Genetics

Latest AI and machine learning research in genetics for healthcare professionals.

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Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance.

Sugarcane is an economically important crop, but its genomic complexity has hindered advances in mol...

DeepPheno: Predicting single gene loss-of-function phenotypes using an ontology-aware hierarchical classifier.

Predicting the phenotypes resulting from molecular perturbations is one of the key challenges in gen...

Screening of important metabolites and KRAS genotypes in colon cancer using secondary ion mass spectrometry.

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) is an imaging-based analytical technique t...

Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning.

Traces from single-molecule fluorescence microscopy (SMFM) experiments exhibit photophysical artifac...

Genome-enabled prediction of reproductive traits in Nellore cattle using parametric models and machine learning methods.

This study aimed to assess the predictive ability of different machine learning (ML) methods for gen...

Shared decision-making and maternity care in the deep learning age: Acknowledging and overcoming inherited defeaters.

In recent years there has been an explosion of interest in Artificial Intelligence (AI) both in heal...

Diagnosis of thyroid neoplasm using support vector machine algorithms based on platelet RNA-seq.

OBJECTIVE: To assess the capacity of support vector machine (SVM) algorithms that are developed base...

An integrative knowledge graph for rare diseases, derived from the Genetic and Rare Diseases Information Center (GARD).

BACKGROUND: The Genetic and Rare Diseases (GARD) Information Center was established by the National ...

Deep learning based DNA:RNA triplex forming potential prediction.

BACKGROUND: Long non-coding RNAs (lncRNAs) can exert functions via forming triplex with DNA. The cur...

DeepciRGO: functional prediction of circular RNAs through hierarchical deep neural networks using heterogeneous network features.

BACKGROUND: Circular RNAs (circRNAs) are special noncoding RNA molecules with closed loop structures...

Quantitative Structure-Mutation-Activity Relationship Tests (QSMART) model for protein kinase inhibitor response prediction.

BACKGROUND: Protein kinases are a large family of druggable proteins that are genomically and proteo...

Robot-assisted adrenalectomy: state of the art.

Currently, laparoscopic adrenalectomy is worldwide considered the gold standard technique. Both tran...

Predicting antibody affinity changes upon mutations by combining multiple predictors.

Antibodies are proteins working in our immune system with high affinity and specificity for target a...

Deep learning predicts short non-coding RNA functions from only raw sequence data.

Small non-coding RNAs (ncRNAs) are short non-coding sequences involved in gene regulation in many bi...

Spider webs inspiring soft robotics.

In soft robotics, bio-inspiration ranges from hard- to software. Orb web spiders provide excellent e...

Triage of documents containing protein interactions affected by mutations using an NLP based machine learning approach.

BACKGROUND: Information on protein-protein interactions affected by mutations is very useful for und...

Using chatbots to screen for heritable cancer syndromes in patients undergoing routine colonoscopy.

BACKGROUND: Hereditary colorectal cancer (HCRC) syndromes account for 10% of colorectal cancers but ...

Evolution-Communication Spiking Neural P Systems.

Spiking neural P systems (SNP systems) are a class of distributed and parallel computation models, w...

Prediction and prioritization of autism-associated long non-coding RNAs using gene expression and sequence features.

BACKGROUND: Autism spectrum disorders (ASD) refer to a range of neurodevelopmental conditions, which...

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