Qualitative Research Examples in Life Sciences: Case Studies and Insights

Qualitative research in the life sciences examines experiences, perceptions, behaviors, and social processes that quantitative measurements alone cannot capture. This article presents concrete examples of qualitative studies across biomedical, clinical, veterinary, and translational research settings, with attention to methods, design choices, and practical applications. The examples draw from peer-reviewed literature and illustrate how researchers use interviews, thematic analysis, grounded theory, ethnography, and qualitative synthesis to answer questions about lived experience, clinical implementation, and intervention development.

Scope and Reader Context

Researchers, students, and life-science professionals increasingly encounter qualitative methods in journals, grant applications, and collaborative projects. Understanding what qualitative research looks like in practice helps readers evaluate published studies, design their own investigations, and integrate qualitative evidence with quantitative findings. The examples in this article span human genetics, child mental health, nursing practice, cardiology, veterinary medicine, and systems biology. Each example includes the research question, the qualitative method used, and the practical contribution of the findings.

At a Glance: Qualitative Study Examples in Life Sciences

Core Principles of Qualitative Research in Life Sciences

Qualitative research addresses questions about meaning, experience, and context that quantitative designs cannot answer. The approach is particularly valuable when researchers need to understand how patients, clinicians, or other stakeholders perceive a condition, treatment, or service. Qualitative methods also generate hypotheses and theoretical frameworks that subsequent quantitative studies can test.

Reliability, Validity, and Sampling

Qualitative research has its own standards for quality that differ from quantitative criteria. The American Journal of Medical Quality clarifies that qualitative researchers must attend to reliability, validity, sampling, and generalizability in ways appropriate to their methods. Reliability in qualitative research concerns the consistency and trustworthiness of the analytic process. Validity relates to whether the interpretations accurately reflect participant perspectives. Sampling in qualitative studies typically aims for depth instead of statistical representativeness, often using purposive strategies to select participants who can provide rich information about the phenomenon of interest. Generalizability in qualitative research is typically conceptual instead of statistical, meaning findings transfer to other contexts through thick description and theoretical insight.

Major Theoretical Frameworks

Three major theoretical frameworks guide qualitative data collection and analysis in medical science. Ethnography involves prolonged engagement with a cultural group to understand their practices and beliefs from an insider perspective. Existential phenomenology focuses on the lived experience of a phenomenon, seeking to describe how individuals experience health, illness, or treatment. Grounded theory aims to develop theory from data through systematic comparison of cases and iterative coding. Each framework has distinct steps for data collection and analysis, and each has contributed to improving medical care through real-life applications.

The Role of Qualitative Research in Translational Science

Qualitative methods have been relatively absent from translational science in child mental health and neurodevelopmental conditions. An editorial in the Journal of Child Psychology and Psychiatry argues for pragmatic integration of qualitative and quantitative methods to accelerate development of effective interventions. Qualitative methods contribute at specific points across the discovery, development, and implementation phases of the translational cycle. For example, qualitative interviews can reveal patient priorities that shape intervention design, while qualitative process evaluations can explain why an intervention succeeded or failed in real-world settings.

Practical Workflow for Conducting Qualitative Research

Researchers planning qualitative studies in life sciences should follow a structured workflow that addresses design, data collection, analysis, and reporting.

Step 1: Define the Research Question

Qualitative research questions typically begin with how, what, or why. They explore processes, experiences, or meanings instead of testing predetermined hypotheses. For example, a researcher might ask how adults with a metabolic condition experience dietary management or what barriers clinicians perceive to implementing a new genetic test.

Step 2: Select an Appropriate Design

The research question determines the design. Phenomenology suits questions about lived experience. Grounded theory suits questions about social processes and theory development. Ethnography suits questions about cultural practices. Qualitative synthesis designs, including scoping reviews, meta-ethnography, and rapid realist reviews, suit questions about the collective meaning of existing qualitative evidence.

Step 3: Plan Sampling and Recruitment

Purposive sampling selects participants who can provide relevant information. Convenience sampling recruits readily available participants. Snowball sampling asks initial participants to refer others. The cardiology pharmacogenomics study used all three strategies to recruit 15 cardiologists from public and private institutions in the United Arab Emirates.

Step 4: Collect Data

Common data collection methods include semi-structured interviews, in-depth interviews, focus groups, participant observation, and free-text questionnaires. Interviews are typically audio-recorded and transcribed verbatim for analysis. The phenylketonuria study conducted in-depth qualitative interviews with eight New Zealand adults, transcribed the interviews, and analyzed them using grounded theory.

