Easily misdiagnosed X-linked adrenoleukodystrophy.

Background: Addison's disease and X-linked adrenoleukodystrophy (X-ALD) (Addison's-only) are two diseases that need to be identified. Addison's disease is easy to diagnose clinically when only skin and mucosal pigmentation symptoms are present. However, X-ALD (Addison's-only) caused by ABCD1 gene variation is ignored, thus losing the opportunity for early treatment.

scCorrector: a robust method for integrating multi-study single-cell data.

The advent of single-cell sequencing technologies has revolutionized cell biology studies. However, integrative analyses of diverse single-cell data face serious challenges, including technological noise, sample heterogeneity, and different modalities and species. To address these problems, we propose scCorrector, a variational autoencoder-based model that can integrate single-cell data from different studies and map them into a common space.

scInterpreter: a knowledge-regularized generative model for interpretably integrating scRNA-seq data.

Background: The rapid emergence of single-cell RNA-seq (scRNA-seq) data presents remarkable opportunities for broad investigations through integration analyses. However, most integration models are black boxes that lack interpretability or are hard to train.

Results: To address the above issues, we propose scInterpreter, a deep learning-based interpretable model.

GraphCPIs: A novel graph-based computational model for potential compound-protein interactions.

Identifying proteins that interact with drug compounds has been recognized as an important part in the process of drug discovery. Despite extensive efforts that have been invested in predicting compound-protein interactions (CPIs), existing traditional methods still face several challenges. The computer-aided methods can identify high-quality CPI candidates instantaneously.

The effect and mechanism of dl-3-n-butylphthalide on angiogenesis in a rat model of chronic myocardial ischemia.

Objective: To assess the effect of dl-3-n-butylphthalide (NBP) on angiogenesis and its underlying mechanism in a rat model of chronic myocardial ischemia (CMI).

Methods: Forty Sprague-Dawley rats were randomly divided into four groups: model, low-dose NBP (L-NBP), middle-dose NBP (M-NBP), or high-dose NBP (H-NBP) (n=10/group). All groups received intraperitoneal injections of isoprinosine hydrochloride daily for 14 days.

Predicting the Sequence Specificities of DNA-Binding Proteins by DNA Fine-Tuned Language Model With Decaying Learning Rates.

DNA-binding proteins (DBPs) play vital roles in the regulation of biological systems. Although there are already many deep learning methods for predicting the sequence specificities of DBPs, they face two challenges as follows. Classic deep learning methods for DBPs prediction usually fail to capture the dependencies between genomic sequences since their commonly used one-hot codes are mutually orthogonal.

A learning-based method to predict LncRNA-disease associations by combining CNN and ELM.

Background: lncRNAs play a critical role in numerous biological processes and life activities, especially diseases. Considering that traditional wet experiments for identifying uncovered lncRNA-disease associations is limited in terms of time consumption and labor cost. It is imperative to construct reliable and efficient computational models as addition for practice.

A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning.

Identification of drug-target interactions (DTIs) is a significant step in the drug discovery or repositioning process. Compared with the time-consuming and labor-intensive in vivo experimental methods, the computational models can provide high-quality DTI candidates in an instant. In this study, we propose a novel method called LGDTI to predict DTIs based on large-scale graph representation learning.

iMDA-BN: Identification of miRNA-disease associations based on the biological network and graph embedding algorithm.

Benefiting from advances in high-throughput experimental techniques, important regulatory roles of miRNAs, lncRNAs, and proteins, as well as biological property information, are gradually being complemented. As the key data support to promote biomedical research, domain knowledge such as intermolecular relationships that are increasingly revealed by molecular genome-wide analysis is often used to guide the discovery of potential associations. However, the method of performing network representation learning from the perspective of the global biological network is scarce.

Prediction of drug-target interactions from multi-molecular network based on LINE network representation method.

Background: The prediction of potential drug-target interactions (DTIs) not only provides a better comprehension of biological processes but also is critical for identifying new drugs. However, due to the disadvantages of expensive and high time-consuming traditional experiments, only a small section of interactions between drugs and targets in the database were verified experimentally. Therefore, it is meaningful and important to develop new computational methods with good performance for DTIs prediction.

MIPDH: A Novel Computational Model for Predicting microRNA-mRNA Interactions by DeepWalk on a Heterogeneous Network.

Analysis of miRNA-target mRNA interaction (MTI) is of crucial significance in discovering new target candidates for miRNAs. However, the biological experiments for identifying MTIs have a high false positive rate and are high-priced, time-consuming, and arduous. It is an urgent task to develop effective computational approaches to enhance the investigation of miRNA-target mRNA relationships.

Prediction of Drug-Target Interactions From Multi-Molecular Network Based on Deep Walk Embedding Model.

Predicting drug-target interactions (DTIs) is crucial in innovative drug discovery, drug repositioning and other fields. However, there are many shortcomings for predicting DTIs using traditional biological experimental methods, such as the high-cost, time-consumption, low efficiency, and so on, which make these methods difficult to widely apply. As a supplement, the method can provide helpful information for predictions of DTIs in a timely manner.

Learning Representations to Predict Intermolecular Interactions on Large-Scale Heterogeneous Molecular Association Network.

