The American Journal of Bioinformatics is an open-access, peer-reviewed journal dedicated to publishing high-quality research in the field of bioinformatics. It serves as a platform for scholars and professionals to share innovative methodologies, applications, and case studies that advance the understanding and practice of bioinformatics.
All articles published in the American Journal of Bioinformatics (AJB) are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). Under this license, authors retain full copyright of their work and grant permission to anyone to: Share — copy and redistribute the material in any medium or format Adapt — remix, transform, and build upon the material for any purpose, even commercially Conditions: Attribution: Appropriate credit must be given to the original author(s) and the source, a link to the license must be provided, and any changes made must be indicated. No additional restrictions: You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. This open-access license ensures that scholarly work published in AJB is freely accessible and usable, promoting knowledge dissemination and academic collaboration worldw
Natural Language Processing with AI for Unstructured Claims Data
Despite decades of investment in electronic claims processing, a substantial proportion of healthcare insurance claims still depend on unstructured clinical artifacts such as physician progress notes, discharge summaries, operative reports, pathology narratives, and scanned medical records. These free-text sources often contain the most critical evidence for medical necessity, diagnosis–procedure alignment, and compliance with coverage policies, yet they remain poorly exploited by traditional rule-based adjudication systems that are optimized for structured codes andtabular data. This paper examines the role of artificial intelligence driven Natural Language Processing (NLP) in transforming unstructured claims data into actionable signals for automated and semi-automated adjudication. We analyze how contemporary NLP models ranging from clinical named entity recognition and medical concept normalization to transformer-based contextual embeddings enable the extraction of diagnoses, procedures, temporal events, and provider intent from heterogeneous clinical narratives. Particular emphasis is placed on challenges unique to the medical domain, including terminological ambiguity, negation, context sensitivity, clinical abbreviations, and cross-document inference. The study further explores how NLP-derived features can be reconciled with structured claims standards such as X12 EDI andHL7 FHIR, enabling hybrid adjudication pipelines that combine narrative intelligence with coded data. By positioning NLP as a semantic bridge between clinical documentation and claims infrastructure, this work highlights its potential to reduce manual review rates, accelerate decision timelines, and improve adjudication accuracy while preserving regulatory compliance and auditability.
Copyright (c) 2026 Sanjay Bandare (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Real Time Adjudication vs. Batch Processing: A Comparative Analysis of Claims Processing Speed, Accuracy, and Cost
The need for effective, accurate, and financially viable claims processing methods has increased due to the growing administrative burden and cost pressures within the US healthcare system. Real-time adjudication (RTA) and batch processing are two of the most prevalent paradigms. They are essentially distinct operational models that have a big impact on payer performance, provider cash flow, and patient happiness. With an emphasis on three crucial performance dimensions—processing speed, adjudication accuracy, and operational cost efficiency—this study offers a thorough comparison of RTA versus batch processing in the context of U.S. healthcare claims management. Leveraging empirical data from commercial payers, Medicare Advantage plans, and large provider networks, the study evaluates transaction throughput, error rates, rework cycles, and cost-per-claim metrics across both models. The analysis demonstrates that real-time adjudication significantly reduces claim turnaround time—from an industry average of 7–14 days in batch environments to sub-second or near-instantaneous decisions—while simultaneously lowering administrative overhead and denial rates through rule-based automation and AI-assisted validation. Conversely, batch processing, although cost-effective for high-volume standardized claims, exhibits higher latency, increased manual intervention, and greater susceptibility to cumulative errors and reprocessing costs. The findings underscore that while batch processing remains viable for legacy systems and low-complexity claim sets, real-time adjudication offers superior value in dynamic, value-based care environments by enhancing revenue cycle predictability, minimizing provider-payer friction, and improving patient financial transparency. The study concludes by proposing a hybrid architectural framework tailored to the U.S. healthcare ecosystem, integrating real-time decision engines with scalable batch infrastructures to optimize performance, compliance, and cost containment..
Bioinformatics Approaches to Personalized Health and Wellness
Personalized health and wellness is an emerging field that uses individual biological data to tailor healthcare strategies and lifestyle interventions. Bioinformatics plays a central role in this area by enabling the analysis of genomic, transcriptomic, proteomic, and metabolomic data to understand individual health profiles. This article reviews the bioinformatics approaches used in personalized health and wellness, focusing on precision medicine, genetic testing, biomarker discovery, and lifestyle optimization. We discuss how these tools are applied to optimize health outcomes, prevent disease, and personalize wellness strategies based on an individual's unique genetic makeup and lifestyle.
