Tampere University Biomedical Informatics (Biomedical Technology) Master's Thesis Guide: BBT.OPN.002, BBT.MJS.109 and Trepo

Quick answer: which Biomedical Informatics route is this?

This guide is for Biomedical Informatics in Tampere University’s Master’s Programme in Biomedical Technology, leading to Master of Science in Natural Sciences. It is not the separately published Biomedical Sciences and Engineering route leading to Master of Science. The degree is 120 ECTS, while current BBTM.BMI-S14 Advanced Studies in Biomedical Informatics is at least 80 ECTS.

The current Natural Sciences thesis route is unusually clear in the 2026-2027 module tree: BBT.MJS.109 Thesis Seminar, Natural Sciences - 2 ECTS, pass/fail and BBT.OPN.002 Master´s Thesis, Natural Sciences - 30 ECTS, graded 0-5. A separate BBT.OPN.EXT.002 Research Associated with MSc Thesis - 10 ECTS, pass/fail may be used for thesis-associated laboratory, computational or clinical-data research when defined with the supervisor in the initial thesis plan. It is not the thesis itself and is not universally compulsory.

1. The Natural Sciences route is not the Natural Sciences route

Biomedical Informatics exists in two current Tampere master’s contexts. This guide concerns the Biomedical Technology / Master of Science / Natural Sciences route. Its governing advanced-studies object is BBTM.BMI-S14, at least 80 ECTS. Do not replace it with the separate engineering BBTM.TEK-S20 structure, and do not import that route’s Technology-thesis administration.

The subject matter overlaps heavily - bioinformatics, AI, health informatics, biological data, systems biology and clinical information - but the degree title and thesis course objects are distinct. Whenever a shared course page is used as methodological evidence, that does not mean the other degree’s thesis rules apply. For this Natural Sciences route, BBT.MJS.109 remains the seminar anchor even where subject teaching is shared across programmes.

2. The thesis is 30 ECTS; seminar and optional research are separate

BBT.OPN.002 Master´s Thesis, Natural Sciences is 30 ECTS and graded 0-5. It is based on supervised experimental or computational research and culminates in a scientific report. Before starting, a project plan must be accepted by the professor responsible for the MSc specialisation. The course also requires a maturity examination.

BBT.MJS.109 is a separate 2 ECTS pass/fail seminar. It asks students to present both their thesis plans and later their results and discuss background, aims, methods, interpretation, conclusions and future directions. Its current completion option is compulsory in all its parts.

BBT.OPN.EXT.002 is another separate object: 10 ECTS, pass/fail, thesis-associated research. It can consist of laboratory work, computational work or acquisition of clinical data, but it must be defined with the supervisor and linked to the thesis plan. Do not describe this optional research as turning the thesis into a 40 ECTS thesis.

3. Plan the Natural Sciences thesis before touching the final dataset

BBT.OPN.002 explicitly requires an accepted project plan. Use that requirement to freeze the scientific question, data source, experimental unit, primary outcomes, validation logic, ethics/data permissions and analysis plan as early as practical. If BBT.OPN.EXT.002 is used, state exactly what research activity it covers and how it supports - but remains separate from - the 30 ECTS thesis.

Natural Sciences Biomedical Informatics can be experimental or computational. A genomics pipeline, healthcare-data analysis, image/signal analysis, systems-biology study or mixed laboratory-computational project can all fit when the biomedical question and evidence chain are defensible.

4. Start with the biomedical question, not the algorithm

A weak thesis starts with “I want to use deep learning.” A stronger thesis starts with a biomedical or health-system question and asks which computational method can answer it. Define the unit you are trying to predict, describe, classify, discover or support; identify the outcome or biological phenomenon; specify the population or data-generating process; and define the evidence required for the intended claim. BBTM.TEK-S20 explicitly trains students to design systems, algorithms, software, models, methods and processes while understanding the limitations of AI. The algorithm is therefore part of the method, not the scientific objective itself.

5. Build a data-provenance table before analysis

For every dataset, record where it came from, why it was collected, cohort or source system, collection dates where relevant, inclusion and exclusion rules, unit of observation, available outcomes, missingness, prior processing and permission constraints. Biomedical Informatics spans molecular, cellular, patient, population and healthcare-system data. A row in a table can represent a sequencing read, a cell, a visit, a patient, an admission or a hospital. Those units cannot be treated as equivalent merely because they are stored in one dataframe. A provenance table prevents later confusion about what a model actually learned from.

6. Define the experimental unit and grouping structure

Repeated measurements are a major source of false confidence. Thousands of cells can come from one donor; hundreds of image slices can come from one patient; dozens of visits can come from one person; many laboratory measurements can come from one biological sample. Decide which unit is independent for the claim and preserve the hierarchy during analysis. If the question is patient-level prediction, the patient usually needs to be the splitting unit. If the question is donor-level biology, cells within a donor are nested observations rather than independent donors. This decision belongs in Methods before model training.