Step 5: Analyze Data

Thematic analysis identifies patterns across the data. Grounded theory uses constant comparative methods to develop categories and theoretical relationships. Content analysis quantifies the presence of certain words or concepts within qualitative data. The dog pain study used qualitative text analysis of free-text entries to extract the wording owners used to describe pain-related behavioral changes.

Step 6: Report Findings

Reporting standards for qualitative research exist through the EQUATOR Network, which provides guidelines and resources for transparent reporting of health research. Following reporting guidelines helps readers assess the quality and transferability of qualitative findings.

Case Study: Lived Experience of Phenylketonuria

The New Zealand Medical Journal published a grounded theory study exploring the lived experience of phenylketonuria (PKU) in adults. PKU is a genetic metabolic condition requiring strict dietary management to prevent neurological damage. The researchers conducted in-depth qualitative interviews with eight New Zealand adults with early-treated PKU.

Research Question and Design

The study asked how adults with PKU experience living with their condition and how that experience relates to treatment adherence. The researchers chose grounded theory because it allows themes to emerge from participant accounts instead of imposing predetermined categories.

Key Findings

Several consistent themes emerged from the analysis. Participants described chronic uncertainty operating on multiple levels, including uncertainty about metabolic control, long-term health outcomes, and the consequences of dietary deviations. Maintaining interpersonal relationships posed challenges because the PKU diet affected social eating, family dynamics, and romantic relationships. Participants also described a basic incompatibility between the PKU diet and many lifestyle demands, including work schedules, travel, and social events.

Practical Implications

The findings suggested that medical practitioners could better tailor services for adults with PKU by facilitating self-management, conveying realistic expectations of metabolic control, and increasing the volume of information directed to PKU adults. This example illustrates how qualitative research generates actionable recommendations for clinical practice.

Case Study: Dog Owners' Recognition of Acute Pain Signs

A study published in PLOS ONE investigated behavioral changes indicative of acute pain in dogs that owners recognize in a home setting. The research addressed a practical gap in veterinary medicine: owners often struggle to recognize pain in their pets after discharge from clinical care.

Research Design and Methods

The study enrolled owners of 51 dogs treated at the Small Animal Clinic of Utrecht University. Owners documented and video recorded observed behavioral changes and estimated their dog's pain level within the first week after clinical discharge. Three veterinarians independently analyzed the owner-recorded videos to provide a professional reference perspective. Free-text entries from participants were analyzed qualitatively, and the wording used by owners whose dogs were deemed in pain according to the veterinary video evaluation was extracted.

Key Findings

The most frequently reported behavioral changes on the day of discharge and the two days thereafter were changes in walking (72.1 percent), playing with an object (70.5 percent), and playing with the owner (68.9 percent). The empirical data indicated decreases in playing behavior, explorative behavior, and eating. Logistic regression analysis showed significant associations between owner-estimated pain scores and behavioral changes for these tested items. Notably, no correlation was found between pain estimation by veterinarians and dog owners.

Contribution

The qualitative text analysis provided insight into the wording owners used to describe pain-related behavioral changes. This wording can inform the development of an owner-directed acute pain scoring instrument. Such an instrument is needed to help owners recognize and correctly interpret pain-induced behavior in their dogs.

Case Study: Cardiologists' Perspectives on Pharmacogenomics Implementation

A qualitative study from the United Arab Emirates examined cardiologists' perspectives on implementing pharmacogenomics in a hybrid public-private health system. Pharmacogenomics can optimize cardiovascular therapy, yet routine integration in cardiology remains limited.

Research Design and Methods

The study used an abductive analytical approach with semi-structured interviews of 15 cardiologists from public and private institutions. Participants were recruited through purposive, convenience, and snowball sampling. Interviews were transcribed verbatim and thematically analyzed in NVivo software. The Consolidated Framework for Implementation Research guided the analysis across intervention characteristics, outer setting, inner setting, individual characteristics, and process.

Key Findings

Clinicians expressed strong conceptual support for pharmacogenomics, especially in higher-risk scenarios, but reported limited hands-on exposure and confidence. Barriers included perceived test complexity, cost, and lack of reimbursement, insufficient laboratory capacity and electronic health record integration, unclear workflows and role ownership, and turnaround times misaligned with acute care. Outer-setting constraints included ambiguous policy signals and payer criteria. Inner-setting variability in resources, leadership engagement, and communication pathways further limited uptake.

