Genomic Data vs Genetic Data: Understanding the Differences and Applications

Genetic data and genomic data are related but distinct categories of biological information. Genetic data typically refers to information about specific genes, variants, or chromosomal regions that are known to be associated with particular traits or conditions. Genomic data encompasses the complete or near-complete DNA sequence of an organism, including all genes, noncoding regions, and structural features. The distinction matters for study design, data storage, analysis methods, and clinical interpretation. Researchers and clinicians must understand which type of data answers which question, because the choice affects cost, computational requirements, and the kinds of conclusions that can be drawn.

Defining Genetic Data and Genomic Data

Genetic data focuses on individual genes or specific variants. A genetic test might examine a single gene for known mutations, such as testing for variants in a gene associated with a hereditary condition. Genetic data can also include information about chromosomes, such as karyotype results that reveal large structural abnormalities. The scope is narrow and targeted. The interpretation relies on established knowledge about the clinical significance of specific variants.

Genomic data refers to the entire genetic material of an organism. Whole genome sequencing produces a complete DNA sequence. Whole exome sequencing targets the protein-coding regions of the genome, which represent a small fraction of the total DNA but contain most known disease-causing variants. Transcriptomic data, such as RNA sequencing results, captures gene expression across the entire transcriptome. Epigenomic data describes chemical modifications to DNA and chromatin that affect gene activity without changing the underlying sequence.

The distinction between genetic and genomic data is not always sharp. A targeted gene panel that sequences 50 genes produces data that is genetic in scope but genomic in method. A whole genome sequence contains genetic information about every individual gene. The practical difference lies in the breadth of data collected and the analytical approach required.

Core Principles of Genetic Data

Genetic data answers focused questions about specific variants. A clinician might order genetic testing for a patient with symptoms suggestive of a particular condition. The test examines known disease-associated genes and reports whether pathogenic variants are present. The result is typically binary or categorical: a variant is present or absent, pathogenic or benign.

The interpretation of genetic data depends on variant classification. Variants are classified based on population frequency, predicted effect on protein function, segregation with disease in families, and functional studies. The American College of Medical Genetics and Genomics has established a framework for variant classification that is widely used in clinical laboratories. Variants are categorized as pathogenic, likely pathogenic, uncertain significance, likely benign, or benign.

Genetic data has limitations. It does not capture the full complexity of gene regulation, gene-gene interactions, or environmental influences. A person may carry a pathogenic variant but never develop the associated condition due to incomplete penetrance. Conversely, the absence of known pathogenic variants does not rule out a genetic cause, because the causative variant may be in a gene that was not tested or may be a type of variant that the test cannot detect.

Core Principles of Genomic Data

Genomic data provides a comprehensive view of an organism's genetic material. Whole genome sequencing reveals the complete DNA sequence, including coding regions, introns, regulatory elements, and intergenic regions. This breadth enables discovery of novel variants, analysis of structural variation, and investigation of noncoding regions that regulate gene expression.

Genomic data is inherently large and complex. A single human genome contains approximately three billion base pairs. Storing, processing, and analyzing this volume of data requires substantial computational infrastructure. The NCBI Data Resources provide access to large-scale genomic datasets, reference sequences, and analysis tools that researchers use to interpret genomic information.

Genomic approaches generate multiple data types. Whole genome sequencing provides DNA sequence information. RNA sequencing measures gene expression levels across the transcriptome. DNA methylation arrays and chromatin immunoprecipitation sequencing characterize epigenetic modifications. Each data type captures a different layer of biological information, and integrating these layers can reveal regulatory mechanisms that are invisible in sequence data alone.

The EMBL-EBI Training resources offer instruction on handling and analyzing genomic data, including practical guidance on data formats, quality control, and bioinformatics workflows. These resources are valuable for researchers who need to develop computational skills for genomic analysis.

At a Glance: Genetic Data vs Genomic Data

Choosing Between Genetic and Genomic Approaches

The choice between genetic and genomic data depends on the question being asked, the clinical context, and available resources. A targeted genetic test is appropriate when a specific condition is suspected and the associated gene or genes are known. For example, testing for variants in a single gene that causes a well-characterized hereditary syndrome is faster, cheaper, and easier to interpret than whole genome sequencing.

Genomic approaches are appropriate when the genetic basis is unknown, when multiple genes may be involved, or when a comprehensive assessment is needed. Whole exome sequencing is often used in the diagnosis of rare diseases when standard genetic testing has not identified a cause. Whole genome sequencing provides even broader coverage, including noncoding regions that may contain disease-causing variants.

