The DOI matches its Crossref record; 17 of the 18 references it cites were found in a registry or an index; none of the 19 sources its claims cite is checked for support yet.
Visible Machine Learning for Biomedicine
Open accessAbstract
A major ambition of artificial intelligence lies in translating patient data to successful therapies. Machine learning models face particular challenges in biomedicine, however, including handling of extreme data heterogeneity and lack of mechanistic insight into predictions. Here, we argue for “visible” approaches that guide model structure with experimental biology.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
The paper argues that current black-box machine learning is intrinsically limited in biomedicine by extreme data heterogeneity and lack of mechanistic interpretation, and proposes “visible” machine learning guided by biological structure. It outlines examples and near-term milestones for interpretable, mechanism-aware biomedical models.
Key findings
- Current machine learning faces two acute biomedical challenges: input heterogeneity, where the same outcome can arise from vastly different inputs (e.g., cancer from many rare combinations of genetic alterations), and black-box predictions that do not link outputs to underlying mechanisms.
- Brute-force data scaling is limited because patient numbers are finite; even common conditions like cancer and heart disease may saturate at “a few million patient examples,” which the authors argue is modest compared with the data needs of complex biological systems.
- Visible learning uses prior biological structure—such as molecular networks and hierarchical cell/tissue organization—to guide model architecture, potentially addressing heterogeneity through biologically meaningful compression and enabling mechanistic interpretation of internal model states.
- Examples cited include Lin et al. (2017), who structured a deep neural network based on hierarchical organization of transcriptional regulatory factors; DCell (Ma et al., 2018), which maps neurons to biological modules to translate mutated genes to cell proliferation; and Alvarez et al. (2016), who used a transcriptional regulatory network to infer regulatory protein activities from mRNA expression.
Methods
- This is a commentary/perspective, not an empirical study; no new dataset, sample, or statistical analysis is reported.
- The argument synthesizes prior literature and examples from machine learning and computational biology to propose visible machine learning and near-term milestones.
Limitations
- The paper states limitations of current black-box machine learning: data heterogeneity, lack of mechanistic insight, finite patient datasets, and a trade-off between increased model resolution and model scope.
- As a commentary, it presents no primary validation, quantitative benchmarks, or systematic review; its proposals and milestones are not empirically tested here (evident from the text).
- The paper also notes that literature curation is difficult and misses unknown human biology, and that basic experimental data types are not usually well connected to patient-data analysis.
Model-written summary. Check it against the paper before relying on it.
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Claims the paper makes
0 of 19 cited sources checked · 0 supported · 0 flagged
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Here, we argue that the answer is no–that the very high complexity of biological systems will intrinsically limit applications of current “black box” machine learning in patient data.”
§ Visible Machine Learning for Biomedicine > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Rests on an assumption: Biological systems are almost certainly more complex than those addressed by machine learning in other areas.
None of the sources cited for this claim have been checked yet.
In the paper
“In contrast, direct incorporation of the biological structure of cells and tissues, such as explored by Lin et al. (2017) and Ma et al. (2018), leads to a ready-made working model of how biological inputs, such as genotype, are compressed to determine outcomes.”
§ Visible Machine Learning for Biomedicine > Understanding Biomedical Data with Visible Machine Learning
What the sources say
- Not checked yetcites Lin et al., 2017 source
- Not checked yetcites Ma et al., 2018
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesNone of the sources cited for this claim have been checked yet.
In the paper
“Network connections can also nominate a set of genes whose input data should be aggregated to create a composite feature, such as pooling of rarely altered genes in cancer into a single pathway, which, viewed as a single aggregate, is recurrently altered across a patient population (Alvarez et al., 2016; Chuang et al., 2007).”
§ Visible Machine Learning for Biomedicine > Groundwork toward Visible Machine Learning in Biology
What the sources say
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn the paper
“Neurons are organized into banks, each of which maps to a distinct biological module within a large hierarchy of known cellular components and processes.”