Facilitators and Implications

Reported facilitators included multidisciplinary service models with input from pharmacists and genetics specialists, targeted case-based training, and initial deployment in non-acute contexts. These findings illustrate how qualitative research identifies implementation barriers and facilitators that quantitative studies might miss.

Case Study: Nurses' Experiences Using Artificial Intelligence in Clinical Practice

A systematic review published in 2026 synthesized registered nurses' experiences of using artificial intelligence tools in clinical practice. The review addressed a gap in the literature, as most syntheses focused on nurses' attitudes or readiness instead of experiences after direct use.

Research Design and Methods

The systematic literature review searched CINAHL, Embase, MEDLINE, PsycINFO, and PubMed databases. Two reviewers independently screened titles, abstracts, and full texts and appraised included studies using the Mixed Methods Appraisal Tool. Thematic synthesis used a primarily deductive framework based on the Technology Acceptance Model 2, with additional facilitating conditions from the unified theory of acceptance and use of technology.

Key Findings

Twenty studies met the inclusion criteria. Perceived usefulness and facilitating conditions were most frequently reported. Nurses described artificial intelligence as supporting decision-making, workflow efficiency, and confidence when implementation included adequate training, interoperability, and technical infrastructure. Ease of use was closely tied to interface design and training. Job relevance and output quality were rated more positively when nurses described artificial intelligence as aligning with nursing tasks and producing interpretable, reliable outputs.

Barriers

Common barriers included usability issues, limited integration into workflows and electronic systems, privacy and trust concerns, and inconsistent or poorly contextualized outputs. This example demonstrates how qualitative evidence synthesis can inform implementation decisions across multiple studies.

Qualitative Data in Computational Biology

Qualitative data also appear in systems biology, where measurements of cellular regulatory systems are often qualitative. For example, measurements of signaling readouts in the presence and absence of mutations may reveal a rank ordering of responses across conditions but not the precise extents of mutation-induced differences.

The Problem of Ignoring Qualitative Data

Qualitative data are often ignored by mathematical modelers or considered in an ad hoc manner. An earlier study focused on the roles of MEK isoforms in ERK activation tuned model parameter values manually to obtain consistency with a combination of qualitative and quantitative data. This approach was not reproducible and did not provide insights into parametric or prediction uncertainties.

A Systematic Approach

A study using PyBioNetFit generated formalized statements of qualitative observations, making these observations more reusable, and improved the model parameterization procedure by applying a systematic and automated approach. The software enabled qualitative data to be leveraged together with quantitative data in parameterization of systems biology models and facilitated uncertainty quantification. These capabilities are important for reliable estimation of model parameters and model analyses in studies of cellular regulatory systems and reproducibility.

Qualitative Evidence Synthesis Designs

Qualitative synthesis combines rigorous processes and authorial judgment to present the collective meaning of research outputs. The European Journal of General Practice describes three exemplary designs for qualitative synthesis in primary care research.

Scoping Reviews

Scoping reviews aim to provide an overview of the evidence or knowledge available or to answer questions regarding the nature and diversity of the evidence. They are useful for mapping a research area, identifying gaps, and clarifying concepts.

Meta-Ethnography

Meta-ethnographies intend to systematically compare data from primary qualitative studies to identify and develop new overarching concepts, theories, and models. This approach moves beyond summarizing findings to generate new interpretive insights.

Rapid Realist Reviews

Rapid realist reviews aim to provide knowledge synthesis by looking at complex questions while responding to time-sensitive and emerging issues. They address the question of what works, for whom, in what circumstances, and how. This design is particularly useful for policy and practice decisions that cannot wait for lengthy review processes.

Preregistration of Qualitative Research

Preregistration, an initiative to enhance research credibility, has been applied primarily to quantitative research. A paper in Accountability in Research investigates whether the preregistration format could also boost the credibility of qualitative research.

The Postdiction Distinction

A crucial distinction underlying preregistration is between prediction and postdiction. In qualitative research, data are used to decide which way interpretation should move forward, using data to generate hypotheses and new research questions. Qualitative research is thus a real-life example of postdiction research.

Objections and Rebuttals

Some may object to preregistering qualitative studies because qualitative research generally does not test hypotheses and because qualitative research design is typically flexible and subjective. The authors rebut these objections by arguing that making hypotheses explicit is just one feature of preregistration, that flexibility can be tracked using preregistration, and that preregistration would provide a check on subjectivity.