Population genomic screening represents an emerging application. A study of 50,063 individuals who underwent genomic screening for actionable hereditary disorders found that 8.6% carried pathogenic or likely pathogenic variants conferring monogenic risk. The study also found that relevant health care utilization was higher in individuals with positive results, with a small but significant increase in median health care costs post-test compared with pre-test in participants with positive results. These findings suggest that genomic screening can identify at-risk individuals and prompt intervention without substantially increasing health care costs. See Genetic findings and health care utilization among individuals undergoing population genomic screening for actionable hereditary disorders for the full study.

Data Generation Methods

Targeted Genetic Testing

Targeted genetic testing examines specific genes or variants. Sanger sequencing remains the standard method for confirming variants in single genes. Targeted gene panels use next-generation sequencing to analyze multiple genes simultaneously. These panels are designed around specific clinical indications, such as hereditary cancer syndromes or cardiomyopathy.

Targeted panels balance breadth and cost. A panel of 50 to 100 genes costs less than whole exome sequencing and produces data that is easier to interpret because all genes on the panel have known disease associations. However, panels can miss variants in genes that are not included, and they cannot discover novel disease genes.

Whole Exome Sequencing

Whole exome sequencing captures the protein-coding regions of the genome. Although exons represent only about 1% to 2% of the human genome, they contain approximately 85% of known disease-causing variants. Exome sequencing is widely used in clinical diagnostics for rare diseases and in research to identify novel disease genes.

Exome sequencing requires enrichment of coding regions before sequencing. The captured regions are sequenced at high depth to ensure reliable variant detection. Analysis involves aligning reads to the reference genome, calling variants, and filtering against population databases to identify candidate disease-causing variants.

Whole Genome Sequencing

Whole genome sequencing provides the most complete view of an organism's genetic material. It captures coding and noncoding regions, including regulatory elements, introns, and structural variants. Whole genome sequencing can detect variants that exome sequencing misses, such as deep intronic variants that affect splicing or structural rearrangements.

The cost of whole genome sequencing has decreased substantially, making it feasible for clinical and research applications. However, the data volume and analysis complexity remain significant. Whole genome data requires substantial storage and computational resources, and interpretation of variants in noncoding regions remains challenging because the functional consequences are often unknown.

Transcriptomic and Epigenomic Methods

RNA sequencing measures gene expression across the transcriptome. This approach captures the functional state of cells, revealing which genes are active and at what levels. Transcriptomic data can identify genes that are differentially expressed between conditions, discover novel transcripts, and characterize splicing patterns.

RNA sequencing has diagnostic utility beyond expression analysis. A study of 60 individuals with RNU4ATAC-opathy, a condition caused by variants in a noncoding gene, demonstrated that RNA sequencing enabled reclassification of variants of uncertain significance as likely pathogenic in 6 individuals. All individuals who underwent RNA sequencing showed a consistent pattern of minor intron retention. The study highlighted that variants in noncoding genes are often overlooked in analysis because of their noncoding nature, and laboratories should ensure such genes are appropriately assessed by their analysis pipelines. See RNU4ATAC-opathy: Clinical, molecular, and transcriptomic insights from a large cohort for details.

Epigenomic methods characterize chemical modifications to DNA and chromatin. DNA methylation arrays measure methylation across the genome. Chromatin immunoprecipitation sequencing identifies regions bound by specific proteins. These methods reveal regulatory mechanisms that influence gene expression without changing the DNA sequence.

Data Analysis Workflows

Quality Control

Quality control is the first step in any genomic analysis workflow. Raw sequencing data must be assessed for read quality, adapter contamination, and sequencing depth. Low-quality reads can produce false variant calls, and inadequate depth can cause true variants to be missed.

For RNA sequencing data, quality control includes assessment of mapping rates, gene body coverage, and detection of batch effects. A comparative transcriptomic analysis of intestinal tissues from Penaeus vannamei shrimp challenged with Vibrio parahaemolyticus used KEGG enrichment analysis to identify the ABC transporter pathway as markedly enriched among upregulated genes. The study selected three genes for RNAi assays based on expression profiles and domain characteristics, and silencing one of these genes, PvABCF2, significantly increased mortality, tissue damage, and Vibrio load in challenged shrimp. See Integrated RNA-seq and RNAi analyses reveal that ABCF2 is involved in defense against Vibrio parahaemolyticus in Penaeus vannamei for the full findings.

