Genetics

Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set.

PURPOSE: To determine whether a multivariate machine learning-based model using computer-extracted features of pre-treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict pathologic complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer patients.

Structure and Protein Interaction-Based Gene Ontology Annotations Reveal Likely Functions of Uncharacterized Proteins on Human Chromosome 17.

Understanding the function of human proteins is essential to decipher the molecular mechanisms of human diseases and phenotypes. Of the 17 470 human protein coding genes in the neXtProt 2018-01-17 database with unequivocal protein existence evidence (PE1), 1260 proteins do not have characterized functions. To reveal the function of poorly annotated human proteins, we developed a hybrid pipeline th...

Recurrent Neural Network for Predicting Transcription Factor Binding Sites.

It is well known that DNA sequence contains a certain amount of transcription factors (TF) binding sites, and only part of them are identified through...

Deep-RBPPred: Predicting RNA binding proteins in the proteome scale based on deep learning.

RNA binding protein (RBP) plays an important role in cellular processes. Identifying RBPs by computation and experiment are both essential. Recently, ...

CamurWeb: a classification software and a large knowledge base for gene expression data of cancer.

BACKGROUND: The high growth of Next Generation Sequencing data currently demands new knowledge extraction methods. In particular, the RNA sequencing g...

Prediction of RNA-protein interactions by combining deep convolutional neural network with feature selection ensemble method.

RNA-protein interaction (RPI) plays an important role in the basic cellular processes of organisms. Unfortunately, due to time and cost constraints, i...

Effective DNA binding protein prediction by using key features via Chou's general PseAAC.

DNA-binding proteins (DBPs) are responsible for several cellular functions, starting from our immunity system to the transport of oxygen. In the recen...

Dense neural networks for predicting chromatin conformation.

BACKGROUND: DNA inside eukaryotic cells wraps around histones to form the 11nm chromatin fiber that can further fold into higher-order DNA loops, whic...

New bacitracin-resistant nisin-producing strain of and its physiological characterization.

Nisin A belonging to the class I bacteriocins and produced by subsp. is widely used in many countries as highly efficient and safe preservative prev...

CaSTLe - Classification of single cells by transfer learning: Harnessing the power of publicly available single cell RNA sequencing experiments to annotate new experiments.

Single-cell RNA sequencing (scRNA-seq) is an emerging technology for profiling the gene expression of thousands of cells at the single cell resolution...

Diagnosis of T-cell-mediated kidney rejection in formalin-fixed, paraffin-embedded tissues using RNA-Seq-based machine learning algorithms.

Molecular diagnosis is being increasingly used in transplant pathology to render more objective and quantitative determinations that also provide mech...

Combining High Speed ELM Learning with a Deep Convolutional Neural Network Feature Encoding for Predicting Protein-RNA Interactions.

Emerging evidence has shown that RNA plays a crucial role in many cellular processes, and their biological functions are primarily achieved by binding...

PBMDR: A particle swarm optimization-based multifactor dimensionality reduction for the detection of multilocus interactions.

Studies on multilocus interactions have mainly investigated the associations between genetic variations from the related genes and histopathological t...

A semi-supervised deep learning method based on stacked sparse auto-encoder for cancer prediction using RNA-seq data.

BACKGROUND AND OBJECTIVE: Cancer has become a complex health problem due to its high mortality. Over the past few decades, with the rapid development ...

Machine Learning Classification and Structure-Functional Analysis of Cancer Mutations Reveal Unique Dynamic and Network Signatures of Driver Sites in Oncogenes and Tumor Suppressor Genes.

In this study, we developed two cancer-specific machine learning classifiers for prediction of driver mutations in cancer-associated genes that were v...

Gene Regulatory Relationship Mining Using Improved Three-Phase Dependency Analysis Approach.

How to mine the gene regulatory relationship and construct gene regulatory network (GRN) is of utmost interest within the whole biological community, ...

A Hybrid Feature Selection Method Based on Binary State Transition Algorithm and ReliefF.

Feature selection problems often appear in the application of data mining, which have been difficult to handle due to the NP-hard property of these pr...

WEADE: A workflow for enrichment analysis and data exploration.

Data analysis based on enrichment of Gene Ontology terms has become an important step in exploring large gene or protein expression datasets and sever...

Gene ontology enrichment analysis of congenital diaphragmatic hernia-associated genes.

Congenital diaphragmatic hernia (CDH) is a commonly occurring major congenital anomaly with a profound impact on neonatal mortality. The etiology of C...

A universal SNP and small-indel variant caller using deep neural networks.

Despite rapid advances in sequencing technologies, accurately calling genetic variants present in an individual genome from billions of short, errorfu...