Genetics

Varifocal-Net: A Chromosome Classification Approach Using Deep Convolutional Networks.

Chromosome classification is critical for karyotyping in abnormality diagnosis. To expedite the diagnosis, we present a novel method named Varifocal-Net for simultaneous classification of chromosome's type and polarity using deep convolutional networks. The approach consists of one global-scale network (G-Net) and one local-scale network (L-Net). It follows three stages. The first stage is to lear...

Gene Expression Classification of Lung Adenocarcinoma into Molecular Subtypes.

As one of the most common malignancies in the world, lung adenocarcinoma (LUAD) is currently difficult to cure. However, the advent of precision medicine provides an opportunity to improve the treatment of lung cancer. Subtyping lung cancer plays an important role in performing a specific treatment. Here, we developed a framework that combines k-means clustering, t-test, sensitivity analysis, self...

Scalable analysis of cell-type composition from single-cell transcriptomics using deep recurrent learning.

Recent advances in large-scale single-cell RNA-seq enable fine-grained characterization of phenotypically distinct cellular states in heterogeneous ti...

MetaPheno: A critical evaluation of deep learning and machine learning in metagenome-based disease prediction.

The human microbiome plays a number of critical roles, impacting almost every aspect of human health and well-being. Conditions in the microbiome have...

Incorporating microbial community data with machine learning techniques to predict feed substrates in microbial fuel cells.

The complicated interactions that occur in mixed-species biotechnologies, including biosensors, hinder chemical detection specificity. This lack of sp...

Machine Learning Prediction of DNA Charge Transport.

First-principles calculations of charge transfer in DNA molecules are computationally expensive given that conducting charge carriers interact with in...

DDOT: A Swiss Army Knife for Investigating Data-Driven Biological Ontologies.

Systems biology requires not only genome-scale data but also methods to integrate these data into interpretable models. Previously, we developed appro...

Machine learning applied to transcriptomic data to identify genes associated with feed efficiency in pigs.

BACKGROUND: To date, the molecular mechanisms that underlie residual feed intake (RFI) in pigs are unknown. Results from different genome-wide associa...

NetSurfP-2.0: Improved prediction of protein structural features by integrated deep learning.

The ability to predict local structural features of a protein from the primary sequence is of paramount importance for unraveling its function in abse...

Evolutionarily informed deep learning methods for predicting relative transcript abundance from DNA sequence.

Deep learning methodologies have revolutionized prediction in many fields and show potential to do the same in molecular biology and genetics. However...

Detection of chromosome structural variation by targeted next-generation sequencing and a deep learning application.

Molecular testing is increasingly important in cancer diagnosis. Targeted next generation sequencing (NGS) is widely accepted method but structural va...

Deep convolutional neural networks for accurate somatic mutation detection.

Accurate detection of somatic mutations is still a challenge in cancer analysis. Here we present NeuSomatic, the first convolutional neural network ap...

Machine learning approaches outperform distance- and tree-based methods for DNA barcoding of Pterocarpus wood.

Machine-learning approaches (MLAs) for DNA barcoding outperform distance- and tree-based methods on identification accuracy and cost-effectiveness to ...

A multi-task convolutional deep neural network for variant calling in single molecule sequencing.

The accurate identification of DNA sequence variants is an important, but challenging task in genomics. It is particularly difficult for single molecu...

RPITER: A Hierarchical Deep Learning Framework for ncRNA⁻Protein Interaction Prediction.

Non-coding RNAs (ncRNAs) play crucial roles in multiple fundamental biological processes, such as post-transcriptional gene regulation, and are implic...

Introducing high school students to the Gene Ontology classification system.

We present a tutorial that introduces high school students to the Gene Ontology classification system which is widely used in genomics and systems bio...

MaGIC: a machine learning tool set and web application for monoallelic gene inference from chromatin.

BACKGROUND: A large fraction of human and mouse autosomal genes are subject to random monoallelic expression (MAE), an epigenetic mechanism characteri...

Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets.

The human immunodeficiency virus (HIV) causes over a million deaths every year and has a huge economic impact in many countries. The first class of dr...

Mechanism of glucocerebrosidase activation and dysfunction in Gaucher disease unraveled by molecular dynamics and deep learning.

The lysosomal enzyme glucocerebrosidase-1 (GCase) catalyzes the cleavage of a major glycolipid glucosylceramide into glucose and ceramide. The absence...

Unlocking HDR-mediated nucleotide editing by identifying high-efficiency target sites using machine learning.

Editing individual nucleotides is a crucial component for validating genomic disease association. It is currently hampered by CRISPR-Cas-mediated "bas...