Genetics

SCONES: Self-Consistent Neural Network for Protein Stability Prediction Upon Mutation.

Engineering proteins to have desired properties by mutating amino acids at specific sites is commonplace. Such engineered proteins must be stable to function. Experimental methods used to determine stability at throughputs required to scan the protein sequence space thoroughly are laborious. To this end, many machine learning based methods have been developed to predict thermodynamic stability cha...

Identification of clinical trait-related small RNA biomarkers with weighted gene co-expression network analysis for personalized medicine in endocervical adenocarcinoma.

Endocervical adenocarcinoma (EAC) is an aggressive type of endocervical cancer. At present, molecular research on EAC mainly focuses on the genome and mRNA transcriptome, the investigation of small RNAs in EAC has not been fully described. Here, we systematically explored small RNAs in 14 EAC patients with different subtypes using small RNA sequencing. MiRNAs and tRNA-derived RNAs (tDRs) accounted...

Non-invasive diagnostic tool for Parkinson's disease by sebum RNA profile with machine learning.

Parkinson's disease (PD) is a progressive neurodegenerative disease presenting with motor and non-motor symptoms, including skin disorders (seborrheic...

Change of Cytokines in Chronic Hepatitis B Patients and HBeAg are Positively Correlated with HBV RNA, Based on Real-world Study.

BACKGROUND AND AIMS: The natural course of chronic hepatitis B virus (HBV) infection is widely studied; however, follow-up studies of the same patient...

GenNet framework: interpretable deep learning for predicting phenotypes from genetic data.

Applying deep learning in population genomics is challenging because of computational issues and lack of interpretable models. Here, we propose GenNet...

RDDSVM: accurate prediction of A-to-I RNA editing sites from sequence using support vector machines.

Adenosine to inosine (A-to-I) editing in RNA is involved in various biological processes like gene expression, alternative splicing, and mRNA degradat...

MIDGET:Detecting differential gene expression on microarray data.

Backgound and Objective: Detecting differentially expressed genes is an important step in genome wide analysis and expression profiling. There are a w...

Hybrid Deep Learning Based on a Heterogeneous Network Profile for Functional Annotations of Genes.

Functional annotation of unknown function genes reveals unidentified functions that can enhance our understanding of complex genome communications. A ...

Identifying digenic disease genes via machine learning in the Undiagnosed Diseases Network.

Rare diseases affect millions of people worldwide, and discovering their genetic causes is challenging. More than half of the individuals analyzed by ...

Biomolecular simulation based machine learning models accurately predict sites of tolerability to the unnatural amino acid acridonylalanine.

The incorporation of unnatural amino acids (Uaas) has provided an avenue for novel chemistries to be explored in biological systems. However, the succ...

Weakly supervised learning on unannotated H&E-stained slides predicts BRAF mutation in thyroid cancer with high accuracy.

Deep neural networks (DNNs) that predict mutational status from H&E slides of cancers can enable inexpensive and timely precision oncology. Although e...

Evaluation of deep learning approaches for modeling transcription factor sequence specificity.

As a key component of gene regulation, transcription factors (TFs) play an important role in a number of biological processes. To fully understand the...

An pipeline for the discovery of multitarget ligands: A case study for epi-polypharmacology based on DNMT1/HDAC2 inhibition.

The search for novel therapeutic compounds remains an overwhelming task owing to the time-consuming and expensive nature of the drug development proce...

Repurposing non-oncology small-molecule drugs to improve cancer therapy: Current situation and future directions.

Drug repurposing or repositioning has been well-known to refer to the therapeutic applications of a drug for another indication other than it was orig...

SWnet: a deep learning model for drug response prediction from cancer genomic signatures and compound chemical structures.

BACKGROUND: One of the major challenges in precision medicine is accurate prediction of individual patient's response to drugs. A great number of comp...

Predicting drug sensitivity of cancer cells based on DNA methylation levels.

Cancer cell lines, which are cell cultures derived from tumor samples, represent one of the least expensive and most studied preclinical models for dr...

RF-SVM: Identification of DNA-binding proteins based on comprehensive feature representation methods and support vector machine.

Protein-DNA interactions play an important role in biological progress, such as DNA replication, repair, and modification processes. In order to have ...

Diagnostic classification of coronavirus disease 2019 (COVID-19) and other pneumonias using radiomics features in CT chest images.

We propose a classification method using the radiomics features of CT chest images to identify patients with coronavirus disease 2019 (COVID-19) and o...

Deep learning detects acute myeloid leukemia and predicts NPM1 mutation status from bone marrow smears.

The evaluation of bone marrow morphology by experienced hematopathologists is essential in the diagnosis of acute myeloid leukemia (AML); however, it ...

Research on RNA secondary structure predicting via bidirectional recurrent neural network.

BACKGROUND: RNA secondary structure prediction is an important research content in the field of biological information. Predicting RNA secondary struc...