Alignment and Variant Calling

Sequence reads must be aligned to a reference genome before variants can be identified. The choice of reference genome affects downstream analysis, and different aligners have different strengths in handling mismatches, indels, and structural variants.

Variant calling identifies positions where the sequenced sample differs from the reference. Germline variant calling identifies variants present in all cells, while somatic variant calling identifies variants present in a subset of cells, such as tumor cells. Somatic variant calling requires careful filtering to distinguish true variants from sequencing artifacts.

Expression Quantification and Differential Analysis

RNA sequencing analysis quantifies gene expression levels and identifies genes that differ between conditions. Reads are mapped to a reference transcriptome, and expression levels are estimated for each gene. Differential expression analysis compares expression between groups and identifies statistically significant changes.

The choice of analysis method affects results. A comparison of clustering methods for time course genomic data found that different methods performed best under different conditions. Functional clustering models performed well when gene curves were short and sparse, while dynamic time warping and weighted gene co-expression network analysis performed well when gene curves were medium or long. Weighted gene co-expression network analysis and model-based clustering were the best methods when performance and computation time were both considered. See Comparison of Clustering Methods for Time Course Genomic Data: Applications to Aging Effects for the method comparison.

Integration of Multiple Data Types

Integrating genomic, transcriptomic, and epigenomic data can reveal biological mechanisms that are invisible in any single data type. A study of desmoid tumors performed integrated genomic, transcriptomic, and DNA methylation profiling on 76 tumors. Beyond canonical CTNNB1 and APC mutations, the study discovered frequent mutations in chromatin-remodeling genes, notably KMT2C, found in over 50% of samples. Unsupervised transcriptomic analysis revealed two molecular subtypes: immune-myogenic and mesenchymal-like. The immune-myogenic subtype exhibited high expression of myogenic markers, immune checkpoint genes, tertiary lymphoid structure signatures, and global enhancer hypomethylation linked to interferon and myogenesis pathways. See Profiling of desmoid tumors reveals frequent mutations in chromatin-remodeling genes and identifies an Immune-myogenic subtype for the full study.

Data Management and Sharing

Storage and Infrastructure

Genomic data requires substantial storage capacity. A single whole genome sequence produces approximately 100 gigabytes of raw data, and processed data adds additional storage requirements. Research institutions and clinical laboratories must maintain secure storage systems with backup and disaster recovery capabilities.

Data compression reduces storage requirements but adds computational overhead. Lossless compression preserves all information, while lossy compression can reduce file sizes at the cost of some accuracy. The choice depends on the intended use of the data and regulatory requirements.

Data Sharing Policies

Genomic data sharing enables replication of findings, secondary analysis, and aggregation across studies. The NIH Genomic Data Sharing Policy establishes expectations for sharing genomic data generated with NIH funding. The policy requires researchers to deposit data in designated repositories and to share data in a timely manner while protecting participant privacy.

Data sharing raises privacy concerns. Genomic data is uniquely identifying, and re-identification risks persist even after de-identification. A study of genomic professionals in Australia explored perceptions of patient genomic data ownership and the balance between privacy safeguarding and data sharing. See Balancing the safeguarding of privacy and data sharing: perceptions of genomic professionals on patient genomic data ownership in Australia for the professional perspectives.

FAIR Principles

The FAIR Guiding Principles provide a framework for making data findable, accessible, interoperable, and reusable. Published in Scientific Data, the principles emphasize that data should be described with rich metadata, deposited in accessible repositories, formatted using standard vocabularies, and documented with clear provenance. See The FAIR Guiding Principles for the full articulation of these principles.

Applying FAIR principles to genomic data requires attention to file formats, metadata standards, and repository selection. Common genomic data formats include FASTQ for raw reads, BAM for aligned reads, and VCF for variant calls. Metadata should include information about the sample, sequencing platform, analysis pipeline, and quality metrics.

Clinical Applications and Interpretation

Diagnostic Testing

Genetic and genomic testing are used in clinical diagnosis across multiple specialties. Targeted genetic testing confirms suspected hereditary conditions. Genomic testing identifies causes of undiagnosed diseases and guides treatment decisions in oncology.

The integration of genomic data into electronic health records affects genetics care delivery. A study published in Genetics in Medicine examined the impact of integrating genomic data into the electronic health record on genetics care delivery. See Impact of integrating genomic data into the electronic health record on genetics care delivery for the findings.

