Left atrial single-cell transcriptomics reveals amphiregulin as a surrogate marker for atrial fibrillation.

Atrial fibrillation (AF) is strongly associated with strokes, heart failure, and increased mortality. This study aims to identify the monocyte-macrophage heterogeneity and interactions of these cells with non-immune cells, and to identify functional biomarkers in patients with AF. Therefore, we assess the single cell landscape of left atria (LA), using a combination of single cell and nucleus RNA-seq.

Single-Cell RNA Sequencing Reveals an Immune Landscape of CD4 T Cells in Coronary Culprit Plaques With Acute Coronary Syndrome in Humans-Brief Report.

Background: Acute coronary syndrome (ACS) involves plaque-related thrombosis, causing primary ischemic cardiomyopathy or lethal arrhythmia. We previously demonstrated a unique immune landscape of myeloid cells in the culprit plaques causing ACS by using single-cell RNA sequencing. Here, we aimed to characterize T cells in a single-cell level, assess clonal expansion of T cells, and find a therapeutic target to prevent ACS.

Estimation of important binding sites in compounds that interact with proteins.

Proteins are one of the important substances in understanding biological activity, and many of them express the function by binding to other proteins or small molecules (ligands) on the molecular surface. This interaction often occurs in the hollows (pockets) on the molecular surface of the protein. It is known that when pockets are similar in structure and physical properties, they are likely to express similar functions and to bind similar ligands.

Protein-protein interaction extraction with feature selection by evaluating contribution levels of groups consisting of related features.

Background: Protein-protein interaction (PPI) extraction from published scientific articles is one key issue in biological research due to its importance in grasping biological processes. Despite considerable advances of recent research in automatic PPI extraction from articles, demand remains to enhance the performance of the existing methods.

Results: Our feature-based method incorporates the strength of many kinds of diverse features, such as lexical and word context features derived from sentences, syntactic features derived from parse trees, and features using existing patterns to extract PPIs automatically from articles.

Extraction of Protein-Protein Interaction from Scientific Articles by Predicting Dominant Keywords.

For the automatic extraction of protein-protein interaction information from scientific articles, a machine learning approach is useful. The classifier is generated from training data represented using several features to decide whether a protein pair in each sentence has an interaction. Such a specific keyword that is directly related to interaction as "bind" or "interact" plays an important role for training classifiers.

A method of searching for related literature on protein structure analysis by considering a user's intention.

Background: In recent years, with advances in techniques for protein structure analysis, the knowledge about protein structure and function has been published in a vast number of articles. A method to search for specific publications from such a large pool of articles is needed. In this paper, we propose a method to search for related articles on protein structure analysis by using an article itself as a query.

A method for supporting retrieval of articles on protein structure analysis considering users' intention.

Background: In recent years, information about protein structure and function is described in a large amount of articles. However, a naive full-text search by specific keywords often fails to find desired articles, because the articles involve the ambiguous and complicated concepts that cannot be described with uniform representation. For retrieving articles on protein structure and function, it is important to consider the relevance between structural and/or functional concepts by identifying the user's intention.

Interaction site prediction by structural similarity to neighboring clusters in protein-protein interaction networks.

Background: Recently, revealing the function of proteins with protein-protein interaction (PPI) networks is regarded as one of important issues in bioinformatics. With the development of experimental methods such as the yeast two-hybrid method, the data of protein interaction have been increasing extremely. Many databases dealing with these data comprehensively have been constructed and applied to analyzing PPI networks.