§ Visible Machine Learning for Biomedicine > Groundwork toward Visible Machine Learning in Biology
What the sources say
- Not checked yetcites Ma et al., 2018
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“Like man-made engines, biological systems are also complex machines whose outputs emerge from a hierarchy of internal components (Simon, 1962).”
§ Visible Machine Learning for Biomedicine > Toward Visible Engines for Machine Learning
What the sources say
- Not checked yetcites Simon, 1962
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Even for common conditions like cancer and heart disease, the number of available datasets will saturate at a few million patient examples.”
§ Visible Machine Learning for Biomedicine > Dual Challenges of Data Heterogeneity and Lack of Mechanistic Interpretation
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclear- Rests on an assumption: The total number of patients is finite.
In the paper
“For instance, an important lesson is that increased model resolution (e.g., introducing many detailed biochemical parameters) may come at the expense of model scope (e.g., the ability to address all biological elements and generalize to new patient cohorts).”
§ Visible Machine Learning for Biomedicine > Groundwork toward Visible Machine Learning in Biology
What the sources say
- Not checked yetcites Fisher and Henzinger, 2007
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As a consequence, even after obtaining the genome sequence of every patient, the genetic patterns driving disease may still remain undiscovered by current statistical methods.”
§ Visible Machine Learning for Biomedicine > Dual Challenges of Data Heterogeneity and Lack of Mechanistic Interpretation
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Rests on an assumption: Biological systems are almost certainly more complex than those addressed by machine learning in other areas.
- Rests on an assumption: The total number of patients is finite.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is nonetheless easy to see how prior knowledge of biological structure might provide distinct advantages to models capable of incorporating this information–what we here call visible learning–and recent research has begun to prove the concept.”
§ Visible Machine Learning for Biomedicine > Toward Visible Engines for Machine Learning
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclearA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“First, prior knowledge of biological structure can address the problem of data heterogeneity, since different input patterns, even when entirely distinct from one another (i.e., without common patterns), converge on common higher-order biological processing units corresponding to discrete modular components of cells and tissues.”
§ Visible Machine Learning for Biomedicine > Understanding Biomedical Data with Visible Machine Learning
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclear
- It may state two separate claims as one. 10
- Probably not a substantial claim. 1
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While a million may seem large, this number is modest compared to the amounts of data often needed to train a statistical model, compounded by the as-yet-unknown, but undoubtedly high, complexity of biological systems.”
§ Visible Machine Learning for Biomedicine > Dual Challenges of Data Heterogeneity and Lack of Mechanistic Interpretation
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes? Checkable: unclear✓ Load-bearing: passes? Based: unclearIt may state two separate claims as one.
- Rests on an assumption: Biological systems are almost certainly more complex than those addressed by machine learning in other areas.
In the paper
“Similar heterogeneity arises in patient data from nearly all common diseases, including cardiovascular, metabolic, and neurodevelopmental disorders, in which recurrent patterns are elusive, making it difficult to make reliable predictions (Boyle et al., 2017).”
§ Visible Machine Learning for Biomedicine > Dual Challenges of Data Heterogeneity and Lack of Mechanistic Interpretation
What the sources say
- Not checked yetcites Boyle et al., 2017 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“For example, cancer can arise as the result of many different combinations of genetic alterations involving many potential genes, any one of which may be mutated only rarely; as a consequence, each new patient presents a distinct constellation of molecular changes never before seen in nature (Alvarez et al., 2016; Kourou et al., 2014).”
§ Visible Machine Learning for Biomedicine > Dual Challenges of Data Heterogeneity and Lack of Mechanistic Interpretation
What the sources say
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“Similarly, in biomedicine, many machine-learning methods are being developed to predict patient outcomes (Kourou et al., 2014), but these approaches typically do not link predictions to underlying mechanisms (Figure 1B).”