Copyright (c) 2025 James Carter (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Harnessing Bioinformatics for Translational Medicine
Translational medicine bridges the gap between laboratory research and patient care by translating scientific discoveries into clinical applications. Bioinformatics plays a critical role in this process by enabling the analysis and interpretation of complex biological data to improve diagnosis, treatment, and patient outcomes. This article explores how bioinformatics is being harnessed for translational medicine, with a focus on genomic data analysis, personalized medicine, biomarker discovery, and clinical trial design. We discuss the challenges and future directions in integrating bioinformatics tools into clinical practice to accelerate the translation of research into effective therapies.
Copyright (c) 2025 Emma Clark (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Bioinformatics and the Search for Novel Antibiotics
The increasing prevalence of antibiotic-resistant bacteria presents a significant global health challenge. Bioinformatics has become a powerful tool in the search for novel antibiotics by enabling the identification of potential drug targets, screening of large compound libraries, and analysis of microbial genomes. This article explores how bioinformatics approaches, including computational drug discovery, genomic sequencing, and machine learning, are being used to identify new antibiotics. We discuss the potential of these technologies to overcome the challenges posed by antibiotic resistance and highlight the role of bioinformatics in accelerating the drug discovery process
Copyright (c) 2025 Austra Lian (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Comparative Metabolomics: A Bioinformatics Perspective
Metabolomics is a rapidly growing field that involves the comprehensive study of small molecule metabolites in biological systems. Comparative metabolomics, which compares metabolite profiles across different conditions, species, or time points, is essential for understanding metabolic changes in health and disease. Bioinformatics tools play a key role in the analysis, integration, and interpretation of complex metabolomics data. This article reviews bioinformatics approaches in comparative metabolomics, focusing on data preprocessing, statistical analysis, pathway analysis, and the integration of metabolomics with other omics data. We also discuss the challenges in analyzing metabolomics data and the future directions for bioinformatics in advancing metabolomics research.
Copyright (c) 2025 Robert Black (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Bioinformatics Tools for the Study of Functional Genomics
Functional genomics is a field of genomics that aims to understand the role of genes and their interactions in cellular processes and disease. Bioinformatics tools are essential for the analysis and interpretation of high-throughput functional genomics data, including RNA sequencing (RNA-Seq), chromatin immunoprecipitation sequencing (ChIP-Seq), and gene expression profiling. This article reviews bioinformatics tools commonly used in functional genomics research, focusing on tools for data preprocessing, analysis, and visualization. We also explore how these tools are applied to study gene regulation, transcriptional networks, and the molecular mechanisms underlying diseases.
Copyright (c) 2025 Charles Lee (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Ethical Considerations in Bioinformatics andGenomic Research
As genomic technologies and bioinformatics approaches continue to advance, ethical considerations have become an increasingly important aspect of research. The collection, analysis, and interpretation of genomic data raise significant ethical questions regarding privacy, consent, and the potential for misuse of genetic information. This article explores the ethical challenges in bioinformatics and genomic research, focusing on issues such as data privacy, informed consent, genetic discrimination, and the impact of genetic data on vulnerable populations. We also discuss the role of regulatory frameworks and ethical guidelines in ensuring that genomic research is conducted responsibly and for the benefit of society.
Copyright (c) 2025 James Brown (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Implications of Survival Epidemiology for the Field of Bioinformatics
Building on the definition advanced by Raphael Cuomo, father of survival epidemiology, this paper analyzes how a postdiagnosis population science reframes core bioinformatics tasks. Survival epidemiology treats diagnosis as a causal threshold that changes the data generating process. Inclusion now conditions on disease. Treatment and supportive care reshape biology. Competing risks intensify. Exposure associations estimated for incidence cannot be assumed to apply to survival among patients (Cuomo, 2025). For bioinformatics, that thesis has immediate operational consequences because computational pipelines increasingly define cohorts, baselines, and features for prognosis and for real world effectiveness studies. When a pipeline defines baseline using information collected after therapy begins, or restricts to patients who survive long enough to receive a biomarker assay, the resulting model can appear accurate while encoding time misalignment and immortal time or label leakage that distort clinical meaning (Suissa, 2008). Survival epidemiology therefore pushes bioinformatics toward event based longitudinal data models that make clinical decision points explicit and that represent the postdiagnosis course in a way that can be audited, reproduced, and linked to the estimand of interest (Hernán and Robins, 2016). It also favors modeling frameworks that reflect postdiagnosis realities. Competing event methods help avoid conflating disease specific outcomes with other causes of death (Fine and Gray, 1999). Multistate representations help distinguish progression from mortality and from other clinically meaningful transitions (Putter et al., 2007). Joint models become important when biomarkers both predict and influence treatment decisions (Rizopoulos, 2012). Finally, it argues that computational reporting and deployment should distinguish prevention estimands from postdiagnosis survival estimands so that algorithms and clinical messages do not inadvertently export prevention narratives into settings where they may be inappropriate for people already diagnosed (Cuomo, 2025).
