A New Approach to Large Multiomics Data Integration
Bibliographic record
Abstract
Data reduction and data mining are common practices for handling large-scale data from wide-ranging sources, but high-dimensional omics and imaging data sets present difficult challenges for feature extraction and data mining due to the large number of features that cannot be simultaneously examined. The sample numbers and variables in these methods are constantly growing as new technologies are developed, and computational analysis needs to evolve to keep up with growing demand. In recent years, there has been a rapid uptake of nonlinear dimensionality reduction via methods such as t-distributed stochastic neighbor embedding and uniform manifold approximation and projection. These approaches have revolutionized our ability to visualize and interpret high-dimensional data and have rapidly become preferred methods for analysis of data sets containing an extremely high number of variables. Further to this is the emerging interest in combining information from multiple omics sources to gain a more holistic view of systems biology. Current state-of-the-art algorithms can perform data mining, visualization, and classification on routine data sets but struggle when data sets grow above a certain size. We present a new approach to large and multiomic data integration to extract, mine, and integrate large multiomics data sets that were previously considered prohibitively large. Here, we demonstrate the use of deep learning on subsampled nonlinear dimensionality reduction using t-SNE and UMAP to extract features from large complex data sets including mass spectrometry imaging and chromosome conformation capture. We then go on to demonstrate how this method can be used to learn embeddings from the fusion of different omics data, allowing metabolomics data to be projected into a reduced transcriptomics representation.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".