§ Visible Machine Learning for Biomedicine > Dual Challenges of Data Heterogeneity and Lack of Mechanistic Interpretation
What the sources say
- Not checked yetcites Kourou et al., 2014 source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“A second well-known challenge is that modern machine learning models, including deep neural networks, are black boxes, devices which focus on predicting outputs from inputs without regard for the mechanism or rationale by which a particular outcome is brought about.”
§ Visible Machine Learning for Biomedicine > Dual Challenges of Data Heterogeneity and Lack of Mechanistic Interpretation
What the sources say
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Given input patient data, execution of the model not only produces a final output state; it also reveals the states of internal biological systems. The most striking of these internal states provide hypotheses as to the underlying mechanisms governing patient phenotype, which is important because many internal biological states are difficult to measure through direct experimental observations.”
§ Visible Machine Learning for Biomedicine > Understanding Biomedical Data with Visible Machine Learning
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“That is, machine learning will not replace the need for experimental cell and tissue biology; it will be substantially enabled by such knowledge, given the right visible intelligence infrastructure.”
§ Visible Machine Learning for Biomedicine > Abstract
Tests✓ Asserted: passes? Checkable: unclear✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: Causal mechanistic insights are key to identifying drug targets and advancing basic biological knowledge.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Internal states of the model may also indicate biological components that can be targeted by therapeutic interventions or form the basis for in silico testing of treatment combinations.”
§ Visible Machine Learning for Biomedicine > Understanding Biomedical Data with Visible Machine Learning
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: Causal mechanistic insights are key to identifying drug targets and advancing basic biological knowledge.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Second, models guided by biological structure can be interpreted mechanistically, informing our understanding of the system and suggesting potential therapeutic strategies.”
§ Visible Machine Learning for Biomedicine > Understanding Biomedical Data with Visible Machine Learning
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: Causal mechanistic insights are key to identifying drug targets and advancing basic biological knowledge.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Parallel development of these directions will enable a new generation of biomedical machine learning, replacing black-box models that focus on isolated problem domains with visible models that survey general biological systems.”
§ Visible Machine Learning for Biomedicine > Goals and Milestones for the Near Future
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
The paper relies on this without support.
Biological systems are almost certainly more complex than those addressed by machine learning in other areas.
“Although such input heterogeneity is a property of many and perhaps all complex systems, biological systems are almost certainly more complex than those addressed by machine learning in other areas.”
The paper relies on this without support.
The total number of patients is finite.
“The total number of patients is finite, however.”
The paper relies on this without support.
Causal mechanistic insights are key to identifying drug targets and advancing basic biological knowledge.
“This is a missed opportunity, as causal mechanistic insights are key to identifying drug targets and advancing basic biological knowledge.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Ideker and co-workers showed that AI/ML network architectures can be crafted following the logic of a hierarchy (ontology) that reflects the known machinery of cells.182,183”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [182]
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Metadata
- Authors
- Yu, M. K., Ma, J., Fisher, J., Kreisberg, J. F., Raphael, B. J., & Ideker, T.
- Affiliations
- University of California San Diego (US)
- University of Cambridge (GB)
- Princeton University (US)
- Corresponding author
- Raphael, Benjamin J.
- Ideker, Trey
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2018-06
- Volume
- 173
- Issue
- 7
- Pages
- 1562-1565
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC6483071
- OpenAlex
- W2808686685
- Semantic Scholar
- 2dc18f661b400033abd1086b917c451d3358aef2
- Cited by
- 162
- References
- 18
- Citations by year
- 2026: 8 · 2025: 10 · 2024: 20 · 2023: 23 · 2022: 24 · 2021: 23 · 2020: 29 · 2019: 22 · 2018: 2
- FWCI
- 10.71
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (R01 HG009979, R01 HG007069, U24 CA211000, P41 GM103504, OT3 TR002026)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Keywords
- model interpretability, data heterogeneity
- MeSH terms
- Machine Learning, Algorithms, Computational Biology, Biomedical Research
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-10, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-10 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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