Copyright (c) 2025 Beatrice S. Pendelton , Eleanor J. Vance, Thomas O. Croft, M.D (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Bioinformatics in Understanding the Genetic Basis of Complex Diseases
Complex diseases such as cancer, diabetes, and cardiovascular diseases are influenced by multiple genetic, environmental, and lifestyle factors. Bioinformatics plays a key role in unraveling the genetic basis of these diseases by providing tools to analyze large-scale genomic, transcriptomic, and clinical data. This article explores the use of bioinformatics techniques in identifying genetic variants associated with complex diseases, including genome-wide association studies (GWAS), next-generation sequencing (NGS), and integrative multi-omics approaches. We discuss the challenges faced in understanding the genetic basis of complex diseases and highlight future directions for bioinformatics in advancing personalized medicine.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
Computational Biology Approaches to Studying Neurogenetics
Neurogenetics is a rapidly growing field that investigates the genetic factors underlying neurological and psychiatric disorders. Computational biology approaches have revolutionized neurogenetic research by providing tools to analyze large-scale genomic, transcriptomic, and proteomic data. These approaches help identify genetic variations, gene expression patterns, and molecular networks that contribute to diseases such as Alzheimer's, Parkinson's, and autism. This article explores computational biology techniques, including next-generation sequencing (NGS), genome-wide association studies (GWAS), and machine learning, in the study of neurogenetics. We discuss the integration of multi-omics data, challenges in data interpretation, and the potential of computational biology to advance our understanding of neurogenetic disorders.
Development of Bioinformatics Software for Drug Discovery and Design
Bioinformatics software has become an essential tool in drug discovery and design, enabling researchers to analyze and predict molecular interactions, optimize drug candidates, and reduce the time and cost of drug development. The integration of bioinformatics techniques such as molecular docking, virtual screening, and quantitative structure-activity relationship (QSAR) modeling has revolutionized the process of drug design. This article explores the development of bioinformatics software tools used in drug discovery, focusing on their applications in target identification, drug screening, and lead optimization. We also discuss the challenges faced in developing these tools and the future directions in bioinformatics-driven drug design.
Copyright (c) 2025 Dr. David Thompson (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Bioinformatics and the Emerging Role of Artificial Intelligence in Genomic Research
The combination of bioinformatics and artificial intelligence (AI) is revolutionizing genomic research by enabling the analysis of complex and large-scale biological data. AI techniques, including machine learning and deep learning, are increasingly being applied to interpret genomic data, identify patterns, and predict outcomes that were previously challenging to achieve. This article explores the emerging role of AI in genomic research, with a focus on applications such as gene expression analysis, variant calling, and drug discovery. We also discuss the challenges and future directions of integrating AI into bioinformatics for advancing personalized medicine and precision genomics.
The Use of Bioinformatics in the Identification of Novel Therapeutic Targets
The identification of novel therapeutic targets is a crucial step in the development of new treatments for a wide range of diseases. Bioinformatics has become a powerful tool in the discovery and validation of potential drug targets by analyzing vast amounts of genomic, transcriptomic, and proteomic data. This article discusses the role of bioinformatics in the identification of therapeutic targets, including the use of omics data analysis, functional annotation, network-based approaches, and predictive modeling. We also explore the challenges and future directions in this field and highlight the importance of bioinformatics in advancing personalized medicine
The identification of novel therapeutic targets is a crucial step in the development of new treatments for a wide range of diseases. Bioinformatics has become a powerful tool in the discovery and validation of potential drug targets by analyzing vast amounts of genomic, transcriptomic, and proteomic data. This article discusses the role of bioinformatics in the identification of therapeutic targets, including the use of omics data analysis, functional annotation, network-based approaches, and predictive modeling. We also explore the challenges and future directions in this field and highlight the importance of bioinformatics in advancing personalized medicine.