7. Prevent participant and sample leakage

The same participant or closely linked records must not appear on both sides of a train/test split when the claim is performance on unseen people. Row-wise random splitting can leak repeated visits, images, cells or derived features. Leakage can also occur through preprocessing: normalisation, feature selection, imputation or dimensionality reduction fitted on the entire dataset may expose the test set to the training pipeline. Build splits first where possible, then learn data-dependent preprocessing inside the training folds. State explicitly what entity was grouped during splitting.

8. Separate tuning from final evaluation

A validation set or cross-validation loop helps choose features, model class and hyperparameters. A final test set estimates performance after those choices are fixed. Repeatedly checking the test set and changing the model turns that test set into another tuning set. For small biomedical datasets, nested cross-validation or a comparable separation can be more defensible than a single random split. The key principle is informational separation: the data used to choose the pipeline should not also be the untouched evidence used to claim final generalisation.

9. Baselines are part of the scientific argument

A complex model is only useful if it improves on a meaningful alternative. Include a simple statistical model, clinical score, conventional rule, majority/prevalence baseline or established bioinformatics method when appropriate. If a deep model performs no better than logistic regression after proper validation, that result is scientifically valuable. Explain the trade-off among accuracy, calibration, interpretability, computation and deployment requirements. BBTM.TEK-S20 asks students to understand the possibilities and limitations of advanced methods; a baseline is one of the clearest ways to demonstrate that understanding.

10. Choose metrics before looking for the best result

Metric choice should follow the task. Accuracy can be misleading with imbalance; F1 can hide probability calibration; AUC measures ranking rather than the quality of predicted probabilities; a small p-value does not quantify prediction usefulness. For regression, define the scale and error measure. For survival analysis, define time origin, event and censoring. For a clinical decision-support project, specify the operating threshold and the consequences of false positives and false negatives. Report uncertainty where appropriate, and distinguish statistical improvement from clinically meaningful benefit.

11. Missing data and imputation need a documented pipeline

Do not silently drop or fill missing values. State which variables are missing, how much, why missingness might occur, and whether it relates to the outcome or workflow. Imputation fitted on all data before splitting can leak information. Fit it inside the training process and apply the learned transformation to validation/test data. If complete-case analysis changes the population substantially, describe that selection effect. A sensitivity analysis using reasonable alternative missing-data assumptions can show whether the main conclusion depends on one convenient preprocessing decision.

12. Confounding can masquerade as biomedical signal

Models can learn age, sex, hospital, batch, scanner, laboratory protocol, calendar time, treatment pathway or coding convention instead of the biological mechanism you intended to study. Examine whether these variables differ between outcome groups. When possible, design the split so that site or time differences are tested rather than memorised. Report performance by relevant subgroup if the claim spans heterogeneous populations. High predictive performance does not establish that the top features are causal drivers of disease; predictive association and causal explanation are different scientific goals.

13. External, temporal and site validation answer different questions

Random internal cross-validation asks whether the pipeline generalises to held-out records from roughly the same data-generating environment. A site-separated test asks whether it transfers to another organisation or instrument context. A temporal test asks whether it works on later data after practice, coding or population changes. External validation asks still broader transportability questions. State exactly which evidence you have. Do not describe an internal split as “externally validated,” and do not generalise a single-site retrospective model to all healthcare systems without evidence.

14. BBT.021: sequence and omics data need versioned provenance

Current BBT.021 Bioinformatics covers DNA-seq, RNA-seq, scRNA-seq, epigenetics, proteomics, alignment, annotations, databases, genome browsers and enrichment analysis. In a thesis, record the reference genome/build, annotation/database versions and major tools or pipelines when they affect results. Sequencing and omics types have different measurement processes; avoid treating every matrix as generic tabular data. Document quality control, alignment/quantification or feature construction sufficiently for a knowledgeable reader to understand how raw or source data became the analysed matrix.

15. Multiple testing is not optional in high-throughput work

Current BBT.BI.202 Biological Data Analysis explicitly covers applied hypothesis testing and multiple testing. When thousands of genes, proteins, regions or features are tested, unadjusted p-values produce many expected false positives. State the multiplicity-control approach and what family of tests it applies to. Keep exploratory ranking separate from confirmatory inference. If enrichment follows a selected gene list, document how the list was created and what background universe and database version were used. Otherwise apparently precise biological conclusions can depend on hidden analytical choices.