Cancer Genomics

Cancer genomics uses genomic data to characterize tumors and guide treatment. Comparative genomic hybridization was an early method for detecting genetic alterations in cancer. A study of metastatic renal cell carcinoma used comparative genomic hybridization to detect genetic alterations and correlate them with clinical and histological data. See Genetic alterations in metastatic renal cell carcinoma detected by comparative genomic hybridization: correlation with clinical and histological data for the study.

Modern cancer genomics uses next-generation sequencing to identify somatic mutations, copy number alterations, and structural variants. The data guides selection of targeted therapies, immunotherapies, and clinical trial enrollment. Artificial intelligence methods are increasingly applied to cancer genomic data. A review of 47,586 articles on artificial intelligence applications to genomic data in cancer research found significant growth in this area, while noting ongoing attention is needed to address ethical considerations, interpretability of algorithms, and potential data biases. See Artificial intelligence applications to genomic data in cancer research: a review of recent trends and emerging areas for the review.

Variant Interpretation Challenges

Variant interpretation remains a major challenge in genomic medicine. Many variants identified by genomic sequencing have unknown clinical significance. Classification requires integration of population frequency data, computational predictions, family segregation studies, and functional evidence.

RNA sequencing can aid variant classification. In the RNU4ATAC-opathy cohort, RNA sequencing enabled reclassification of variants of uncertain significance as likely pathogenic in 6 individuals. This demonstrates the value of functional data in resolving ambiguous genetic findings.

Pharmacogenomics

Pharmacogenomic data informs drug selection and dosing. Genetic variants affect drug metabolism, transport, and target response. Testing for these variants can prevent adverse drug reactions and improve treatment efficacy.

Pharmacogenomic testing can be performed using targeted panels or extracted from genomic sequencing data. The choice depends on the clinical context and the turnaround time required. Targeted panels provide rapid results for specific drug-gene pairs, while genomic sequencing provides comprehensive pharmacogenomic information that can be used throughout a patient's lifetime.

Computational Methods and Machine Learning

Statistical Methods for Genomic Data

Genomic data presents statistical challenges due to high dimensionality and complex correlation structures. Traditional regression methods that take vectors as covariates may encounter difficulties in handling tensor data due to ultrahigh dimensionality and complex structure. A sparse regularized Tucker tensor regression model was introduced to exploit the structure of tensor covariates and perform feature selection on tensor data. The model reduced ultrahigh dimensionality to a manageable level using Tucker decomposition of the regression coefficient tensor. Analysis of melanoma genomic data demonstrated that the model achieved better prediction performance and identified markers with important implications. See Sparse regularized low-rank tensor regression with applications in genomic data analysis for the methodological details.

Machine Learning Applications

Machine learning methods are widely applied to genomic data for classification, prediction, and feature selection. Deep learning approaches can identify patterns in complex genomic data that traditional methods miss.

A fuzzy logic system combined with machine learning algorithms and conventional survival analysis, named FuzzyDeepCoxPH, was proposed to identify high-risk missense mutation variants and candidate genes associated with cancer mortality. The system used deep learning-derived abstracted weights and Cox proportional hazards ratios to develop four model-based risk scores. Fuzzy rules integrated these considerations to develop advanced risk estimation. See Applications of Deep Learning and Fuzzy Systems to Detect Cancer Mortality in Next-Generation Genomic Data for the method description.

Computational Mechanism Studies

The computational mechanisms underlying genetic and evolutionary operators are relevant to genomic data applications. An editorial in Frontiers in Genetics addressed computational mechanism of genetic and evolutionary operators and optimizations in genomic data applications. See Editorial: Computational mechanism of genetic/evolutionary operator and optimizations in genomic data applications for the editorial content.

Workforce Competency and Education

Genomics Competency Gaps

The integration of genomics into clinical care requires a workforce with appropriate knowledge and skills. A cross-sectional survey of 502 nursing students and practicing nurses in China evaluated genomics competency using the Chinese version of the Genetics and Genomics Nursing Practice Survey. The overall mean genomics knowledge score was 9.51, with no significant differences across cohorts. However, self-rated understanding showed notable differences among cohorts, and over one-quarter of respondents reported no formal genomics education in their curriculum. The study proposed a tiered educational framework to address stage-specific needs. See From classroom to clinic: analyzing the genomics competency gap across undergraduate students, graduate students, and practicing nurses in China for the survey findings.

Training Resources

Researchers and clinicians need training in genomic data analysis. The EMBL-EBI Training program offers courses and materials on bioinformatics, including topics relevant to genomic data analysis. The NCBI Data Resources provide access to databases, tools, and documentation that support genomic research.

