Evidence
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Beyond data limitations, the computational demands of LLMs pose a further barrier to their routine adoption in bioinformatics. Training and deploying large models require substantial GPU memory, storage, and energy, which are often unavailable in typical academic or clinical settings. This resource gap constrains who can develop and apply these models, reinforcing the concentration of LLM research in well-resourced institutions.
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Heterogeneous large datasets integration using Bayesian factor regression ↗
Equation ( 1 ) regresses the observed data X X on known covariates and on a latent factor structure. In particular, it allows additive batch effects to be accounted for by incorporating the variables recording the batches into v i \texttt{v}_{i} . However, in practice one often observes more complex…
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Equation ( 1 ) regresses the observed data X X on known covariates and on a latent factor structure. In particular, it allows additive batch effects to be accounted for by incorporating the variables recording the batches into v i \texttt{v}_{i} . However, in practice one often observes more complex batch effects; specifically in bioinformatics it is common to observe multiplicative effects on the variance ( Johnson et al., 2007 ) . We will later describe an example of this, shown in Figure 5 . Such artefacts cannot be captured by ( 1 ) given that Σ \Sigma is assumed constant across all individuals.