16. Dimensionality reduction and clustering are exploratory evidence

Heatmaps, PCA-like embeddings and clustering can reveal structure, but a visually separated plot is not proof of a biological class. Scaling, feature selection, batch correction and distance choices can change the geometry. Report those choices. Cluster labels should be validated with independent biological information or robust sensitivity analysis before they are described as real cell types, disease subgroups or molecular mechanisms. If the analysis is exploratory, say so. An examiner is more likely to trust a bounded exploratory conclusion than a confident but unsupported discovery claim.

17. Single-cell studies: cells are nested within biological samples

Tampere’s Biomedical Informatics environment includes single-cell data through BBT.021 and BBT.INT.809 Cell Imaging and Signal Processing. Thousands of cells from one person or one culture do not create thousands of biological replicates. Preserve donor/sample identity through quality control, splitting and statistics. Report filtering, normalisation, dimensionality-reduction and clustering choices. If a classifier is evaluated at cell level, make sure cells from the same donor do not leak across training and test sets when the claim is donor- or patient-level generalisation.

18. Systems biology networks are models, not mechanisms by default

Current BBT.MJS.102 Systems Biology covers graph theory, gene co-expression networks, community detection, functional analysis and biomedical knowledge graphs. A network edge may represent correlation, database relation or another computational rule; it is not automatically a physical interaction. Define nodes, edges, thresholds and data sources. Community detection can generate useful hypotheses, but network structure alone does not prove a causal biological pathway. The Discussion should clearly separate computationally inferred relationships from experimentally established mechanisms.

19. Survival analysis needs time semantics

BBT.BI.202 includes survival analysis. Define the time origin, event of interest, censoring rule, follow-up horizon and whether competing outcomes matter. Check whether predictors are known at the prediction time; using future information creates leakage. If repeated updates are modelled, explain the landmark or time-varying design. A high concordance measure does not automatically mean predicted survival probabilities are calibrated. The thesis should state whether it is ranking risk, estimating absolute risk, explaining associations or supporting a decision.

20. Healthcare-process data are generated by workflows

Current BBT.038 Health Care Processes and Information Systems teaches healthcare processes, governance, records, standards and ICT architectures. Electronic health records are not neutral measurements of biology: they are produced by care workflows, documentation requirements, reimbursement, availability of tests and clinician decisions. Missing tests can mean “not clinically indicated,” not random absence. Diagnosis codes can change over time. A health-informatics thesis should therefore describe the workflow that generated the data and discuss how that workflow constrains interpretation.

21. Interoperability claims need exact standards and versions

Current BBT.MJS.144 covers health-informatics standards and interoperability, including HL7, CDA and IHE. Saying “the system is interoperable” is too broad. Identify the exchange, standard/profile/version, terminology and conformance condition being evaluated. Distinguish syntactic exchange from semantic consistency and from successful workflow integration. A prototype that parses a message format does not automatically interoperate with every healthcare system. Record the actual implementation tested and avoid copying confidential production configuration into the public thesis.

22. Security and privacy are engineering requirements

BBT.MJS.144 explicitly includes security, privacy and data protection. Treat them as design constraints from the start. Define data flows, access roles, storage/processing environment and threat-relevant assumptions at a level appropriate to the thesis. Do not claim a system is “secure” because one control worked or because data were pseudonymised. The thesis can demonstrate selected controls or a risk analysis, but enterprise security and formal compliance require broader evidence. Never expose credentials, patient identifiers or restricted architecture details in appendices or screenshots.

23. Decision support is not autonomous clinical decision-making

Current BBT.HTI.509 Decision Support in Healthcare provides a relevant academic environment for models intended to assist care. Be explicit about the user and decision: clinician, patient, planner, researcher or system administrator. A retrospective model can estimate historical performance but does not automatically establish prospective safety, workflow fit or patient benefit. Define how false positives and negatives matter, whether probabilities are calibrated and what information the user sees. Keep academic decision-support evaluation separate from claims of an authorised autonomous clinical system.

24. Health-software regulation: intended use changes the question

Current BBT.MJS.144 includes regulation relevant to health software. Whether software falls under particular medical-device obligations depends on intended use and context, so avoid generic compliance claims. A master’s thesis can describe applicable considerations, test technical requirements or evaluate interoperability/security. It should not imply formal conformity, certification, CE marking or market authorisation unless that status independently exists and is correctly documented. Separate intended use, technical verification, clinical evaluation, security/privacy evidence and formal regulatory status.

25. Human, clinical and genomic data require governance before analysis

Before accessing identifiable or pseudonymised participant data, patient records, genomic information or other sensitive datasets, establish the applicable ethics, permission and data-protection route. Tampere’s student data-protection guidance requires role and responsibility decisions before processing. A research group possessing data does not automatically authorise every student use. Document the approved data source and secure environment. Minimise variables, avoid unnecessary exports, and make sure notebooks, filenames, screenshots, model artefacts and AI prompts do not leak protected information.

