Genetics

Recent methodology progress of deep learning for RNA-protein interaction prediction.

Interactions between RNAs and proteins play essential roles in many important biological processes. Benefitting from the advances of next generation sequencing technologies, hundreds of RNA-binding proteins (RBP) and their associated RNAs have been revealed, which enables the large-scale prediction of RNA-protein interactions using machine learning methods. Till now, a wide range of computational ...

Gene pathogenicity prediction of Mendelian diseases via the random forest algorithm.

The study of Mendelian diseases and the identification of their causative genes are of great significance in the field of genetics. The evaluation of the pathogenicity of genes and the total number of Mendelian disease genes are both important questions worth studying. However, very few studies have addressed these issues to date, so we attempt to answer them in this study. We calculated the gene ...

Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm.

Recent analysis identified distinct genomic subtypes of lower-grade glioma tumors which are associated with shape features. In this study, we propose ...

Hemochromatosis in India: First Report of Whole Exome Sequencing With Review of the Literature.

BACKGROUND: Primary hemochromatosis is unusual in India. The homeostatic iron regulator (HFE) gene C282Y mutation, a common cause for hemochromatosis ...

MLSeq: Machine learning interface for RNA-sequencing data.

BACKGROUND AND OBJECTIVE: In the last decade, RNA-sequencing technology has become method-of-choice and prefered to microarray technology for gene exp...

Beyond multidrug resistance: Leveraging rare variants with machine and statistical learning models in Mycobacterium tuberculosis resistance prediction.

BACKGROUND: The diagnosis of multidrug resistant and extensively drug resistant tuberculosis is a global health priority. Whole genome sequencing of c...

Deep Learning/Artificial Intelligence and Blood-Based DNA Epigenomic Prediction of Cerebral Palsy.

The etiology of cerebral palsy (CP) is complex and remains inadequately understood. Early detection of CP is an important clinical objective as this i...

Predicting miRNA-lncRNA interactions and recognizing their regulatory roles in stress response of plants.

It has been found that each non-coding RNA (ncRNA) can act not only through its target gene, but also interact with each other to act on biological tr...

Prediction of binding property of RNA-binding proteins using multi-sized filters and multi-modal deep convolutional neural network.

RNA-binding proteins (RBPs) are important in gene expression regulations by post-transcriptional control of RNAs and immune system development and its...

Cells in ExperimentaL Life Sciences (CELLS-2018): capturing the knowledge of normal and diseased cells with ontologies.

Cell cultures and cell lines are widely used in life science experiments. In conjunction with the 2018 International Conference on Biomedical Ontology...

Machine-learning based radiogenomics analysis of MRI features and metagenes in glioblastoma multiforme patients with different survival time.

BACKGROUND: This study aimed to examine multi-dimensional MRI features' predictability on survival outcome and associations with differentially expres...

PGxO and PGxLOD: a reconciliation of pharmacogenomic knowledge of various provenances, enabling further comparison.

BACKGROUND: Pharmacogenomics (PGx) studies how genomic variations impact variations in drug response phenotypes. Knowledge in pharmacogenomics is typi...

SPINDLE: End-to-end learning from EEG/EMG to extrapolate animal sleep scoring across experimental settings, labs and species.

Understanding sleep and its perturbation by environment, mutation, or medication remains a central problem in biomedical research. Its examination in ...

DNAPred: Accurate Identification of DNA-Binding Sites from Protein Sequence by Ensembled Hyperplane-Distance-Based Support Vector Machines.

Accurate identification of protein-DNA binding sites is significant for both understanding protein function and drug design. Machine-learning-based me...

A directed learning strategy integrating multiple omic data improves genomic prediction.

Genomic prediction (GP) aims to construct a statistical model for predicting phenotypes using genome-wide markers and is a promising strategy for acce...

Hybrid model for efficient prediction of poly(A) signals in human genomic DNA.

Polyadenylation signals (PAS) are found in most protein-coding and some non-coding genes in eukaryotes. Their accurate recognition improves understand...

RNA-Protein Binding Sites Prediction via Multi Scale Convolutional Gated Recurrent Unit Networks.

RNA-Protein binding plays important roles in the field of gene expression. With the development of high throughput sequencing, several conventional me...

Exon level machine learning analyses elucidate novel candidate miRNA targets in an avian model of fetal alcohol spectrum disorder.

Gestational alcohol exposure causes fetal alcohol spectrum disorder (FASD) and is a prominent cause of neurodevelopmental disability. Whole transcript...

Enzymatic Weight Update Algorithm for DNA-Based Molecular Learning.

Recent research in DNA nanotechnology has demonstrated that biological substrates can be used for computing at a molecular level. However, in vitro de...

Capsule Network Based Modeling of Multi-omics Data for Discovery of Breast Cancer-Related Genes.

Breast cancer is one of the most common cancers all over the world, which bring about more than 450,000 deaths each year. Although this malignancy has...