Genetics

A refined cell-of-origin classifier with targeted NGS and artificial intelligence shows robust predictive value in DLBCL.

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous entity of B-cell lymphoma. Cell-of-origin (COO) classification of DLBCL is required in routine practice by the World Health Organization classification for biological and therapeutic insights. Genetic subtypes uncovered recently are based on distinct genetic alterations in DLBCL, which are different from the COO subtypes defined by gene expr...

DeepCLIP: predicting the effect of mutations on protein-RNA binding with deep learning.

Nucleotide variants can cause functional changes by altering protein-RNA binding in various ways that are not easy to predict. This can affect processes such as splicing, nuclear shuttling, and stability of the transcript. Therefore, correct modeling of protein-RNA binding is critical when predicting the effects of sequence variations. Many RNA-binding proteins recognize a diverse set of motifs an...

Machine learning and statistical methods for clustering single-cell RNA-sequencing data.

UNLABELLED: Single-cell RNAsequencing (scRNA-seq) technologies have enabled the large-scale whole-transcriptome profiling of each individual single ce...

A critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation.

A number of machine learning (ML)-based algorithms have been proposed for predicting mutation-induced stability changes in proteins. In this critical ...

A novel rat robot controlled by electrical stimulation of the nigrostriatal pathway.

OBJECTIVE: Artificial manipulation of animal movement could offer interesting advantages and potential applications using the animal's inherited super...

Imputing missing RNA-sequencing data from DNA methylation by using a transfer learning-based neural network.

BACKGROUND: Gene expression plays a key intermediate role in linking molecular features at the DNA level and phenotype. However, owing to various limi...

Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions.

MOTIVATION: RNA-protein interactions are key effectors of post-transcriptional regulation. Significant experimental and bioinformatics efforts have be...

[Study on precise mechanism of Chinese patent medicine from perspective of activating data].

Traditional Chinese medicine(TCM) syndrome differentiation and treatment has a characteristic and advantageous efficacy in the prevention and treatmen...

Artificial Neural Network enabling Clinically Meaningful Biological Image Data Generation.

Biological experiments for developing efficient cancer therapeutics require significant resources of time and costs particularly in acquiring biologic...

Evaluating the Predictability of Cancer Types from 536 Somatic Mutations: A New Dataset.

In this paper, we introduce a new dataset for cancer research containing somatic mutation states of 536 genes of the Cancer Gene Census (CGC). We used...

Predicting the pathogenicity of protein coding mutations using Natural Language Processing.

DNA-Sequencing of tumor cells has revealed thousands of genetic mutations. However, cancer is caused by only some of them. Identifying mutations that ...

Antidepressant pathways of the Chinese herb through genetic ontology analysis.

Active compounds and corresponding targets of the traditional Chinese herb, were obtained from systems pharmacological database and placed into ClueG...

[Identification of nucleosome positioning using support vector machine method based on comprehensive DNA sequence feature].

In this article, based on z-curve theory and position weight matrix (PWM), a model for nucleosome sequences was constructed. Nucleosome sequence datas...

Prediction of condition-specific regulatory genes using machine learning.

Recent advances in genomic technologies have generated data on large-scale protein-DNA interactions and open chromatin regions for many eukaryotic spe...

Uncovering the key dimensions of high-throughput biomolecular data using deep learning.

Recent advances in high-throughput single-cell RNA-seq have enabled us to measure thousands of gene expression levels at single-cell resolution. Howev...

Predicting the Landscape of Recombination Using Deep Learning.

Accurately inferring the genome-wide landscape of recombination rates in natural populations is a central aim in genomics, as patterns of linkage infl...

DNA4mC-LIP: a linear integration method to identify N4-methylcytosine site in multiple species.

MOTIVATION: DNA N4-methylcytosine (4mC) is a crucial epigenetic modification. However, the knowledge about its biological functions is limited. Effect...

A multitask multiple kernel learning formulation for discriminating early- and late-stage cancers.

MOTIVATION: Genomic information is increasingly being used in diagnosis, prognosis and treatment of cancer. The severity of the disease is usually mea...

eBreCaP: extreme learning-based model for breast cancer survival prediction.

Breast cancer is the second leading cause of death in the world. Breast cancer research is focused towards its early prediction, diagnosis, and progno...

Genome-wide prediction for complex traits under the presence of dominance effects in simulated populations using GBLUP and machine learning methods.

The aim of this study was to compare the predictive performance of the Genomic Best Linear Unbiased Predictor (GBLUP) and machine learning methods (Ra...