Common Failure Patterns in Genomic Data Analysis

Sample and Data Quality Issues

Poor sample quality produces unreliable data. Degraded DNA or RNA leads to low sequencing depth, increased error rates, and failed assays. Contamination with foreign DNA can produce false variant calls. Sample mix-ups produce results that do not match the intended individual.

Quality metrics should be monitored throughout the analysis pipeline. Sequencing depth, mapping rate, duplication rate, and contamination estimates provide early indicators of problems. Samples that fail quality thresholds should be repeated or excluded from analysis.

Analysis Pipeline Errors

Analysis pipeline errors produce incorrect results even when data quality is adequate. Misalignment of reads can cause false variant calls. Incorrect reference genome versions produce inconsistent results across studies. Parameter choices affect sensitivity and specificity of variant calling.

Reproducibility requires documentation of the analysis pipeline, including software versions, parameters, and reference genome versions. Containerization and workflow management systems help ensure that analyses can be reproduced exactly.

Interpretation Errors

Interpretation errors occur when variants are classified incorrectly. Overinterpretation of variants of uncertain significance can lead to inappropriate clinical action. Underinterpretation can miss clinically significant findings. Both errors have consequences for patient care.

Interpretation should follow established guidelines and incorporate all available evidence. Variant classification should be reviewed by qualified professionals and updated as new evidence emerges.

Records and Documentation

Data Documentation Requirements

Genomic data analysis requires comprehensive documentation. Records should include sample identifiers, collection dates, consent information, laboratory protocols, sequencing platforms, analysis pipelines, and quality metrics. This documentation supports reproducibility, audit, and regulatory compliance.

The NIH Genomic Data Sharing Policy specifies expectations for data documentation and sharing. Researchers should review the policy requirements before initiating studies that generate genomic data.

Clinical Record Integration

Clinical genomic data should be integrated into electronic health records to support patient care. The integration enables clinicians to access genomic information at the point of care and supports longitudinal tracking of genetic findings. However, integration raises challenges related to data storage, interpretation, and updates as knowledge evolves.

Safety and Regulatory Context

Privacy and Confidentiality

Genomic data is sensitive personal information. Privacy protections must address both data security and participant consent. De-identification reduces but does not eliminate re-identification risk, and genomic data can be linked to individuals through comparison with public databases.

Researchers and clinicians must comply with applicable privacy regulations and institutional policies. Data sharing should be conducted through approved mechanisms that protect participant privacy while enabling scientific progress.

Ethical Considerations

Genomic data raises ethical considerations related to consent, return of results, and data ownership. Participants should understand how their data will be used, who will have access, and what findings will be returned. Incidental findings, such as variants associated with conditions unrelated to the primary reason for testing, require policies for disclosure.

The study of genomic professionals in Australia explored perceptions of patient genomic data ownership. See Balancing the safeguarding of privacy and data sharing: perceptions of genomic professionals on patient genomic data ownership in Australia for the professional perspectives on this issue.

Professional Escalation Criteria

When to Seek Specialized Consultation

Clinicians and researchers should seek specialized consultation when genomic data analysis exceeds their expertise. Indications for escalation include:

• Variants of uncertain significance that may affect clinical decisions
• Unexpected or incidental findings that require specialized interpretation
• Cases where genomic results conflict with clinical presentation
• Data quality issues that cannot be resolved with standard procedures
• Questions about data sharing, privacy, or regulatory compliance

Laboratory and Bioinformatics Support

Clinical laboratories and bioinformatics cores provide specialized support for genomic data analysis. These groups have expertise in variant interpretation, quality control, and regulatory compliance. Early consultation can prevent errors and reduce the time required to reach clinically actionable conclusions.

Frequently Asked Questions

What is the main difference between genetic data and genomic data?

Genetic data refers to information about specific genes, variants, or chromosomal regions that are known to be associated with particular traits or conditions. Genomic data encompasses the complete or near-complete genetic material of an organism, including all genes, noncoding regions, and structural features. Genetic data answers focused questions about known variants, while genomic data provides a comprehensive view that enables discovery and broad analysis.

When should a researcher choose genetic testing over genomic sequencing?

Genetic testing is appropriate when a specific condition is suspected and the associated gene or genes are known. It is faster, cheaper, and easier to interpret than genomic sequencing. Genomic sequencing is appropriate when the genetic basis is unknown, when multiple genes may be involved, or when a comprehensive assessment is needed.

Can genomic data be used to answer genetic questions?