26. Pseudonymised data are still personal data when re-linking is possible

Replacing names with study IDs is valuable but does not automatically make a dataset anonymous. If a key or other information can reconnect the data to people, it remains pseudonymised personal data. Genomic data can also be intrinsically sensitive and potentially identifying. Describe what identifiers were removed, who can access linkage information, and what data may appear in the public thesis. When reporting rare combinations or tiny subgroups, consider whether aggregate results could still reveal individuals.

27. AI use: distinguish research AI from writing assistance

The programme itself studies advanced computational methods including AI, while Tampere also has university-wide rules for AI in studies. Separate AI that is part of the research method from generative AI used to help with writing or coding. For research models, document data, architecture/version, training and evaluation. For generative assistance, follow acknowledgement requirements and verify outputs. Never paste protected clinical, genomic, company or unpublished data into an external AI service without an approved basis. Save the final code and reasoning needed for reproducibility rather than relying on a transient chat.

28. Reproducibility requires more than a Git repository

Record the data version or approved extract, code snapshot/commit, package environment, database/reference versions, random seeds where relevant, and the exact workflow that generates final tables and figures. Manual corrections or relabelling must be logged. Intermediate processed datasets should either be reproducibly regenerated or documented. If computation is nondeterministic, report the variability or stabilisation method. A repository is only useful when another knowledgeable researcher can map its files to the Methods and understand which version produced the submitted results.

29. Write the Methods and data dictionary early

As soon as access is approved and the pipeline is stable, write a data dictionary and Methods skeleton. Define each variable, unit, coding rule, source and allowed missing values. Record the split strategy, preprocessing and planned primary metric before repeatedly inspecting final results. This early writing exposes ambiguous labels, duplicated patients, leakage risks and undocumented transformations while they can still be fixed. Keep a result ledger linking each research question to dataset version, analysis script, figure/table and conclusion.

30. Project plan, supervision and data-access dependencies

The Natural Sciences route starts with a project plan accepted by the professor responsible for the MSc specialisation. Treat that plan as a scientific contract: define the question, datasets or experiments, supervision, milestones, ethics/permissions, access dependencies, computing environment and fallback scope. For clinical, genomic or partner data, confirm access and lawful processing before designing an analysis that depends on them.

If you use BBT.OPN.EXT.002, describe its laboratory, computational or clinical-data research activity in this initial planning and explain its relevance to the thesis. Keep its 10 ECTS pass/fail credit separate from BBT.OPN.002’s 30 ECTS thesis.

31. Results: report the whole evaluation, not the best run

A defensible result section reports predefined primary analyses, meaningful baselines, uncertainty, subgroup or sensitivity analyses where justified, and failed or negative results that change interpretation. Do not present the best seed, fold, threshold, cohort or preprocessing variant as if it were the only analysis performed.

For omics or high-dimensional data, preserve multiplicity control and independent biological units. For clinical prediction, report calibration and operational threshold implications when relevant. For health-information systems, distinguish technical interoperability from demonstrated clinical or organisational benefit.

32. Maturity, Turnitin, Trepo and final audit

BBT.OPN.002 explicitly requires a maturity examination. Follow the current Tampere maturity instructions for the route that applies to your prior language demonstration. Before examination, complete the originality process required by Tampere; the university-wide thesis workflow uses Turnitin, followed by final submission to Trepo after supervisor permission. Master’s theses are graded 0-5, and the university-wide normal examiner period is 21 days, extended to 28 days when an additional maturity test is required.

Before freezing the manuscript, audit the entire evidence chain: question → cohort/sample → preprocessing → split/experimental design → model/statistics → raw outputs → figures → conclusions. Check that personal or restricted data are absent from the public thesis, all AI use follows current Tampere guidance, dataset and software versions are recorded, and every clinical or biological claim stays within the evidence level actually tested.

33. Final Natural Sciences Biomedical Informatics checklist

Confirm BBTM.BMI-S14, BBT.MJS.109 (2 ECTS pass/fail) and BBT.OPN.002 (30 ECTS, 0-5) in your current Sisu plan. If BBT.OPN.EXT.002 (10 ECTS pass/fail) is used, confirm it was defined with the supervisor and is not being misreported as part of the thesis credit value. Verify project-plan approval, seminar participation, maturity requirement, ethics/data permissions, Turnitin, Trepo/PDF-A, public-versus-restricted material and graduation timing.

Scientifically, verify the experimental unit, participant/sample grouping, leakage prevention, preprocessing provenance, reference/database versions, baseline choice, metric rationale, uncertainty, multiplicity, confounding, external/temporal validation claims and reproducibility artifacts. A strong Natural Sciences Biomedical Informatics thesis is not the model with the highest score; it is the study whose biomedical question and evidence remain traceable and correctly bounded from raw data to final conclusion.