Genetics

MSIsensor-ct: microsatellite instability detection using cfDNA sequencing data.

MOTIVATION: Microsatellite instability (MSI) is a promising biomarker for cancer prognosis and chemosensitivity. Techniques are rapidly evolving for the detection of MSI from tumor-normal paired or tumor-only sequencing data. However, tumor tissues are often insufficient, unavailable, or otherwise difficult to procure. Increasing clinical evidence indicates the enormous potential of plasma circula...

Locating transcription factor binding sites by fully convolutional neural network.

Transcription factors (TFs) play an important role in regulating gene expression, thus identification of the regions bound by them has become a fundamental step for molecular and cellular biology. In recent years, an increasing number of deep learning (DL) based methods have been proposed for predicting TF binding sites (TFBSs) and achieved impressive prediction performance. However, these methods...

jSRC: a flexible and accurate joint learning algorithm for clustering of single-cell RNA-sequencing data.

Single-cell RNA-sequencing (scRNA-seq) explores the transcriptome of genes at cell level, which sheds light on revealing the heterogeneity and dynamic...

Machine learning application for patient stratification and phenotype/genotype investigation in a rare disease.

Alkaptonuria (AKU, OMIM: 203500) is an autosomal recessive disorder caused by mutations in the Homogentisate 1,2-dioxygenase (HGD) gene. A lack of sta...

A transformer architecture based on BERT and 2D convolutional neural network to identify DNA enhancers from sequence information.

Recently, language representation models have drawn a lot of attention in the natural language processing field due to their remarkable results. Among...

Feature extraction approaches for biological sequences: a comparative study of mathematical features.

As consequence of the various genomic sequencing projects, an increasing volume of biological sequence data is being produced. Although machine learni...

Evaluation of machine learning approaches for cell-type identification from single-cell transcriptomics data.

Single-cell transcriptomics technologies have vast potential in advancing our understanding of cellular heterogeneity in complex tissues. While method...

A sequence-based deep learning approach to predict CTCF-mediated chromatin loop.

Three-dimensional (3D) architecture of the chromosomes is of crucial importance for transcription regulation and DNA replication. Various high-through...

Knowledge-based classification of fine-grained immune cell types in single-cell RNA-Seq data.

Single-cell RNA sequencing (scRNA-Seq) is an emerging strategy for characterizing immune cell populations. Compared to flow or mass cytometry, scRNA-S...

Deep embedded clustering with multiple objectives on scRNA-seq data.

In recent years, single-cell RNA sequencing (scRNA-seq) technologies have been widely adopted to interrogate gene expression of individual cells; it b...

SAResNet: self-attention residual network for predicting DNA-protein binding.

Knowledge of the specificity of DNA-protein binding is crucial for understanding the mechanisms of gene expression, regulation and gene therapy. In re...

Machine learning revealed stemness features and a novel stemness-based classification with appealing implications in discriminating the prognosis, immunotherapy and temozolomide responses of 906 glioblastoma patients.

Glioblastoma (GBM) is the most malignant and lethal intracranial tumor, with extremely limited treatment options. Immunotherapy has been widely studie...

usDSM: a novel method for deleterious synonymous mutation prediction using undersampling scheme.

Although synonymous mutations do not alter the encoded amino acids, they may impact protein function by interfering with the regulation of RNA splicin...

Improving feature selection performance for classification of gene expression data using Harris Hawks optimizer with variable neighborhood learning.

Gene expression profiling has played a significant role in the identification and classification of tumor molecules. In gene expression data, only a f...

Deep learning of gene relationships from single cell time-course expression data.

Time-course gene-expression data have been widely used to infer regulatory and signaling relationships between genes. Most of the widely used methods ...

Applying Machine Learning to Stem Cell Culture and Differentiation.

Machine learning techniques are increasingly becoming incorporated into biological research workflows in a variety of disciplines, most notably cancer...

Geometric deep learning of RNA structure.

RNA molecules adopt three-dimensional structures that are critical to their function and of interest in drug discovery. Few RNA structures are known, ...

Bound2Learn: a machine learning approach for classification of DNA-bound proteins from single-molecule tracking experiments.

DNA-bound proteins are essential elements for the maintenance, regulation, and use of the genome. The time they spend bound to DNA provides useful inf...

RNAProt: an efficient and feature-rich RNA binding protein binding site predictor.

BACKGROUND: Cross-linking and immunoprecipitation followed by next-generation sequencing (CLIP-seq) is the state-of-the-art technique used to experime...

Artificial intelligence for proteomics and biomarker discovery.

There is an avalanche of biomedical data generation and a parallel expansion in computational capabilities to analyze and make sense of these data. St...