Applications of Bioinformatics in Clinical Genomics
Clinical genomics has emerged as a pivotal field in modern medicine, enabling the understanding of genetic variations in relation to disease development, diagnosis, and treatment. Bioinformatics has played a central role in analyzing and interpreting large-scale genomic data in clinical applications. This article discusses the applications of bioinformatics in clinical genomics, including variant calling, clinical genome interpretation, genetic counseling, and precision medicine. We also explore the challenges associated with clinical genomics and the future directions in which bioinformatics is transforming personalized healthcare.
Bioinformatics in the Study of Host-Pathogen Interactions
Host-pathogen interactions are fundamental to understanding the mechanisms of infection, immune response, and disease progression. Bioinformatics has become an essential tool in studying these interactions by analyzing large-scale genomic, transcriptomic, and proteomic data. This article reviews the bioinformatics methods used to study host-pathogen interactions, focusing on data integration, functional annotation, and predictive modeling. We explore the applications of bioinformatics in identifying key host and pathogen factors involved in infection, as well as the challenges and future directions in this area of research.
Data Mining Approaches for Identifying Genetic Markers in Cancer
Cancer is a complex disease driven by genetic mutations that lead to uncontrolled cell growth. Identifying genetic markers associated with cancer can aid in early diagnosis, prognosis, and the development of personalized treatments. Data mining techniques have become a valuable tool in identifying these markers from large-scale genomic datasets. This article explores various data mining approaches, including supervised and unsupervised learning, feature selection, and clustering methods, used to identify genetic markers in cancer. We also discuss the challenges in cancer genetic marker discovery and the potential applications of these markers in cancer treatment and personalized medicine.
The Role of Bioinformatics in Advancing Immunotherapy Research
Immunotherapy has emerged as a revolutionary treatment approach for cancer, harnessing the body's immune system to target and eliminate tumor cells. The development of effective immunotherapies requires a deep understanding of the immune system and the complex interactions between immune cells and cancer cells. Bioinformatics has become an essential tool in advancing immunotherapy research by providing computational methods to analyze large-scale omics data and model immune responses. This article reviews the role of bioinformatics in immunotherapy research, with a focus on the identification of biomarkers, immune system modeling, and the design of personalized immunotherapies. We also discuss the challenges and future directions in the integration of bioinformatics into immunotherapy development.
Bioinformatics Tools for the Functional Annotation of Genomes
Functional annotation of genomes involves the identification and interpretation of genes and other functional elements within a genome. With the advent of high-throughput sequencing technologies, the volume of genomic data has exploded, necessitating the development of bioinformatics tools that can efficiently and accurately annotate genomes. This article reviews the bioinformatics tools and methods used for genome annotation, focusing on functional annotation, gene prediction, and the identification of regulatory elements. We also discuss challenges in the field, such as the annotation of non-coding regions and the integration of multi-omics data to enhance genome annotation.
Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-nd/4.0
Machine Learning in the Prediction of Epigenetic Modifications
Epigenetic modifications, such as DNA methylation and histone modification, play a crucial role in gene regulation and are associated with various biological processes, including development, aging, and disease. The prediction of epigenetic modifications from genomic data is a challenging task, as these modifications are context-dependent and influenced by genetic, environmental, and stochastic factors. Machine learning (ML) techniques have emerged as powerful tools for predicting epigenetic modifications by learning patterns from large-scale omics data. This article reviews the use of machine learning methods in predicting epigenetic modifications, focusing on feature selection, data integration, and ML algorithms. We also discuss the potential applications, challenges, and future directions for integrating machine learning into epigenetics research..
Big Data and Cloud Computing in Bioinformatics Research
The integration of big data and cloud computing has revolutionized bioinformatics research, enabling the analysis and interpretation of large-scale biological data. Bioinformatics generates vast amounts of data from sequencing technologies, imaging, and experimental assays, which require advanced computational methods and infrastructure to process. Cloud computing provides scalable resources and computing power to support bioinformatics workflows, offering flexibility in data storage, processing, and analysis. This article explores the role of big data and cloud computing in bioinformatics research, with a focus on their applications in genomics, proteomics, systems biology, and precision medicine. We discuss the advantages, challenges, and future directions of big data and cloud computing in bioinformatics.