Practical Benefits

Preregistering qualitative studies is practically useful for combating dissemination bias and could incentivize qualitative researchers to report constantly on their study's development. The paper concludes with suggested modifications to the Open Science Framework preregistration form to tailor it for qualitative studies.

Behavior Change Technique Taxonomy Development

A suite of five studies published in Health Technology Assessment developed and evaluated a taxonomic method for reporting and describing behavior change interventions. The research involved consensus methods, randomized controlled trials, and analysis of qualitative data.

The Need for a Shared Language

Meeting global health challenges requires effective behavior change interventions, which depend on accurate intervention reporting. Current reporting often lacks detail, preventing accurate replication and implementation. Recent developments have specified intervention content into behavior change techniques, the active ingredients such as goal-setting and self-monitoring of behavior.

Methods and Participants

Four hundred participants from 12 countries engaged in investigating, designing, or delivering behavior change interventions. The participants included systematic reviewers, researchers, practitioners, and policy-makers. The research aimed to develop a method of specifying intervention content in terms of component behavior change techniques, lay a foundation for a comprehensive methodology applicable to different types of complex interventions, develop resources to support application of the taxonomy, and achieve multidisciplinary and international acceptance.

Contribution

The resulting taxonomy provides an internationally shared language for specifying and developing interventions. This technology can be used for synthesizing evidence, implementing effective interventions, and testing theory.

Common Failure Patterns in Qualitative Research

Researchers new to qualitative methods often encounter predictable problems. Recognizing these patterns helps in designing stronger studies and evaluating published work.

Confusing Qualitative Methods with Anecdote

Qualitative research is sometimes dismissed as anecdotal because it uses small samples and non-numeric data. Rigorous qualitative studies use systematic sampling, transparent analytic procedures, and audit trails to ensure trustworthiness. The confirmability audit provides a check on subjectivity by documenting the research process.

Inadequate Description of Methods

Poor reporting prevents readers from assessing the quality of qualitative research. The EQUATOR Network provides reporting guidelines that help authors describe their methods transparently. Researchers should report their sampling strategy, data collection procedures, analytic approach, and steps taken to ensure rigor.

Overgeneralizing Findings

Qualitative findings are context-bound and transferable through thick description instead of statistically generalizable. Researchers should avoid claiming that findings apply to populations beyond those studied. The phenylketonuria study, for example, involved eight New Zealand adults, and its findings should be interpreted in that context.

Ignoring Negative Cases

Qualitative analysis should account for data that do not fit emerging themes. Attending to negative cases strengthens the credibility of interpretations and prevents premature closure.

Failing to Integrate Qualitative and Quantitative Data

Qualitative and quantitative methods answer different questions. Studies that use both should clearly articulate how the methods complement each other. The dog pain study combined quantitative logistic regression with qualitative text analysis to develop a more complete understanding of pain recognition.

Records and Measurements in Qualitative Research

Qualitative research generates records that differ from quantitative datasets but require equally careful management.

Types of Records

Common records include interview transcripts, audio and video recordings, field notes, free-text questionnaire responses, and analytic memos. The dog pain study used owner-recorded videos analyzed independently by three veterinarians, creating a professional reference perspective against which owner observations could be compared.

Documentation Practices

Researchers should document their sampling decisions, data collection procedures, and analytic choices. The cardiology pharmacogenomics study documented recruitment through purposive, convenience, and snowball sampling and used NVivo software for thematic analysis. Transparent documentation supports the confirmability audit and enables readers to assess the trustworthiness of findings.

Data Management

Qualitative data require attention to confidentiality, particularly when participants discuss sensitive health information. Researchers should use pseudonyms, remove identifying details from transcripts, and store data securely. Institutional policies and jurisdictional requirements govern data retention and sharing.

Quality and Welfare Controls

Qualitative research in life sciences must attend to participant welfare and research quality.

Participant Welfare

Studies involving patients, clinicians, or other human participants require ethical approval and informed consent. Researchers should consider the emotional impact of interviews, particularly when topics involve illness, disability, or difficult experiences. The phenylketonuria study explored sensitive aspects of living with a chronic condition and required careful attention to participant comfort.

Research Quality

Quality in qualitative research involves credibility, transferability, dependability, and confirmability. Credibility refers to the confidence in the truth of the findings. Transferability concerns the extent to which findings apply to other contexts. Dependability refers to the consistency of the research process. Confirmability concerns the degree to which findings are shaped by participants instead of researcher bias.

