only recently gone from analysing a few or a limited number of genes at a time to investigating thousands of genes, moving from the study of inheritance units to the investigation of an organism's entire genome. The science of genomes, or "genomics," was originally focused to determining DNA sequences (the nucleotide order on a specific fragment of DNA), but it quickly evolved to a …
Published in Research and Reviews : Journal of Computational Biology · Vol. 10, Issue 3, 2021 · pp. 10–12 Read article
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Physics-Informed Generative and Tensor-Based Framework for DNA Sequence Simulation and Genomic Structure Discovery
Abstract: In this paper, we explore the intersection of artificial intelligence (AI) and mathematical physics to propose advanced methods for DNA sequence generation and analysis. Specifically, we investigate how physics-informed Generative Adversarial Networks (GANs) and tensor network representations can be harnessed to restructure DNA for applications in genetic science. The proposed methodology offers a unique integration of concepts of thermodynamic modeling with innovative GAN architecture in order to allow the creation …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 13, Issue 2, 2026 Read article
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Revolutionizing Vaccine Development:The Transformative Role of Bioinformatics in Designing Next-Generation Immunotherapies
Abstract: Vaccines have long been central to the prevention and control of infectious diseases, dramatically reducing morbidity and mortality worldwide. In the modern era, the integration of bioinformatics has revolutionized vaccine development by enabling rapid, precise, and cost-effective identification of potential vaccine targets. This seminar explores the multifaceted applications of bioinformatics in vaccinology, including antigen discovery, epitope prediction, structural modeling, molecular docking, and immunoinformatics-driven vaccine design. Special emphasis is placed on …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 12, Issue 3, 2025 · pp. 19–33 Read article
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Glitches in the Implementation of Bioinformatics in Medical Settings: A Comprehensive Review
Abstract: Bioinformatics is a dynamic field at the intersection of biology, computer science, and information technology, offering new possibilities in medicine by enabling a deeper understanding of genomics, molecular biology, and personalized treatment approaches. Its integration into healthcare could greatly enhance diagnostics, enable tailored treatments for individuals, and facilitate the analysis of large biological datasets. However, despite its potential, several barriers impede its successful implementation in clinical settings. These include technical …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 11, Issue 3, 2024 · pp. 1–6 Read article
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Artificial Intelligence in Diagnostics: Advancements, Challenges, and Future Prospects
Abstract: AI is changing (and will change) healthcare as we know it, and diagnostics might be the specialty that feels the most discomfort. Artificial intelligence-based analytical systems are facilitating the detection, diagnosis, and treatment of a variety of diseases, with better accuracy, speed, and results. Now, this abstract investigates the role of AI in diagnostics, scouring its elements, landmark techniques, transformative impact and future overview. This article explains AI and discusses …
Published in Research and Reviews: A Journal of Health Professions · Vol. 15, Issue 3, 2025 · pp. 8–17 Read article
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Bioinformatics and Medicine: Bringing Data to the Bedside
Abstract: From being an empirical and experience-based practice, modern medicine has transformed into a codified and research-based discipline, known as Evidence-Based Medicine (EBM). Though EBM has greatly enhanced the quality of medical practice through population-scale clinical trials, it still has limitations in managing biologically diverse patient populations, especially when dealing with clinical outliers who respond in an unusual way to standard treatments. With the rapid progress in genomics, proteomics, and high-throughput …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 2, 2026 · pp. 41–46 Read article
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Genomic Characterization of Emerging Arboviruses in Rural India
Abstract: Arboviruses (arthropod-borne viruses) represent a rapidly evolving group of pathogens responsible for significant morbidity and mortality, particularly in tropical and subtropical regions. Rural India, characterized by dense vector populations, changing ecological patterns, and limited healthcare infrastructure, has become a hotspot for the emergence and re-emergence of arboviral diseases such as dengue, chikungunya, Japanese encephalitis, and more recently, Zika virus infections. Advances in genomic technologies, including next-generation sequencing (NGS), metagenomics, and …
Published in International Journal of Pathogens · Vol. 3, Issue 2, 2026 · pp. 1–8 Read article
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Tools and Techniques for Whole-Genome Alignments
Abstract: The prediction of evolutionary relationships between two or more genomes at the nucleotide level is known as whole-genome alignment (WGA). It possesses characteristics of both gene orthology prediction and collinear sequence alignment. WGAs are useful for genome-wide analyses like phylogenetic inference, genome annotation, and function prediction. So many solutions have been developed despite the fact that this problem is difficult. This article provides an overview of the approaches used to …
Published in Research and Reviews : Journal of Computational Biology · Vol. 11, Issue 2, 2022 · pp. 36–38 Read article
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Artificial Intelligence in Early Diagnosis and Personalized Treatment of Alzheimer’s Disease
Abstract: Artificial intelligence (AI) has become a disruptive technology in the medical care industry, with potential solutions to early diagnosis and customized treatment of Alzheimer’s disease (AD), a progressive neurodegenerative disease and the most prevalent cause of dementia globally. Conventional diagnostic techniques, such as cognitive, neuroimaging and biomarker techniques, are usually limited in the ability to detect disease at its most susceptible stage when treatment interventions are most effective. The recent …
Published in International Journal of Brain Sciences · Vol. 3, Issue 2, 2026 · pp. 15–27 Read article
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Computational Approaches to Understanding Cellular Signaling Pathways
