Genetics

Robotic-navigated assistance in spine surgery.

The aim of this study was to assess the accuracy of pedicle screw placement, as well as intraoperative factors, radiation exposure, and complication rates in adult patients with degenerative disorders of the thoracic and lumbar spines who have undergone robotic-navigated spinal surgery using a contemporary system. The authors reviewed the prospectively collected data on 196 adult patients who had ...

ResMiCo: Increasing the quality of metagenome-assembled genomes with deep learning.

The number of published metagenome assemblies is rapidly growing due to advances in sequencing technologies. However, sequencing errors, variable coverage, repetitive genomic regions, and other factors can produce misassemblies, which are challenging to detect for taxonomically novel genomic data. Assembly errors can affect all downstream analyses of the assemblies. Accuracy for the state of the a...

Wide and deep learning based approaches for classification of Alzheimer's disease using genome-wide association studies.

The increasing incidence of Alzheimer's disease (AD) has been leading towards a significant growth in socioeconomic challenges. A reliable prediction ...

Efficient Generation of Paired Single-Cell Multiomics Profiles by Deep Learning.

Recent advances in single-cell sequencing technology have made it possible to measure multiple paired omics simultaneously in a single cell such as ce...

Preliminary evaluation of deep learning for first-line diagnostic prediction of tumor mutational status.

The detection of tumour gene mutations by DNA or RNA sequencing is crucial for the prescription of effective targeted therapies. Recent developments s...

Modular reconfiguration of DNA origami assemblies using tile displacement.

The power of natural evolution lies in the adaptability of biological organisms but is constrained by the time scale of genetics and reproduction. Eng...

Reconfigurable self-assembled DNA devices.

Modular reconfigurable systems can be achieved with DNA origami, demonstrating the potential to generate molecular robots.

Analysis of cardiac single-cell RNA-sequencing data can be improved by the use of artificial-intelligence-based tools.

Single-cell RNA sequencing (scRNAseq) enables researchers to identify and characterize populations and subpopulations of different cell types in heart...

MutBLESS: A tool to identify disease-prone sites in cancer using deep learning.

Understanding the molecular basis and impact of mutations at different stages of cancer are long-standing challenges in cancer biology. Identification...

Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies.

Antibodies have the capacity to bind a diverse set of antigens, and they have become critical therapeutics and diagnostic molecules. The binding of an...

Modulation of DNA-protein Interactions by Proximal Genetic Elements as Uncovered by Interpretable Deep Learning.

Transcription factors (TF) recognize specific motifs in the genome that are typically 6-12 bp long to regulate various aspects of the cellular machine...

m5U-SVM: identification of RNA 5-methyluridine modification sites based on multi-view features of physicochemical features and distributed representation.

BACKGROUND: RNA 5-methyluridine (m5U) modifications are obtained by methylation at the C position of uridine catalyzed by pyrimidine methylation trans...

Rapid viral metagenomics using SMART-9N amplification and nanopore sequencing.

Emerging and re-emerging viruses are a global health concern. Genome sequencing as an approach for monitoring circulating viruses is currently hampere...

Histopathological bladder cancer gene mutation prediction with hierarchical deep multiple-instance learning.

Gene mutation detection is usually carried out by molecular biological methods, which is expensive and has a long-time cycle. In contrast, pathologica...

Deep learning for optimization of protein expression.

Advances in high-throughput DNA synthesis and sequencing have fuelled the use of massively parallel reporter assays for strain characterization. These...

A scoping review on deep learning for next-generation RNA-Seq. data analysis.

In the last decade, transcriptome research adopting next-generation sequencing (NGS) technologies has gathered incredible momentum amongst functional ...

Machine Learning Predicts the Oxidative Stress Subtypes Provide an Innovative Insight into Colorectal Cancer.

So far, it has been reached the academic consensus that the molecular subtypes are via genomic heterogeneity and immune infiltration patterns. Conside...

Automatic sex estimation using deep convolutional neural network based on orthopantomogram images.

Sex estimation is very important in forensic applications as part of individual identification. Morphological sex estimation methods predominantly foc...

Validation of genetic variants from NGS data using deep convolutional neural networks.

Accurate somatic variant calling from next-generation sequencing data is one most important tasks in personalised cancer therapy. The sophistication o...

Coupling machine learning and epidemiological modelling to characterise optimal fungicide doses when fungicide resistance is partial or quantitative.

Increasing fungicide dose tends to lead to better short-term control of plant diseases. However, high doses select more rapidly for fungicide resistan...