Genetics

Predicting RNA SHAPE scores with deep learning.

Secondary structure prediction approaches rely typically on models of equilibrium free energies that are themselves based on in vitro physical chemistry. Recent transcriptome-wide experiments of in vivo RNA structure based on SHAPE-MaP experiments provide important information that may make it possible to extend current in vitro-based RNA folding models in order to improve the accuracy of computat...

Beyond the limitation of targeted therapy: Improve the application of targeted drugs combining genomic data with machine learning.

Precision oncology involves effectively selecting drugs for cancer patients and planning an effective treatment regimen. However, for Molecular targeted drug, using genomic state of the drug target to select drugs has limitations. Many patients who could benefit from molecularly targeted drugs, but they are being missed due to the insufficient labelling ability of the existing target genes. For no...

A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy.

BACKGROUND: For breast cancer patients undergoing neoadjuvant chemotherapy (NAC), pathologic complete response (pCR; no invasive or in situ) cannot be...

Anatomical pathology (human structural biopathology) in the era of "Big Data", digitalization, 5G and artificial intelligence: Evolution or Revolution?

A proposal of an updated system of the Organization of Scientific Biomedical Kowledge is presented, integrating the historical achievements in patholo...

Machine Learning and Multiparametric Brain MRI to Differentiate Hereditary Diffuse Leukodystrophy with Spheroids from Multiple Sclerosis.

BACKGROUND AND PURPOSE: Hereditary diffuse leukoencephalopathy with spheroids (HDLS) and multiple sclerosis (MS) are demyelinating and neurodegenerati...

Artificial intelligence in gastroenterology: where are we heading?

Artificial intelligence (AI) is coming to medicine in a big wave. From making diagnosis in various medical conditions, following the latest advancemen...

Predicting host taxonomic information from viral genomes: A comparison of feature representations.

The rise in metagenomics has led to an exponential growth in virus discovery. However, the majority of these new virus sequences have no assigned host...

Identifying barley pan-genome sequence anchors using genetic mapping and machine learning.

We identified 1.844 million barley pan-genome sequence anchors from 12,306 genotypes using genetic mapping and machine learning. There is increasing e...

CHEER: HierarCHical taxonomic classification for viral mEtagEnomic data via deep leaRning.

The fast accumulation of viral metagenomic data has contributed significantly to new RNA virus discovery. However, the short read size, complex compos...

LPI-CNNCP: Prediction of lncRNA-protein interactions by using convolutional neural network with the copy-padding trick.

Long noncoding RNAs (lncRNAs) play critical roles in many pathological and biological processes, such as post-transcription, cell differentiation and ...

Rosetta custom score functions accurately predict ΔΔG of mutations at protein-protein interfaces using machine learning.

Protein-protein interfaces play essential roles in a variety of biological processes and many therapeutic molecules are targeted at these interfaces. ...

A biochemically-interpretable machine learning classifier for microbial GWAS.

Current machine learning classifiers have successfully been applied to whole-genome sequencing data to identify genetic determinants of antimicrobial ...

Combining gene expression profiling and machine learning to diagnose B-cell non-Hodgkin lymphoma.

Non-Hodgkin B-cell lymphomas (B-NHLs) are a highly heterogeneous group of mature B-cell malignancies. Their classification thus requires skillful eval...

Prediction of breast cancer proteins involved in immunotherapy, metastasis, and RNA-binding using molecular descriptors and artificial neural networks.

Breast cancer (BC) is a heterogeneous disease where genomic alterations, protein expression deregulation, signaling pathway alterations, hormone disru...

A novel graph attention adversarial network for predicting disease-related associations.

Identifying complex human diseases at molecular level is very helpful, especially in diseases diagnosis, therapy, prognosis and monitoring. Accumulati...

Uncovering the prognostic gene signatures for the improvement of risk stratification in cancers by using deep learning algorithm coupled with wavelet transform.

BACKGROUND: The aim of gene expression-based clinical modelling in tumorigenesis is not only to accurately predict the clinical endpoints, but also to...

Drug Resistance Prediction Using Deep Learning Techniques on HIV-1 Sequence Data.

The fast replication rate and lack of repair mechanisms of human immunodeficiency virus (HIV) contribute to its high mutation frequency, with some mut...

Jupyter notebook-based tools for building structured datasets from the Sequence Read Archive.

The Sequence Read Archive (SRA) is a large public repository that stores raw next-generation sequencing data from thousands of diverse scientific inve...

Putative cell type discovery from single-cell gene expression data.

We present the Single-Cell Clustering Assessment Framework, a method for the automated identification of putative cell types from single-cell RNA sequ...

Single-cell transcriptome reveals the novel role of T-bet in suppressing the immature NK gene signature.

The transcriptional activation and repression during NK cell ontology are poorly understood. Here, using single-cell RNA-sequencing, we reveal a novel...