Computational Analysis of Viral Genomes: Applications in Epidemiology
The rapid evolution and genetic diversity of viral genomes pose significant challenges in understanding and controlling viral diseases. Computational analysis of viral genomes provides powerful tools to study viral evolution, transmission dynamics, and epidemiology. This article explores the role of computational approaches in analyzing viral genomes, focusing on the applications of bioinformatics tools in epidemiological studies. We discuss the methods used to analyze viral genome sequences, track viral mutations, identify potential sources of outbreaks, and predict viral evolution. Additionally, we explore the challenges, opportunities, and future directions in the computational analysis of viral genomes for advancing public health responses
Development of Hypersonic Aircraft for High-Speed Travel and Space Access
The rapid evolution and genetic diversity of viral genomes pose significant challenges in understanding and controlling viral diseases. Computational analysis of viral genomes provides powerful tools to study viral evolution, transmission dynamics, and epidemiology. This article explores the role of computational approaches in analyzing viral genomes, focusing on the applications of bioinformatics tools in epidemiological studies. We discuss the methods used to analyze viral genome sequences, track viral mutations, identify potential sources of outbreaks, and predict viral evolution. Additionally, we explore the challenges, opportunities, and future directions in the computational analysis of viral genomes for advancing public health responses.
Next-Generation Bioinformatics Approaches for Rare Variant Analysis
Rare variants play a crucial role in the genetic underpinnings of many diseases, yet their identification and interpretation remain challenging due to their low frequency in populations. Next-generation sequencing (NGS) technologies have revolutionized genetic research by enabling the detection of rare variants across the genome at unprecedented depths. Bioinformatics approaches are essential for analyzing the vast amounts of data generated by NGS platforms to identify, annotate, and interpret rare genetic variants. This article reviews the latest bioinformatics tools and methodologies for rare variant analysis, discusses their applications in human genetics and disease research, and explores the challenges and future directions in this field.
Bioinformatics in the Study of Microbial Communities in the Human Gut
The human gut harbors a diverse community of microorganisms that play critical roles in digestion, immunity, and overall health. Advancements in sequencing technologies have enabled researchers to analyze the gut microbiome in unprecedented detail, uncovering the complex relationships between microbial communities and host health. Bioinformatics plays a crucial role in this research by providing tools to process, analyze, and interpret large-scale microbiome data. This article explores the role of bioinformatics in the study of microbial communities in the human gut, focusing on microbiome sequencing, data analysis methods, and the identification of microbial signatures associated with health and disease.
Bioinformatics Approaches in Understanding Human Genetic Diversity
Human genetic diversity is a key factor in understanding population history, disease susceptibility, and response to treatment. Bioinformatics approaches have revolutionized the study of human genetic diversity by enabling the analysis of large-scale genomic data across different populations. This article discusses the bioinformatics tools and methods used to study human genetic diversity, including whole-genome sequencing, population genomics, and computational methods for identifying genetic variations. We also explore the role of bioinformatics in understanding the genetic basis of diseases and how this knowledge can inform personalized medicine.
The Role of Artificial Intelligence in Personalized Nursing Care for Chronic Disease Management at Home
This paper examines how artificial intelligence (AI) transforms individualized nursing care delivery for chronically ill patients who get treatment in their homes. Medical care delivered at home becomes increasingly essential because chronic diseases continue to grow more prevalent. The analysis evaluates the potential of AI-enhanced tools to create individualized care programs that boost patient interactions and generate superior medical outcomes for diabetic patients while handling cases of heart failure and COPD individuals. The research examines how AI functions within remote patient care systems and predictive analysis-based exacerbation detection and personalized medication handling and AI-generated nursing virtual assistants for patient teaching assistance while pursuing enhanced clinic workflow operations for home healthcare personnel. An evaluation will be performed on how AI technology delivers individualized nursing care that is both responsive and proactive in nature. The system enables AI algorithms to evaluate extensive patient-generated data which enables pattern detection to generate real-time risk predictions and customized care programs. The study investigates how AI enables patient empowerment through personalized feedback systems which improve patient self-care practices. The assessment evaluates the effectiveness of AI-based strategies for lowering readmission cases while enhancing lifestyle qualityfor hospital patients. The research investigates the beneficial and challenging aspects of AI implementation for chronic disease home care management through proposal of responsible implementation strategies.