Yes, genomic data contains genetic information about every individual gene. A whole genome sequence can be analyzed to determine the status of specific variants in specific genes. However, the analysis requires bioinformatics processing to extract the relevant information, and the cost and complexity of genomic sequencing may not be justified when a targeted genetic test would answer the question.

What are the main challenges in genomic data analysis?

Genomic data analysis faces challenges related to data volume, computational requirements, and interpretation. The data is large and complex, requiring substantial storage and processing infrastructure. Many variants identified by genomic sequencing have unknown clinical significance, and interpretation requires integration of multiple evidence types. Reproducibility requires careful documentation of analysis pipelines and parameters.

How does RNA sequencing differ from DNA sequencing?

DNA sequencing determines the genetic code of an organism, providing information about variants and structural features. RNA sequencing measures gene expression across the transcriptome, revealing which genes are active and at what levels. RNA sequencing can also identify splicing patterns and novel transcripts. The two approaches provide complementary information about the genome and its functional output.

What is the role of data sharing in genomic research?

Data sharing enables replication of findings, secondary analysis, and aggregation across studies. The NIH Genomic Data Sharing Policy establishes expectations for sharing genomic data generated with NIH funding. Data sharing must balance scientific benefits against participant privacy concerns, and the FAIR Guiding Principles provide a framework for making data findable, accessible, interoperable, and reusable.

How are variants of uncertain significance handled in clinical practice?

Variants of uncertain significance are reported to clinicians and patients, but they are not used to guide clinical decisions without additional evidence. Classification may be updated as new evidence emerges from population studies, family segregation analysis, or functional studies. RNA sequencing can aid variant classification by revealing functional consequences of variants, as demonstrated in the RNU4ATAC-opathy cohort where RNA sequencing enabled reclassification of variants of uncertain significance as likely pathogenic.

What training is needed to work with genomic data?

Working with genomic data requires training in bioinformatics, including sequence alignment, variant calling, and statistical analysis. The EMBL-EBI Training program offers courses on these topics, and the NCBI Data Resources provide access to databases and tools. Clinical interpretation of genomic data requires additional training in medical genetics and variant classification.

Related Bioinformatics Guides

• Data Sharing and Privacy in Genomic Research
• Predicting AMR from Genomic Data
• Computational Approaches to Understanding Antimicrobial Resistance (AMR)
• Genomic Selection in Animal Breeding
• Docker and Containerization in Reproducible Research

References and Further Reading

• EMBL-EBI Training. European Bioinformatics Institute.
• NCBI Data Resources. National Center for Biotechnology Information.
• Genomic Data Sharing Policy. National Institutes of Health.
• The FAIR Guiding Principles. Scientific Data.
• Profiling of desmoid tumors reveals frequent mutations in chromatin-remodeling genes and identifies an Immune-myogenic subtype.. 2026.
• Integrated RNA-seq and RNAi analyses reveal that ABCF2 is involved in defense against Vibrio parahaemolyticus in Penaeus vannamei.. 2026.
• RNU4ATAC-opathy: Clinical, molecular, and transcriptomic insights from a large cohort.. 2026.
• Genetic findings and health care utilization among individuals undergoing population genomic screening for actionable hereditary disorders.. 2026.
• From classroom to clinic: analyzing the genomics competency gap across undergraduate students, graduate students, and practicing nurses in China.. 2026.
• Association of LVOT Gradient and LA Strain with Cardiac Events in Pediatric HCM.. 2026.
• Editorial: Computational mechanism of genetic/evolutionary operator and optimizations in genomic data applications. Frontiers in Genetics, 2023.
• Comparison of Clustering Methods for Time Course Genomic Data: Applications to Aging Effects. 2014.
• Artificial intelligence applications to genomic data in cancer research: a review of recent trends and emerging areas. Discover Analytics, 2024.
• Applications of Deep Learning and Fuzzy Systems to Detect Cancer Mortality in Next-Generation Genomic Data. IEEE transactions on fuzzy systems, 2021.
• Sparse regularized low-rank tensor regression with applications in genomic data analysis. Pattern Recognition, 2020.
• Impact of integrating genomic data into the electronic health record on genetics care delivery. Genetics in Medicine, 2022.
• Balancing the safeguarding of privacy and data sharing: perceptions of genomic professionals on patient genomic data ownership in Australia. European Journal of Human Genetics, 2024.
• Genetic alterations in metastatic renal cell carcinoma detected by comparative genomic hybridization: correlation with clinical and histological data.. International Journal of Oncology, 2000.

This article is educational and does not replace validated analysis plans, institutional policy, clinical interpretation, or specialist review.