Reporting Standards

The EQUATOR Network provides access to reporting guidelines for qualitative research. Following these guidelines improves the transparency and usefulness of qualitative studies.

Limitations of Qualitative Research

Qualitative research has inherent limitations that researchers and readers should acknowledge.

Sample Size and Generalizability

Qualitative studies typically use small samples selected for information richness instead of statistical representativeness. Findings may not generalize to broader populations. The phenylketonuria study involved eight participants, and the cardiology study involved 15 cardiologists from one country.

Researcher Subjectivity

Qualitative analysis involves interpretation, and researcher perspectives can shape findings. Strategies to address subjectivity include reflexivity, member checking, and confirmability audits. Preregistration can provide a check on subjectivity by making analytic plans explicit.

Time and Resource Intensity

Qualitative research is labor-intensive. Interviews must be transcribed, coded, and analyzed. The behavior change taxonomy development involved 400 participants from 12 countries and multiple study phases.

Limited Quantification

Qualitative research does not provide effect sizes or statistical significance. It answers questions about meaning and process but cannot establish causal relationships or quantify prevalence.

Safety and Regulatory Context

Qualitative research in life sciences operates within regulatory and ethical frameworks that vary by jurisdiction.

Ethical Approval

Studies involving human participants require review by institutional review boards or research ethics committees. Researchers must obtain informed consent, protect confidentiality, and minimize harm.

Data Protection

Qualitative data often contain sensitive personal information. Researchers must comply with applicable data protection regulations, which vary by jurisdiction. The nurses' artificial intelligence systematic review appraised included studies using the Mixed Methods Appraisal Tool, reflecting attention to methodological quality.

Professional Escalation Criteria

Researchers who encounter participant distress during interviews should have protocols for referral to appropriate support services. Clinicians conducting qualitative research with patients should be prepared to escalate concerns about participant safety or welfare.

Professional Escalation Criteria for Research Teams

Research teams should establish clear criteria for escalating concerns during qualitative studies.

Participant Distress

If a participant becomes distressed during an interview, the researcher should pause the interview, offer support, and consider whether to continue. Researchers should have referral pathways to mental health services or other appropriate support.

Safety Concerns

If a participant discloses risk of harm to self or others, researchers must follow applicable protocols for reporting and referral. These protocols vary by jurisdiction and professional standards.

Data Quality Concerns

If data quality is inadequate, researchers should document the issue and consider whether additional data collection is needed. The dog pain study addressed data quality by having three veterinarians independently analyze owner-recorded videos.

Ethical Concerns

If researchers identify ethical concerns during data collection or analysis, they should consult their institutional review board or research ethics committee.

Practical Implementation Steps for Researchers

Researchers planning qualitative studies in life sciences should follow these practical steps.

Step 1: Clarify the Research Question

Articulate the question in terms of experience, process, or meaning. Avoid questions that require quantitative answers.

Step 2: Select the Design

Choose a design that fits the question. Consider phenomenology for lived experience, grounded theory for theory development, ethnography for cultural practices, and qualitative synthesis for existing evidence.

Step 3: Plan Sampling

Use purposive sampling to select participants who can provide rich information. Document sampling decisions and their rationale.

Step 4: Develop Data Collection Instruments

Create interview guides or observation protocols. Pilot test instruments with a small number of participants.

Step 5: Collect and Manage Data

Record interviews, transcribe verbatim, and manage data securely. Document the data collection process.

Step 6: Analyze Systematically

Use a recognized analytic approach such as thematic analysis, grounded theory, or content analysis. Document coding decisions and maintain an audit trail.

Step 7: Report Transparently

Follow reporting guidelines from the EQUATOR Network. Describe methods in sufficient detail for readers to assess quality.

Step 8: Consider Preregistration

Consider preregistering the study to enhance credibility and combat dissemination bias. Adapt existing preregistration forms for qualitative designs.

Frequently Asked Questions

What is the difference between qualitative and quantitative research in life sciences?

Quantitative research measures variables and tests hypotheses using statistical analysis. Qualitative research explores experiences, perceptions, and social processes using non-numeric data such as interviews, observations, and text. The phenylketonuria study used qualitative interviews to understand lived experience, while the dog pain study combined quantitative logistic regression with qualitative text analysis.

What are the main qualitative research methods used in life sciences?

