It is suggested that progress requires reframing seq2func models as continually refined systems, in which targeted perturbation experiments, systematic evaluation and iterative model updates are tightly coupled through artificial intelligence-experiment feedback loops, enabling self-improving models that progressively deepen mechanistic understanding and more reliably support biological discovery.
Deciphering the regulatory consequences of sequence divergence across human evolution is essential to understanding the molecular basis of human-specific traits and disease. Although millions of derived alleles distinguish humans from great apes, only a small fraction are likely to influence human-specific traits. Prev...
Riley J. Mangan, Nikitha Thoduguli, Dimitar Ivanov et al.· bioRxiv· 0 citations
This review compares convolutional, Transformer-based and graph architectures used to represent local sequence features, chromatin state and three-dimensional genome organisation to their applications to transcription-factor binding, chromatin accessibility, gene expression, non-coding variant prioritisation and regula...
An encyclopedia of enhancer–gene regulatory interactions in the human genome is built, revealing global properties of enhancer networks, identifying differences in regulatory complexity across genes, and improving analyses linking noncoding variants to target genes and cell types for common, complex diseases.
A. Gschwind, Kristy S. Mualim, Alireza Karbalayghareh et al.· Nature· 6 citations
Large-scale genomic language models (gLMs) hold promise for modeling gene regulation, yet their ability to capture personal gene expression variations remains unresolved. We developed xDecoder, a unified decoding framework that utilizes gLMs and sequence-to-function (S2F) embeddings to learn how personal genetic variat...
RegTrust-XAI is developed and evaluated, a trust-aware framework separating these questions using three inference-time signals: ensemble consensus, motif-grounded attribution coherence, and applicability-domain distance, which gives a more complete basis for deciding when to act on a prediction than accuracy alone.
Abdulmujeeb T. Onawole, Sulaimon Basiru, M. Sanni et al.· bioRxiv· 0 citations
Advances in network-enabled gene discovery are summarized, how multi-omics and AI are transforming transcriptional target prediction is discussed, and how these developments may lead to predictive models of plant gene regulation with applications in crop improvement and synthetic biology are considered.
Dae Kwan Ko, Federica Brandizzi· Current opinion in plant bio...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
