Gene function: Getting specific, generally speaking
Bibliographic record
Abstract
Much of modern biological research is concerned with identifying genes and the protein products of genes involved in cellular processes: determining how, when, and where they are involved in specific biochemical processes. The tools by which these aims are achieved can be roughly divided into two types: those that are specific to the study of individual or classes of proteins versus those with more general and broad utility. General tools are methods that allow for rapid inference of function of a gene product, either from the mRNA or the proteins it codes for, for any particular molecule or class of molecules. These are increasingly in demand, particularly those that can be applied to entire genomes or large subsets of the genes contained therein. At the same time, generality comes at a cost. By their nature, general tools do not usually provide high-quality information about the function of a gene and may even mislead, particularly when applied across large numbers of genes. Examples include DNA microarrays and multidimensional separation-MS and yeast two-hybrid strategies that detect protein–protein interactions or complexes (1–9). Although these approaches can, respectively, provide information on whether and to what extent a given gene is being transcribed in a defined condition and with which proteins the protein gene product is interacting with, they cannot provide any insight into other crucial questions. For instance, many proteins are enzymes that catalyze biochemical reactions. In the case of novel genes of unknown function it is not easy to definitively determine whether they are enzymes nor what reactions they may catalyze. In this issue of PNAS Baker et al. (10) present a proof-of-principle study on another approach that might fulfill this need, but unlike the approaches described previously, the strategy is applied in intact, living cells. Here we discuss the significance of this …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".