Genetics

DeepCNPP: Deep Learning Architecture to Distinguish the Promoter of Human Long Non-Coding RNA Genes and Protein-Coding Genes.

Promoter region of protein-coding genes are gradually being well understood, yet no comparable studies exist for the promoter of long non-coding RNA (lncRNA) genes which has emerged as a global potential regulator in multiple cellular process and different diseases for human. To understand the difference in the transcriptional regulation pattern of these genes, previously, we proposed a machine le...

DeepMRMP: A new predictor for multiple types of RNA modification sites using deep learning.

RNA modification plays an indispensable role in the regulation of organisms. RNA modification site prediction offers an insight into diverse cellular processing. Regarding different types of RNA modification site prediction, it is difficult to tell the most relevant feature combinations from a variant of RNA properties. Thereby, the performance of traditional machine learning based predictors reli...

mCSM-PPI2: predicting the effects of mutations on protein-protein interactions.

Protein-protein Interactions are involved in most fundamental biological processes, with disease causing mutations enriched at their interfaces. Here ...

Genetic Neural Networks: an artificial neural network architecture for capturing gene expression relationships.

MOTIVATION: Gene expression prediction is one of the grand challenges in computational biology. The availability of transcriptomics data combined with...

Deep learning: new computational modelling techniques for genomics.

As a data-driven science, genomics largely utilizes machine learning to capture dependencies in data and derive novel biological hypotheses. However, ...

A BLSTM with Attention Network for Predicting Acute Myeloid Leukemia Patient's Prognosis using Comprehensive Clinical Parameters.

The prognosis management is crucial for highrisk disease like Acute Myeloid Leukemia (AML) in order to support decisions of clinical treatment. Howeve...

DeepTACT: predicting 3D chromatin contacts via bootstrapping deep learning.

Interactions between regulatory elements are of crucial importance for the understanding of transcriptional regulation and the interpretation of disea...

Simple tricks of convolutional neural network architectures improve DNA-protein binding prediction.

MOTIVATION: With the accumulation of DNA sequencing data, convolution neural network (CNN) based methods such as DeepBind and DeepSEA have achieved gr...

PPR-Meta: a tool for identifying phages and plasmids from metagenomic fragments using deep learning.

BACKGROUND: Phages and plasmids are the major components of mobile genetic elements, and fragments from such elements generally co-exist with chromoso...

Effect of normalization methods on the performance of supervised learning algorithms applied to HTSeq-FPKM-UQ data sets: 7SK RNA expression as a predictor of survival in patients with colon adenocarcinoma.

MOTIVATION: One of the main challenges in machine learning (ML) is choosing an appropriate normalization method. Here, we examine the effect of variou...

Enhanced prediction of RNA solvent accessibility with long short-term memory neural networks and improved sequence profiles.

MOTIVATION: The de novo prediction of RNA tertiary structure remains a grand challenge. Predicted RNA solvent accessibility provides an opportunity to...

A new method using machine learning for automated image analysis applied to chip-based digital assays.

Chip-based digital assays such as the digital polymerase chain reaction (digital PCR), digital loop-mediated amplification (digital LAMP), digital enz...

DriverML: a machine learning algorithm for identifying driver genes in cancer sequencing studies.

Although rapid progress has been made in computational approaches for prioritizing cancer driver genes, research is far from achieving the ultimate go...

Validity of Natural Language Processing for Ascertainment of and Test Results in SEER Cases of Stage IV Non-Small-Cell Lung Cancer.

PURPOSE: SEER registries do not report results of epidermal growth factor receptor () and anaplastic lymphoma kinase () mutation tests. To facilitate ...

EnsembleCNV: an ensemble machine learning algorithm to identify and genotype copy number variation using SNP array data.

The associations between diseases/traits and copy number variants (CNVs) have not been systematically investigated in genome-wide association studies ...

WHISTLE: a high-accuracy map of the human N6-methyladenosine (m6A) epitranscriptome predicted using a machine learning approach.

N 6-methyladenosine (m6A) is the most prevalent post-transcriptional modification in eukaryotes, and plays a pivotal role in various biological proces...

A parsimonious 3-gene signature predicts clinical outcomes in an acute myeloid leukemia multicohort study.

Acute myeloid leukemia (AML) is a genetically heterogeneous hematological malignancy with variable responses to chemotherapy. Although recurring cytog...

Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species.

MOTIVATION: As one of important epigenetic modifications, DNA N4-methylcytosine (4mC) is recently shown to play crucial roles in restriction-modificat...

DeepGSR: an optimized deep-learning structure for the recognition of genomic signals and regions.

MOTIVATION: Recognition of different genomic signals and regions (GSRs) in DNA is crucial for understanding genome organization, gene regulation, and ...

Machine learning approaches to decipher hormone and HER2 receptor status phenotypes in breast cancer.

Breast cancer prognosis and administration of therapies are aided by knowledge of hormonal and HER2 receptor status. Breast cancer lacking estrogen re...