There is a persistent gap between what computational biologists know about the limitations and failure modes of their analyses and what gets communicated to the bench scientists, program leaders, and investors who act on those analyses. This communication gap has real consequences: decisions are made with more confidence than the underlying analysis warrants, failure modes that could have been anticipated are treated as surprises, and the lessons from failed programs do not propagate back into improved analytical practice.
Things Computational Biologists Know That Often Go Unsaid
A computational biologist running a target prioritization analysis knows that the scores they produce are summaries of available evidence, not measurements of ground truth. They know that the databases they are pulling evidence from are themselves biased toward well-studied biological domains and recent publications. They know that their normalization choices involve assumptions that may not hold perfectly for the specific datasets they are working with. They know that batch effects in the training data for any machine learning components will introduce systematic biases that are not fully correctable.
These are not criticisms of the analysis -- they are standard features of working with biological data at scale. A skilled computational biologist carries these caveats mentally and adjusts their interpretation accordingly. The problem is that these caveats frequently do not make it into the deliverable that leaves the computational team and enters the program decision-making process. A slide showing a ranked target list with confidence scores does not communicate the same information as a conversation with the analyst who produced it.
The Communication Compression Problem
Scientific communication is inherently compressive. A GWAS paper compresses months of analysis into a set of lead SNPs and effect sizes. A target ranking report compresses weeks of multi-omics integration into a list of candidates with scores. A program committee presentation compresses months of program activity into thirty slides. Each compression step removes nuance and uncertainty, and the nuance that gets removed is systematically the kind that makes claims look less certain.
The computational biologists who produce these analyses are usually aware of their limitations. The program leaders and investors who receive the compressed output usually are not, and the format of the deliverable often does not invite the question. A ranked list with scores is a decision document; it does not naturally surface the question "how uncertain is the score for the third-ranked candidate, and why?"
What Good Knowledge Transfer Looks Like
Better knowledge transfer from computational to experimental biology requires specific practices, not just a general commitment to transparency. At minimum, a target ranking deliverable should include: the evidence tier breakdown for each candidate (not just the composite score), explicit notation of which evidence sources are absent or sparse for each candidate, a description of which analytical choices have the most impact on the final ranking, and flagging of candidates where evidence types are in conflict.
Flagging conflicting evidence is particularly important and particularly underused. A target where genetic evidence is strong but proteomics evidence is absent is a different research question from a target where both are present. A target where multiple functional studies show opposite results deserves different treatment than one where all functional studies agree. Calling attention to these specific patterns in the deliverable enables the receiving scientists to design their initial experiments to address the most critical uncertainties rather than proceeding without awareness of them.
The Reproducibility and Audit Trail Problem
Computational biologists also frequently know that their analyses are not as reproducible as they should be. An analysis run in a Jupyter notebook with data that was manually downloaded and preprocessed months ago may produce different results if rerun today, because the database versions have changed, intermediate steps were manual, and some steps were applied interactively without being captured in the analysis code. This is an industry-wide problem in bioinformatics that is well documented and slowly improving, but slowly.
The implication for target discovery programs is that a decision made on the basis of a prioritization analysis that was run a year ago and cannot be reliably reproduced is resting on an analytical foundation that cannot be updated or audited. When new data becomes available -- a new GWAS with higher power, new functional data from a collaborator, a preprint that changes the picture for a specific target -- updating the analysis to incorporate that data is difficult or impossible without essentially starting over.
Domain Knowledge is Irreplaceable but Often Excluded
Computational biologists also know that the most valuable analyses are those that successfully incorporate domain knowledge from wet-lab colleagues. Knowing which cell models are appropriate for a specific target, which assay artifacts are common in a given biological domain, which published results are well-respected versus which come from groups with replication problems -- none of this is captured in databases or scoring algorithms, but it substantially affects the interpretation of computational results.
The structural challenge is that computational and wet-lab biologists often work in parallel rather than iteratively. The computational team produces a ranked list; the wet-lab team receives it and begins experiments. The feedback loop that would allow wet-lab knowledge to shape the computational analysis is often weak or intermittent. More closely integrated workflows -- where computational and wet-lab scientists review the ranking together before experimental commitments are made, and where experimental outcomes feed back into the scoring methodology -- consistently produce better outcomes than the serial handoff model.
Closing the Loop: Outcome Tracking
The most underused tool for bridging the knowledge gap between computational and wet-lab biology is systematic outcome tracking. When a target that was ranked highly by a computational analysis performs well in initial wet-lab characterization, recording which evidence inputs drove the high ranking and whether they were the most predictive provides direct feedback to the scoring methodology. When a high-ranked target fails to show the expected phenotype, the same exercise identifies which evidence was misleading.
Few organizations do this systematically, because it requires maintaining a linkage between the computational analysis and the experimental results that crosses organizational boundaries, timescales, and database systems. But organizations that have implemented outcome tracking -- even informally, as a regular discussion in program reviews -- consistently report that it improves the quality of subsequent computational analyses by updating the mental models of both the computational and wet-lab scientists. The knowledge that was implicitly held by the computational biologist becomes explicit, shared, and actionable for the whole team.