Latest AI and machine learning research in genetics for healthcare professionals.
Design-Build-Test-Learn (DBTL) cycles are a widely employed engineering framework in metabolic engineering. Nonetheless, their performance depends on a wide range of experimental and algorithmic design choices, whose combined effects on the successful optimization of microbial strains remain an open question. In this study, we performed in-silico DBTL cycles based on metabolic kinetic models to qu...
Compared to other nucleotides, the cytosines stand as the most expressive one for gene regulation in plants due to its status as methylation-based epigenetic switch. Methylation of some of these cytosines may have higher impact on downstream genes, making them critical ones. To this date not much has been done to decipher the criticality of such cytosines. This is first such pioneering study in de...
Background. Antenatal depressive symptoms (ADS) are common and underdiagnosed, particularly in low and middle income countries, and are associated wit...
Programmable A-to-I RNA editing using endogenous ADAR enzymes is emerging as a therapeutic strategy, but editability remains difficult to predict beca...
Genome language models (gLM) have the potential to further understanding of regulatory genomics without requiring labeled data. Most gLMs are pretrain...
Monoclonal antibodies (mAbs) are critical in disease diagnostics and therapeutics, yet the performance of mass spectrometry (MS)-based de novo sequenc...
The mRNA serves as a crucial bridge between DNA and proteins. Compared to DNA, mRNA sequences are much more concise and information-dense, which makes...
Background: Gastrointestinal stromal tumor (GIST) is the most common gastrointestinal mesenchymal tumor, driven by tyrosine-protein kinase KIT and pla...
Genetic-based risk prediction is becoming increasingly available for a wide range of common diseases thanks to the growth of large-scale biobanks and ...
ALS shows marked clinical heterogeneity, yet much real-world evidence remains trapped in unstructured reports. Here we introduce MEDSTREM, a large-lan...
RNA velocity techniques have emerged as efficient tools for unraveling the complex trajectories of cell development and differentiation. However, most...
Foundation models aim to learn useful representations of biological sequences. However, the applicability of these representations for a wide range of...
DNA methylation is a central epigenetic modification that regulates gene expression, maintains genomic stability, and guides cellular differentiation....
The effects of perturbation on a biological system can be readily measured in terms of transcriptional changes. However, despite a wealth of transcrip...
Epigenetic mechanisms regulate gene-expression by altering the structure of the chromatin without modifying the underlying DNA sequence. Histone post-...
Logit Lens has been proposed for visualizing tokens that contribute most to LLM answers. Recently, Logit Lens was also shown to be applicable in autor...
Recently, data-centric AI methodology has been a dominant paradigm in single-cell transcriptomics analysis, which treats data representation rather th...
Recent genomic foundation models largely adopt large language model architectures that treat DNA as a one-dimensional token sequence. However, exhaust...
Many optimizers can be interpreted as steepest-descent methods under norm-induced geometries, and thus inherit corresponding implicit biases. We intro...
Deciphering how DNA sequence encodes gene regulation remains a central challenge in biology. Advances in machine learning and functional genomics have...