Advances in biological and medical technologies have been providing us explosive
volumes of biological and physiological data, such as medical images …

Advancements in genomic research such as high-throughput sequencing techniques have
driven modern genomic studies into" big data" disciplines. This data explosion is constantly …

Motivation Histone modifications are among the most important factors that control gene
regulation. Computational methods that predict gene expression from histone modification …

Exponential growth of biomedical literature and clinical data demands more robust yet
precise computational methodologies to extract useful insights from biomedical literature …

Because of the increase in different types of diseases in human habitats, demands for
designing various types of drugs are also increasing. Protein and its structure play a very …

During the past decade, with the significant progress of computational power as well as ever-
rising data availability, deep learning techniques became increasingly popular due to their …

Convolutional neural networks (CNNs) have been successfully applied in a wide variety of
fields, from image processing to genomic sequencing. In the context of biomedical data, we …

Aptamers are short oligonucleotides (DNA/RNA) or peptide molecules that can selectively
bind to their specific targets with high specificity and affinity. As a powerful new class of …

The recent emergence of deep learning to characterize complex patterns of protein big data
reveals its potential to address the classic challenges in the field of protein data mining …

The past decade has seen a revolution in genomic technologies that enabled a flood of
genome-wide profiling of chromatin marks. Recent literature tried to understand gene …