Inferring gene regulatory networks from single-cell transcriptomics based on graph embedding.

Motivation: Gene regulatory networks (GRNs) encode gene regulation in living organisms, and have become a critical tool to understand complex biological processes. However, due to the dynamic and complex nature of gene regulation, inferring GRNs from scRNA-seq data is still a challenging task. Existing computational methods usually focus on the close connections between genes, and ignore the global structure and distal regulatory relationships.

DMFDDI: deep multimodal fusion for drug-drug interaction prediction.

Drug combination therapy has gradually become a promising treatment strategy for complex or co-existing diseases. As drug-drug interactions (DDIs) may cause unexpected adverse drug reactions, DDI prediction is an important task in pharmacology and clinical applications. Recently, researchers have proposed several deep learning methods to predict DDIs.

Predicting synergistic anticancer drug combination based on low-rank global attention mechanism and bilinear predictor.

Motivation: Drug combination therapy has exhibited remarkable therapeutic efficacy and has gradually become a promising clinical treatment strategy of complex diseases such as cancers. As the related databases keep expanding, computational methods based on deep learning model have become powerful tools to predict synergistic drug combinations. However, predicting effective synergistic drug combinations is still a challenge due to the high complexity of drug combinations, the lack of biological interpretability, and the large discrepancy in the response of drug combinations in vivo and in vitro biological systems.

Tocilizumab attenuates acute lung injury in rats with sepsis by regulating S100A12/NLRP3.

Objective: To investigate the mechanism of Tocilizumab (TCZ) in attenuating acute lung injury in rats with sepsis by regulating the S100A12/NLRP3 axis.

Methods: A rat model of sepsis was constructed using cecal ligation and puncture (CLP). Rats were treated with TCZ, and their lung tissue was collected.

Inferring Gene Regulatory Networks From Single-Cell Transcriptomic Data Using Bidirectional RNN.

Accurate inference of gene regulatory rules is critical to understanding cellular processes. Existing computational methods usually decompose the inference of gene regulatory networks (GRNs) into multiple subproblems, rather than detecting potential causal relationships simultaneously, which limits the application to data with a small number of genes. Here, we propose BiRGRN, a novel computational algorithm for inferring GRNs from time-series single-cell RNA-seq (scRNA-seq) data.

A 3D multiscale view convolutional neural network with attention for mental disease diagnosis on MRI images.

Computer Assisted Diagnosis (CAD) based on brain Magnetic Resonance Imaging (MRI) is a popular research field for the computer science and medical engineering. Traditional machine learning and deep learning methods were employed in the classification of brain MRI images in the previous studies. However, the current algorithms rarely take into consideration the influence of multi-scale brain connectivity disorders on some mental diseases.

Effects of emergency treatment mode of damage-control orthopedics in pelvic fracture complicated with multiple fractures.

Objective: This study aimed to observe the application effect of emergency treatment mode of damage-control orthopedics (DCO) in pelvic fracture complicated with multiple fractures.

Methods: Ninety-four patients with pelvic fracture complicated with multiple fractures in our hospital were recruited and divided into two groups according to the random number table method, with 47 cases in each group. Patients in the control group received traditional methods for emergency treatment (early complete treatment), and patients in the research group received DCO for emergency treatment (treatment performed in stages according to patient's physiological tolerance, with simplified initial surgery, followed by ICU resuscitation, and finally definitive surgery).

microRNA-98 protects sepsis mice from cardiac dysfunction, liver and lung injury by negatively regulating HMGA2 through inhibiting NF-κB signaling pathway.

Recently, MicroRNA-98 (miR-98) works as a biomarker in some diseases, such as lung cancer, Schizophrenia, and breast cancer, but there still lack of studies on the function of miR-98 during sepsis. Thus, our study is conducted to figure out the function of miR-98 for the regulation of cardiac dysfunction, liver and lung injury in sepsis mice. Cecum ligation and puncture was used to establish the sepsis mice model.

Efficient string similarity join in multi-core and distributed systems.

In big data area a significant challenge about string similarity join is to find all similar pairs more efficiently. In this paper, we propose a parallel processing framework for efficient string similarity join. First, the input is split into some disjoint small subsets according to the joint frequency distribution and the interval distribution of strings.

Dynamic epigenetic mode analysis using spatial temporal clustering.

Background: Differentiation of human embryonic stem cells requires precise control of gene expression that depends on specific spatial and temporal epigenetic regulation. Recently available temporal epigenomic data derived from cellular differentiation processes provides an unprecedented opportunity for characterizing fundamental properties of epigenomic dynamics and revealing regulatory roles of epigenetic modifications.

Results: This paper presents a spatial temporal clustering approach, named STCluster, which exploits the temporal variation information of epigenomes to characterize dynamic epigenetic mode during cellular differentiation.