Genetics

Integrative machine learning framework for the identification of cell-specific enhancers from the human genome.

Enhancers are deoxyribonucleic acid (DNA) fragments which when bound by transcription factors enhance the transcription of related genes. Due to its sporadic distribution and similar fractions, identification of enhancers from the human genome seems a daunting task. Compared to the traditional experimental approaches, computational methods with easy-to-use platforms could be efficiently applied to...

DISMIR: Deep learning-based noninvasive cancer detection by integrating DNA sequence and methylation information of individual cell-free DNA reads.

Detecting cancer signals in cell-free DNA (cfDNA) high-throughput sequencing data is emerging as a novel noninvasive cancer detection method. Due to the high cost of sequencing, it is crucial to make robust and precise predictions with low-depth cfDNA sequencing data. Here we propose a novel approach named DISMIR, which can provide ultrasensitive and robust cancer detection by integrating DNA sequ...

AniAMPpred: artificial intelligence guided discovery of novel antimicrobial peptides in animal kingdom.

With advancements in genomics, there has been substantial reduction in the cost and time of genome sequencing and has resulted in lot of data in genom...

High-resolution transcription factor binding sites prediction improved performance and interpretability by deep learning method.

Transcription factors (TFs) are essential proteins in regulating the spatiotemporal expression of genes. It is crucial to infer the potential transcri...

High-performance deep learning pipeline predicts individuals in mixtures of DNA using sequencing data.

In this study, we proposed a deep learning (DL) model for classifying individuals from mixtures of DNA samples using 27 short tandem repeats and 94 si...

Elucidation of dynamic microRNA regulations in cancer progression using integrative machine learning.

MOTIVATION: Empowered by advanced genomics discovery tools, recent biomedical research has produced a massive amount of genomic data on (post-)transcr...

RefRGim: an intelligent reference panel reconstruction method for genotype imputation with convolutional neural networks.

Genotype imputation is a statistical method for estimating missing genotypes from a denser haplotype reference panel. Existing methods usually perform...

Machine learning-enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions.

Sorghum (Sorghum bicolor) is a model C4 crop made experimentally tractable by extensive genomic and genetic resources. Biomass sorghum is studied as a...

Machine learning for identifying resistance features of using whole-genome sequence single nucleotide polymorphisms.

, a gram-negative bacterium, is a common pathogen causing nosocomial infection. The drug-resistance rate of is increasing year by year, posing a sev...

Asymmetric Three-dimensional Convolutions For Preterm Infants' Pose Estimation.

Computer-assisted tools for preterm infants' movement monitoring in neonatal intensive care unit (NICU) could support clinicians in highlighting prete...

Multi-modal deep learning of functional and structural neuroimaging and genomic data to predict mental illness.

Neuropsychiatric disorders such as schizophrenia are very heterogeneous in nature and typically diagnosed using self-reported symptoms. This makes it ...

Deep Phenotypic Cell Classification using Capsule Neural Network.

Recent developments in ultra-high-throughput microscopy have created a new generation of cell classification methodologies focused solely on image-bas...

Use of deep learning genomics to discriminate Alzheimer's disease and healthy controls.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Because gene is an impo...

Exploring deep neural networks via layer-peeled model: Minority collapse in imbalanced training.

In this paper, we introduce the , a nonconvex, yet analytically tractable, optimization program, in a quest to better understand deep neural networks ...

New approach of prediction of recurrence in thyroid cancer patients using machine learning.

Although papillary thyroid cancers are known to have a relatively low risk of recurrence, several factors are associated with a higher risk of recurre...

CoCoNet: an efficient deep learning tool for viral metagenome binning.

MOTIVATION: Metagenomic approaches hold the potential to characterize microbial communities and unravel the intricate link between the microbiome and ...

Deep and accurate detection of m6A RNA modifications using miCLIP2 and m6Aboost machine learning.

N6-methyladenosine (m6A) is the most abundant internal RNA modification in eukaryotic mRNAs and influences many aspects of RNA processing. miCLIP (m6A...

Bioinformatics and machine learning methodologies to identify the effects of central nervous system disorders on glioblastoma progression.

Glioblastoma (GBM) is a common malignant brain tumor which often presents as a comorbidity with central nervous system (CNS) disorders. Both CNS disor...

Classification and gene selection of triple-negative breast cancer subtype embedding gene connectivity matrix in deep neural network.

Triple-negative breast cancer (TNBC) has been a challenging breast cancer subtype for oncological therapy. Normally, it can be classified into differe...

Computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles.

The exploration of three-dimensional chromatin interaction and organization provides insight into mechanisms underlying gene regulation, cell differen...