Latest AI and machine learning research in genetics for healthcare professionals.
Annotating single-cell and spatial RNA-seq data can be greatly enhanced by leveraging bulk RNA-seq, which remains a cost-effective and well-established benchmark for characterising transcriptional activity in immune cell populations. However, a major technical hurdle lies in the contrasting properties of these data types: single-cell and spatial data are inherently sparse due to its cell-level sam...
Astrocytes regulate the activity of nearby neurons so disruption of astrocyte calcium dynamics by traumatic brain injury (TBI) could have profound consequences for neural network activity in the brain. In this study, human induced pluripotent stem cell (hiPSC)-derived astrocytes were used in a two-dimensional (2D) in vitro stretch injury model to evaluate the effect of trauma on calcium dynamics, ...
Temporal gene expression is being analyzed via high-throughput profiling of molecular data over time. The expression values of genes are impacted by t...
Generative AI is increasingly used to extract structured information across domains, but its reliability in academic and clinical research, where prec...
CRISPR technologies has become an integral part of plant biotechnology, synthetic biology and basic plant research, routinely used by researchers for ...
Woodland strawberry (Fragaria vesca) is a widely used model system for cultivated strawberries and Rosaceae for molecular genetic studies. Nevertheles...
The gut microbiome plays a crucial role in human health, but machine learning applications in this field face significant challenges, including limite...
Nanobodies are antigen-binding proteins of great interest as diagnostics and therapeutics. Accurate and fast characterization of their complementarity...
Transcriptomic biomarker discovery has been a challenge due to variation in datasets and platforms, complexity in statistical and computational method...
Alignment-based methods are fundamental for sequence comparison but are often computationally prohibitive for large-scale genomic analyses. This limit...
Transformer-based genomic sequence-to-function models effectively capture long-range genomic interactions but incur high computational costs due to th...
The integration of transcriptomic and genomic data is essential for dissecting the molecular basis of complex cardiovascular diseases (CVD). Existing ...
Missense variant interpretation in highly conserved, paralog-rich gene families remains a critical bottleneck for precision medicine. Here, we develop...
T cell exhaustion limits the efficacy of cancer immunotherapies. Here, we performed genome-wide loss-of-function screening in repetitively stimulated ...
Accurate detection of mutations within bacterial species is critical for fundamental studies of microbial evolution, reconstructing transmission event...
Current DNA damage repair (DDR) biomarkers employ binary classifications that fail to capture the molecular complexity of tumors with concurrent repai...
The development of CRISPR-Cas9 cleavage activity prediction tools hinges on data produced from high-throughput guide-target lentiviral library screens...
Secondary metabolites in plants have various physiological functions, including antioxidant and antibacterial activities. Previous studies have sugges...
The rapid evolution of DNA foundation models promises to revolutionize genomics, yet comprehensive evaluations are lacking. Here, we present a compreh...
As sequencing technology improves, more genomes become available. Most lack annotation, automated methods are error prone, and few genomes are ever ma...