Abstract: Cellular signaling pathways are fundamental in regulating vital processes, such as cell growth, differentiation, and apoptosis. The intricate and interconnected nature of these signaling networks requires sophisticated methods for their analysis. Computational approaches, including mathematical modeling, network analysis, and machine learning, have revolutionized the way researchers analyze and simulate cellular signaling. This article provides a comprehensive overview of computational strategies employed to model signaling pathways, with a focus on integrating …
Published in International Journal of Cell Biology and Cellular Functions · Vol. 2, Issue 2, 2024 · pp. 8–13 Read article
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Beyond Generalized Treatment: The Future is Personalized Medicine
Abstract: Personalized medicine, which is also called as precision medicine, is reestablishing the view of new healthcare by replacing the traditional "one-size-fits-all" approach with individual therapies made according to a patients unique genomic organization, environment, and lifestyle. This article talks about the scientific foundation, important applications, ethical challenges, and the significant potential of personalized medicine in enhancing the clinical outcomes. From oncogenic and genetic disorders to chronic diseases, the addition of …
Published in Emerging Trends in Personalized Medicines · Vol. 2, Issue 2, 2025 Read article
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Machine Learning Approaches in Breast Cancer Diagnosis: Current Trends and Future Perspectives
Abstract: Since cancer is still one of the world's top causes of death, precise and effective detection techniques must be developed. Machine learning (ML) approaches have shown promise in recent years for enhancing cancer prognosis and detection. This paper presents a comprehensive review of the application of ML in cancer detection, focusing on various modalities including medical imaging, genomic data, and clinical records. We highlight the challenges associated with traditional cancer …
Published in International Journal of Radio Frequency Innovations · Vol. 2, Issue 1, 2024 · pp. 14–20 Read article
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Parallel Greedy Approach for Phylogenetic Tree Construction in the Context of Marine Species
Abstract: The rebuilding of phylogenetic trees for marine species shows major computing problems because of the massive genomic data and the huge biodiversity inherent in ocean ecosystems. Traditional phylogenetic methods are accurate but become more expensive when they are processing with thousands of marine taxa parallelly. This article shows a critical analysis of parallel greedy algorithms as an adaptable solution for large-scale marine phylogenetics. It examines the main principles of greedy …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 4, Issue 1, 2026 · pp. 33–45 Read article
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Role of Artificial Intelligence in Simulation and Therapeutics in Neurodegenerative Diseases
Abstract: Neurodegenerative diseases, such as Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, etc., are a cause of significant mortality rates due to a lack of curative treatments and their complex nature. Traditional therapeutic methodologies have several disadvantages such as slow diagnosis and a lack of effective treatments. They mainly focused on the management of the disease rather than curing it. The integration of artificial intelligence in the simulation and therapeutics of neurodegenerative …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 16, Issue 1, 2026 · pp. 19–29 Read article
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Statistical Models for Predicting Genetic Variability and Disease Susceptibility
Abstract: Differences in genetics are key to understanding why some individuals are more prone to certain diseases than others. Recent advancements in genomic research, combined with statistical modeling techniques, have made significant strides in predicting disease risk based on genetic factors. This review explores the application of statistical models for predicting genetic variability and their role in disease susceptibility. We discuss traditional methods like linear regression and genome-wide association studies (GWAS), …
Published in Research and Reviews : Journal of Computational Biology · Vol. 14, Issue 1, 2025 · pp. 30–34 Read article
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Advances in Biological Systems Modeling for Predicting Drug Effects in Chronic Disease
Abstract: Biological systems modeling has emerged as a promising tool for understanding and predicting the effects of drugs in the treatment of chronic diseases. Chronic diseases, such as diabetes, cardiovascular diseases, and neurodegenerative disorders pose significant challenges to traditional drug development due to their complex, multifactorial nature. Systems biology approaches, which integrate computational modeling with experimental data, provide a holistic view of disease mechanisms and treatment responses. This review explores recent …
Published in Research and Reviews : Journal of Computational Biology · Vol. 14, Issue 1, 2025 · pp. 17–22 Read article
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Epidemiology and transmission of infectious diseases study using Machine learning
Abstract: Infectious diseases remain a formidable global health challenge, characterized by rapid evolution and complex transmission dynamics that often outpace traditional epidemiological surveillance and response mechanisms. This study investigates the transformative potential of machine learning (ML) methodologies to enhance our understanding and prediction of infectious disease epidemiology and transmission. Leveraging diverse datasets—including clinical records, genomic sequences, environmental factors, social mobility data, and real-time digital footprints—we studies and presented various ML models …
Published in International Journal of Pathogens · Vol. 2, Issue 2, 2025 Read article
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Explainable Artificial Intelligence in Personalized Medicine: Emerging Clinical Perspectives
Abstract: The convergence of artificial intelligence (AI) and precision medicine has transformed contemporary healthcare by enabling data-driven clinical decision-making, individualized therapeutic interventions, and predictive diagnostics. However, despite remarkable advances in machine learning (ML) and deep learning (DL), the widespread adoption of AI in healthcare remains constrained by the “black-box” nature of many computational systems. Clinicians, regulatory agencies, and patients increasingly demand transparency, interpretability, and trustworthiness in AI-guided medical recommendations. Explainable Artificial …
Published in Emerging Trends in Personalized Medicines · Vol. 3, Issue 2, 2026 · pp. 13–29 Read article