Genetics

Deep learning based on susceptibility-weighted MR sequence for detecting cerebral microbleeds and classifying cerebral small vessel disease.

BACKGROUND: Cerebral microbleeds (CMBs) serve as neuroimaging biomarkers to assess risk of intracerebral hemorrhage and diagnose cerebral small vessel disease (CSVD). Therefore, detecting CMBs can evaluate the risk of intracerebral hemorrhage and use its presence to support CSVD classification, both are conducive to optimizing CSVD management. This study aimed to develop and test a deep learning (...

Improving Enhancer Identification with a Multi-Classifier Stacked Ensemble Model.

Enhancers are DNA regions that are responsible for controlling the expression of genes. Enhancers are usually found upstream or downstream of a gene, or even inside a gene's intron region, but are normally located at a distant location from the genes they control. By integrating experimental and computational approaches, it is possible to uncover enhancers within DNA sequences, which possess regul...

KDeep: a new memory-efficient data extraction method for accurately predicting DNA/RNA transcription factor binding sites.

This paper addresses the crucial task of identifying DNA/RNA binding sites, which has implications in drug/vaccine design, protein engineering, and ca...

[The Swecrit Biobank, associated clinical registries, and machine learning (artificial intelligence) improve critical care knowledge].

The unique Swecrit Biobank and its associated clinical registries for sepsis, ARDS, cardiac arrest, trauma, and COVID-19 include more than 150,000 blo...

Batch normalization followed by merging is powerful for phenotype prediction integrating multiple heterogeneous studies.

Heterogeneity in different genomic studies compromises the performance of machine learning models in cross-study phenotype predictions. Overcoming het...

Predicting 3D RNA structure from the nucleotide sequence using Euclidean neural networks.

Fast and accurate 3D RNA structure prediction remains a major challenge in structural biology, mostly due to the size and flexibility of RNA molecules...

Graph neural networks for the identification of novel inhibitors of a small RNA.

MicroRNAs (miRNAs) play a crucial role in post-transcriptional gene regulation and have been implicated in various diseases, including cancers and lun...

c-Diadem: a constrained dual-input deep learning model to identify novel biomarkers in Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is an incurable, debilitating neurodegenerative disorder. Current biomarkers for AD diagnosis require expensive n...

Epigenetic target identification strategy based on multi-feature learning.

The identification of potential epigenetic targets for a known bioactive compound is essential and promising as more and more epigenetic drugs are use...

Polygenic modelling and machine learning approaches in pharmacogenomics: Importance in downstream analysis of genome-wide association study data.

Genome-wide association studies (GWAS) have identified genetic variations associated with adverse drug effects in pharmacogenomics (PGx) research. How...

The impact of misclassification errors on the performance of biomarkers based on next-generation sequencing, a simulation study.

The development of next-generation sequencing (NGS) opens opportunities for new applications such as liquid biopsy, in which tumor mutation genotypes ...

One label is all you need: Interpretable AI-enhanced histopathology for oncology.

Artificial Intelligence (AI)-enhanced histopathology presents unprecedented opportunities to benefit oncology through interpretable methods that requi...

Enhanced detection of Listeria monocytogenes using tetraethylenepentamine-functionalized magnetic nanoparticles and LAMP-CRISPR/Cas12a-based biosensor.

BACKGROUND: Listeria monocytogenes is a pathogenic bacterium that can lead to severe illnesses, especially among vulnerable populations. Therefore, th...

disperseNN2: a neural network for estimating dispersal distance from georeferenced polymorphism data.

Spatial genetic variation is shaped in part by an organism's dispersal ability. We present a deep learning tool, disperseNN2, for estimating the mean ...

Inherently interpretable position-aware convolutional motif kernel networks for biological sequencing data.

Artificial neural networks show promising performance in detecting correlations within data that are associated with specific outcomes. However, the b...

A Machine-Learning-Based Approach to Prediction of Biogeographic Ancestry within Europe.

Data obtained with the use of massive parallel sequencing (MPS) can be valuable in population genetics studies. In particular, such data harbor the po...

Ultra-fast deep-learned CNS tumour classification during surgery.

Central nervous system tumours represent one of the most lethal cancer types, particularly among children. Primary treatment includes neurosurgical re...

Emerging applications of machine learning in genomic medicine and healthcare.

The integration of artificial intelligence technologies has propelled the progress of clinical and genomic medicine in recent years. The significant i...

Reliable interpretability of biology-inspired deep neural networks.

Deep neural networks display impressive performance but suffer from limited interpretability. Biology-inspired deep learning, where the architecture o...

Patient Graph Deep Learning to Predict Breast Cancer Molecular Subtype.

Breast cancer is a heterogeneous disease consisting of a diverse set of genomic mutations and clinical characteristics. The molecular subtypes of brea...