Genetics

Machine Learning Based Real-Time Image-Guided Cell Sorting and Classification.

Cell classification based on phenotypical, spatial, and genetic information greatly advances our understanding of the physiology and pathology of biological systems. Technologies derived from next generation sequencing and fluorescent activated cell sorting are cornerstones for cell- and genomic-based assays supporting cell classification and mapping. However, there exists a deficiency in technolo...

Identification of Hürthle cell cancers: solving a clinical challenge with genomic sequencing and a trio of machine learning algorithms.

BACKGROUND: Identification of Hürthle cell cancers by non-operative fine-needle aspiration biopsy (FNAB) of thyroid nodules is challenging. Resultingly, non-cancerous Hürthle lesions were conventionally distinguished from Hürthle cell cancers by histopathological examination of tissue following surgical resection. Reliance on histopathological evaluation requires patients to undergo surgery to obt...

Novel taxonomy-independent deep learning microbiome approach allows for accurate classification of different forensically relevant human epithelial materials.

Correct identification of different human epithelial materials such as from skin, saliva and vaginal origin is relevant in forensic casework as it pro...

MRCNN: a deep learning model for regression of genome-wide DNA methylation.

BACKGROUND: Determination of genome-wide DNA methylation is significant for both basic research and drug development. As a key epigenetic modification...

DeepHistone: a deep learning approach to predicting histone modifications.

MOTIVATION: Quantitative detection of histone modifications has emerged in the recent years as a major means for understanding such biological process...

Predicting DNA Methylation States with Hybrid Information Based Deep-Learning Model.

DNA methylation plays an important role in the regulation of some biological processes. Up to now, with the development of machine learning models, th...

Prediction of Long Non-Coding RNAs Based on Deep Learning.

With the rapid development of high-throughput sequencing technology, a large number of transcript sequences have been discovered, and how to identify ...

Analyzing DNA methylation patterns in subjects diagnosed with schizophrenia using machine learning methods.

Schizophrenia is a common mental disorder with high heritability. It is genetically complex and to date more than a hundred risk loci have been identi...

Seq2seq Fingerprint with Byte-Pair Encoding for Predicting Changes in Protein Stability upon Single Point Mutation.

The engineering of stable proteins is crucial for various industrial purposes. Several machine learning methods have been developed to predict changes...

Application of Single-Nucleotide Polymorphisms in the Diagnosis of Autism Spectrum Disorders: A Preliminary Study with Artificial Neural Networks.

Autism spectrum disorder (ASD) includes different neurodevelopmental disorders characterized by deficits in social communication, and restricted, repe...

Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning.

Epidermal growth factor receptor (EGFR) genotyping is critical for treatment guidelines such as the use of tyrosine kinase inhibitors in lung adenocar...

An Adolescent with Progressive Enlargement of Digits: Case report and proposed diagnostic criteria for macrodystrophia lipomatosa.

Macrodystrophia lipomatosa (ML) is a rare congenital non-hereditary condition caused by an increase in all mesenchymal elements. We report a 14-year-o...

Deep Collaborative Filtering for Prediction of Disease Genes.

Accurate prioritization of potential disease genes is a fundamental challenge in biomedical research. Various algorithms have been developed to solve ...

Deep-learning augmented RNA-seq analysis of transcript splicing.

A major limitation of RNA sequencing (RNA-seq) analysis of alternative splicing is its reliance on high sequencing coverage. We report DARTS (https://...

LUADpp: an effective prediction model on prognosis of lung adenocarcinomas based on somatic mutational features.

BACKGROUND: Lung adenocarcinoma is the most common type of lung cancers. Whole-genome sequencing studies disclosed the genomic landscape of lung adeno...

Enhancer prediction with histone modification marks using a hybrid neural network model.

Enhancer is a DNA sequence of a genome that controls transcription of downstream target genes. Enhancers are known to be associated with certain epige...

Biochemical, machine learning and molecular approaches for the differential diagnosis of Mucopolysaccharidoses.

This study was aimed to construct classification and regression tree (CART) model of glycosaminoglycans (GAGs) for the differential diagnosis of Mucop...

Group Lasso Regularized Deep Learning for Cancer Prognosis from Multi-Omics and Clinical Features.

Accurate prognosis of patients with cancer is important for the stratification of patients, the optimization of treatment strategies, and the design o...

Molecular Signatures of Fusion Proteins in Cancer.

Although gene fusions are recognized as driver mutations in a wide variety of cancers, the general molecular mechanisms underlying oncogenic fusion pr...

RNA sequencing and swarm intelligence-enhanced classification algorithm development for blood-based disease diagnostics using spliced blood platelet RNA.

Blood-based diagnostics tests, using individual or panels of biomarkers, may revolutionize disease diagnostics and enable minimally invasive therapy m...