Current Bioinformatics - Volume 16, Issue 8, 2021

Background: Single-cell RNA sequencing techniques have emerged as effective approaches for finding the heterogeneity between cells and discovering the differentiation stage. Adaptive total variation graph regularized nonnegative matrix factorization (ATV-NMF) has been proposed to capture the inner geometric structure and determine whether to retain feature details or denoise, which is suitable for analyzing single- Read More

Aims: A number of sequence-based descriptors for proteins have been proposed by many researchers. This study aims to evaluate the performance of these descriptors in predicting protein-protein interactions on the benchmark dataset. Background: The behavior of a protein inside or outside the cell is defined by its interaction with the elements present in the surrounding environment, which include small metabolite Read More

Background: Although surgical resection generally yields excellent outcomes, a number of patients with colon cancer still have relapse or metastasis after surgery. Adjuvant chemotherapy in tumor stage III has been demonstrated to eradicate micrometastasis and improve survival, whereas the benefits of adjuvant chemotherapy in tumor stage II remain controversial. The leading cause is the lack of understanding o Read More

Background: Anti-Inflammatory Peptides (AIPs) are potent therapeutic agents for inflammatory and autoimmune disorders due to their high specificity and minimal toxicity under normal conditions. Therefore, it is greatly significant and beneficial to identify AIPs for further discovering novel and efficient AIPs-based therapeutics. Recently, three computational approaches, which can effectively identify potential AIPs, hav Read More

Background: Informative gene selection is an essential step in performing tumor classification. However, it is difficult to select informative genes related to tumors from large-scale gene expression profiles because of their characteristics, such as high dimensionality, relatively small samples, and class imbalance, and some genes are superfluous and irrelevant. Objective: Many researchers analyze and process gene expressio Read More

Background: The classification of active compounds based on their function using machine learning is essential for predicting the function of new active compounds quickly. These classification results are beneficial to accelerate the work of laboratory assistants in identifying the function of active compounds. In this study, an active compound is represented by the Simplified Molecular-Input Line-Entry System (SMILES) code. Read More

Background: Many studies have been conducted on essentiality prediction in the Saccharomyces cerevisiae genome, but the accuracy is not as high as those in bacterial or human genomes. The most frequently used features are Protein-Protein Interaction (PPI) networks combined with some other features, such as evolutionary conservation, expression level, and protein domain information. Sequence composition featu Read More

Background: RNA-binding protein plays an important role in regulating splicing, RNA transport, and other post-transcriptional processes, identifying special RNA binding domains, and interacting with RNA. Objective: This paper proposes a deep learning framework, DeepFusion-RBP, composed of three submodels. A sliding window is used to obtain sub-sequences, local features are obtained, and then the model is customized for e Read More

Background: Different cancers have different sites of origin, cell types, and forms of genetic mutations that manifest different forms of cancers. Therefore, identifying genes associated with cancer traits and analyzing their functions in different cancers is important for understanding the mechanisms of cancer. Objective: The purpose of this paper is to make up for the shortcomings of single tumor analysis and realize the Read More