Molecular components that are functionally interdependent in human cells constitute molecular association networks. Disease can be caused by disturbance of multiple molecular interactions. New biomolecular regulatory mechanisms can be revealed by discovering new biomolecular interactions.

Bioentity2vec: Attribute- and behavior-driven representation for predicting multi-type relationships between bioentities.

Background: The explosive growth of genomic, chemical, and pathological data provides new opportunities and challenges for humans to thoroughly understand life activities in cells. However, there exist few computational models that aggregate various bioentities to comprehensively reveal the physical and functional landscape of biological systems.

Results: We constructed a molecular association network, which contains 18 edges (relationships) between 8 nodes (bioentities).

iCDA-CGR: Identification of circRNA-disease associations based on Chaos Game Representation.

Found in recent research, tumor cell invasion, proliferation, or other biological processes are controlled by circular RNA. Understanding the association between circRNAs and diseases is an important way to explore the pathogenesis of complex diseases and promote disease-targeted therapy. Most methods, such as k-mer and PSSM, based on the analysis of high-throughput expression data have the tendency to think functionally similar nucleic acid lack direct linear homology regardless of positional information and only quantify nonlinear sequence relationships.

A learning based framework for diverse biomolecule relationship prediction in molecular association network.

Abundant life activities are maintained by various biomolecule relationships in human cells. However, many previous computational models only focus on isolated objects, without considering that cell is a complete entity with ample functions. Inspired by holism, we constructed a Molecular Associations Network (MAN) including 9 kinds of relationships among 5 types of biomolecules, and a prediction model called MAN-GF.

Learning Representation of Molecules in Association Network for Predicting Intermolecular Associations.

A key aim of post-genomic biomedical research is to systematically understand molecules and their interactions in human cells. Multiple biomolecules coordinate to sustain life activities, and interactions between various biomolecules are interconnected. However, existing studies usually only focusing on associations between two or very limited types of molecules.

RPI-SE: a stacking ensemble learning framework for ncRNA-protein interactions prediction using sequence information.

Background: The interactions between non-coding RNAs (ncRNA) and proteins play an essential role in many biological processes. Several high-throughput experimental methods have been applied to detect ncRNA-protein interactions. However, these methods are time-consuming and expensive.

Integrative Construction and Analysis of Molecular Association Network in Human Cells by Fusing Node Attribute and Behavior Information.

Detecting whether a pair of biomolecules associate is of great significance in the study of molecular biology. Hence, computational methods are urgently needed as guidance for practice. However, most of the previous prediction models influenced by reductionism focused on isolated research objects, which have their own inherent defects.

Effects of Levosimendan on Right Ventricular Function in Patients with Acute Decompensated Heart Failure.

Background: To investigate the effects of levosimendan on right ventricular (RV) function in patients with acute decompensated heart failure (ADHF).

Methods: Patients with ADHF admitted from January 2017 to October 2017 were enrolled in this study. The patients were randomized to receive 24-h intravenous levosimendan or placebo.

Construction and Analysis of Molecular Association Network by Combining Behavior Representation and Node Attributes.

A key aim of post-genomic biomedical research is to systematically understand and model complex biomolecular activities based on a systematic perspective. Biomolecular interactions are widespread and interrelated, multiple biomolecules coordinate to sustain life activities, any disturbance of these complex connections can lead to abnormal of life activities or complex diseases. However, many existing researches usually only focus on individual intermolecular interactions.

A Learning-Based Method for LncRNA-Disease Association Identification Combing Similarity Information and Rotation Forest.

Long non-coding RNA (lncRNA) play critical roles in the occurrence and development of various diseases. The determination of the lncRNA-disease associations thus would contribute to provide new insights into the pathogenesis of the disease, the diagnosis, and the gene treatments. Considering that traditional experimental approaches are difficult to detect potential human lncRNA-disease associations from the vast amount of biological data, developing computational method could be of significant value.

Construction and Comprehensive Analysis of a Molecular Association Network via lncRNA-miRNA -Disease-Drug-Protein Graph.

One key issue in the post-genomic era is how to systematically describe the associations between small molecule transcripts or translations inside cells. With the rapid development of high-throughput "omics" technologies, the achieved ability to detect and characterize molecules with other molecule targets opens the possibility of investigating the relationships between different molecules from a global perspective. In this article, a molecular association network (MAN) is constructed and comprehensively analyzed by integrating the associations among miRNA, lncRNA, protein, drug, and disease, in which any kind of potential associations can be predicted.

Comparison of myocardial microcirculatory perfusion after catheter-administered intracoronary thrombolysis with anisodamine versus standard thrombus aspiration in patients with ST-elevation myocardial infarction.

Objective: To evaluate efficacy, safety and feasibility of targeted intracoronary injection using pro-urokinase combined with anisodamine (TCA) versus thrombus aspiration (TA) in ST-elevation myocardial infarction (STEMI) patients with high thrombus loads.

Background: The best method of avoiding thrombus detachment and stroke in PCI patients with high thrombus loads has not yet been established.

Methods: STEMI patients receiving coronary artery angiography or percutaneous coronary intervention (CAG/PCI) with thrombus grade ≥ 3 from January 1, 2017 to June 30, 2018 were randomly assigned to targeted intracoronary thrombolysis (pro-urokinase and anisodamine via catheter (TCA) group), or the TA group which followed the standard thrombus aspiration procedure.