Current Bioinformatics - Volume 15, Issue 10, 2020

The era of big data and high-throughput genomic technology has enabled scientists to have a clear view of plant genomic profiles. However, it has also led to a massive need for computational tools and strategies to interpret this data. In this scenario of huge data inflow, machine learning (ML) approaches are emerging to be the most promising for analysing heterogeneous and unstructured biological datasets. Extending its app Read More

Background: De-regulation of microRNAs (miRNAs) is closely related to many complex diseases, including cancers. In The Cancer Genome Atlas (TCGA), hundreds of differentially expressed miRNAs are stored for each type of cancer, which are hard to be intuitively interpreted. To date, several miRNA set enrichment tools have been tailored to predict the potential disease associations and functions of de-regulated miRN Read More

Background: Protein is a kind of important organics in life. It is varied with its sequences, structures and functions. Protein evolutionary classification is one of the popular research topics in computational bioinformatics. Many studies have used protein sequence information to classify the evolutionary relationships of proteins. As the amount of protein sequence data increases, efficient computational tools are n Read More

Background: Chromosomal DNA contains most of the genetic information of eukaryotes and plays an important role in the growth, development and reproduction of living organisms. Most chromosomal DNA sequences are known to wrap around histones, and distinguishing these DNA sequences from ordinary DNA sequences is important for understanding the genetic code of life. The main difficulty behind this problem Read More

Aim: Robust and more accurate method for identifying transcription factor binding sites (TFBS) for gene expression. Background: Deep neural networks (DNNs) have shown promising growth in solving complex machine learning problems. Conventional techniques are comfortably replaced by DNNs in computer vision, signal processing, healthcare, and genomics. Understanding DNA sequences is always a crucial task in healt Read More

Background: Thymidylate Synthase (TS) is an important target for folic acid inhibitors such as pemetrexed, which has considerable effects on the first-line treatment, second-line treatment and maintenance therapy for patients with late-stage Non-Small Cell Lung Cancer (NSCLC). Therefore, detecting mutations in the TYMS gene encoding TS is critical in clinical applications. With the development of Next-Generation Sequenci Read More

Background: Identification of genomic markers using NGS (next-generation sequencing) technology would be valuable for guiding precision medicine treatments for pancreatic cancers. Traditional somatic mutation methods require both tumor and matched non-tumor samples. However, only tumor samples are available mostly, especially in retrospective studies. In this study, we tried to analyze the associations between c Read More

Background: Transcriptome annotation is the basis for understanding gene structures and analysing gene expression. The transcriptome annotation of many organisms such as humans is far from incomplete, due partly to the challenge in the identification of isoforms that are produced from the same gene through alternative splicing. Third generation sequencing (TGS) reads provide unprecedented opportunity for detecting isofo Read More

Background: In rural China, many natural water bodies and farmlands have been converted into fish farming ponds as an economic developmental strategy. There is still a limited understanding of how the diversity and structure of microbial communities change in nature and become managed fish pond ecosystems. Objective: We aimed to identify the changes of the diversity and structure of microbial community and driving me Read More

Background: Competing endogenous RNA (ceRNA) networks play a pivotal role in tumor diagnosis and progression. Numerous studies have explored the functional landscape and prognostic significance of ceRNA interaction within differentiated tumor cells. Objective: We propose a new perspective by exploring ceRNA networks in the process of glioblastoma stem cell (GSC) differentiation. Methods: In this study, expressio Read More

Background: Francisella tularensis is a stealth pathogen fatal for animals and humans. Ease of its propagation, coupled with high capacity for ailment and death makes it a potential candidate for biological weapon. Objective: Work related to the pathogen’s classification and factors affecting its prolonged existence in soil is limited to statistical measures. Machine learning other than conventional analysis methods ma Read More

Aims: The study aims to find a way to reduce the dimensionality of the dataset. Background: Dimensionality reduction is the key issue of the machine learning process. It does not only improve the prediction performance but also could recommend the intrinsic features and help to explore the biological expression of the machine learning “black box”. Objective: A variety of feature selection algorithms are used to se Read More

Background: Osteoarthritis (OA), one of the most important causes leading to joint disability, was considered as an untreatable disease. A series of genes were reported to regulate the pathogenesis of OA, including microRNAs, Long non-coding RNAs and Circular RNA. So far, the expression profiles and functions of lncRNAs, mRNAs, and circRNAs in OA are not fully understood. Objective: The present study aimed to identify di Read More