Common methods include in-depth interviews, semi-structured interviews, focus groups, participant observation, and qualitative analysis of free-text responses. Major theoretical frameworks include ethnography, existential phenomenology, and grounded theory. The cardiology pharmacogenomics study used semi-structured interviews with thematic analysis, while the phenylketonuria study used in-depth interviews with grounded theory.

How do researchers ensure quality in qualitative research?

Researchers attend to credibility, transferability, dependability, and confirmability. Strategies include systematic sampling, transparent analytic procedures, audit trails, and confirmability audits. Preregistration can provide a check on subjectivity. Reporting guidelines from the EQUATOR Network help ensure transparent reporting.

Can qualitative research be preregistered?

Yes. A paper in Accountability in Research argues that preregistration can boost the credibility of qualitative research. While qualitative research generally does not test hypotheses, preregistration can make hypotheses explicit, track flexibility, and provide a check on subjectivity. The paper suggests modifications to the Open Science Framework preregistration form for qualitative studies.

How is qualitative data used in computational biology?

Qualitative data in systems biology include rank orderings of responses across conditions. A study using PyBioNetFit generated formalized statements of qualitative observations and applied a systematic, automated approach to model parameterization. This enabled qualitative data to be leveraged together with quantitative data and facilitated uncertainty quantification.

What is qualitative evidence synthesis?

Qualitative evidence synthesis pools findings from qualitative studies to present collective meaning. Three exemplary designs are scoping reviews, meta-ethnography, and rapid realist reviews. Scoping reviews provide an overview of evidence, meta-ethnographies compare data to develop new concepts and theories, and rapid realist reviews address what works, for whom, in what circumstances, and how.

What are the limitations of qualitative research?

Qualitative research uses small samples that are not statistically representative, involves researcher interpretation, and is time-intensive. Findings are context-bound and transferable through thick description instead of statistical generalization. Qualitative research does not provide effect sizes or establish causal relationships.

How do qualitative methods contribute to translational science?

Qualitative methods contribute at specific points across the discovery, development, and implementation phases of the translational cycle. An editorial in the Journal of Child Psychology and Psychiatry argues for pragmatic integration of qualitative and quantitative methods to speed up development of effective interventions. Qualitative research can reveal patient priorities, explain intervention success or failure, and identify implementation barriers and facilitators.

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References and Further Reading

• Research Data Framework. National Institute of Standards and Technology.
• EQUATOR Network. EQUATOR Network.
• Experimental Design Assistant. NC3Rs.
• NCBI Literature Resources. National Center for Biotechnology Information.
• PubMed. National Library of Medicine.
• Preregistering qualitative research.. Accountability in research, 2019.
• Analysis and interpretation of microplate-based oxygen consumption and pH data.. Methods in enzymology, 2014.
• Behaviour change techniques: the development and evaluation of a taxonomic method for reporting and describing behaviour change interventions (a suite of five studies involving consensus methods, randomised controlled trials and analysis of qualitative data).. Health technology assessment (Winchester, England), 2015.
• The Quality of Qualitative Research().. American journal of medical quality : the official journal of the American College of Medical Quality, 2019.
• Mechanisms of Mendelian dominance.. Clinical genetics, 2018.
• Phenylketonuria--the lived experience.. The New Zealand medical journal, 2007.
• Editorial: Qualitative contributions to translational science - Practical pointers towards methodological pluralism in child psychology and psychiatry.. Journal of child psychology and psychiatry, and allied disciplines, 2024.
• Series: Practical guidance to qualitative research. Part 7: Qualitative evidence synthesis for emerging themes in primary care research: Scoping review, meta-ethnography and rapid realist review.. The European journal of general practice, 2023.
• Using PyBioNetFit to leverage qualitative and quantitative data in biological model parameterization and uncertainty quantification.. 2026.
• A computational case study of Günter Blobel's idea of protein topogenesis and its influence.. 2026.
• Acute pain sign recognition by dog owners in a home setting.. 2026.
• Nurses' Experiences Using AI in Clinical Practice: Systematic Review.. 2026.
• Cardiologists' perspectives on pharmacogenomics implementation in a hybrid health system: A qualitative study from the United Arab Emirates.. 2026.
• Linguistic diversity in machine learning training data: Content analysis of English and Finnish soundscape descriptions in audio captioning.. 2026.
• The Pre-service Teachers’ Perceptions of Integrated Teaching, Inquiry Learning, using ICT and Real-life Examples in Science Classes. Science Education International, 2024.

This article is educational and does not replace institutional policy, professional advice, or applicable safety and regulatory requirements.