Predicting noncoding RNA and disease associations using multigraph contrastive learning.

MiRNAs and lncRNAs are two essential noncoding RNAs. Predicting associations between noncoding RNAs and diseases can significantly improve the accuracy of early diagnosis.With the continuous breakthroughs in artificial intelligence, researchers increasingly use deep learning methods to predict associations.

DGSIST: Clustering spatial transcriptome data based on deep graph structure Infomax.

Although spatial transcriptomics data provide valuable insights into gene expression profiles and the spatial structure of tissues, most studies rely solely on gene expression information, underutilizing the spatial data. To fully leverage the potential of spatial transcriptomics and graph neural networks, the DGSI (Deep Graph Structure Infomax) model is proposed. This innovative graph data processing model uses graph convolutional neural networks and employs an unsupervised learning approach.

Ethereum Phishing Scam Detection Based on Data Augmentation Method and Hybrid Graph Neural Network Model.

The rapid advancement of blockchain technology has fueled the prosperity of the cryptocurrency market. Unfortunately, it has also facilitated certain criminal activities, particularly the increasing issue of phishing scams on blockchain platforms such as Ethereum. Consequently, developing an efficient phishing detection system is critical for ensuring the security and reliability of cryptocurrency transactions.

Improved Intrusion Detection Based on Hybrid Deep Learning Models and Federated Learning.

The security of the Industrial Internet of Things (IIoT) is of vital importance, and the Network Intrusion Detection System (NIDS) plays an indispensable role in this. Although there is an increasing number of studies on the use of deep learning technology to achieve network intrusion detection, the limited local data of the device may lead to poor model performance because deep learning requires large-scale datasets for training. Some solutions propose to centralize the local datasets of devices for deep learning training, but this may involve user privacy issues.

Prediction of miRNAs and diseases association based on sparse autoencoder and MLP.

MicroRNAs (miRNAs) are small and non-coding RNA molecules which have multiple important regulatory roles within cells. With the deepening research on miRNAs, more and more researches show that the abnormal expression of miRNAs is closely related to various diseases. The relationship between miRNAs and diseases is crucial for discovering the pathogenesis of diseases and exploring new treatment methods.

Advancing single-cell RNA-seq data analysis through the fusion of multi-layer perceptron and graph neural network.

The advancement of single-cell sequencing technology has smoothed the ability to do biological studies at the cellular level. Nevertheless, single-cell RNA sequencing (scRNA-seq) data presents several obstacles due to the considerable heterogeneity, sparsity and complexity. Although many machine-learning models have been devised to tackle these difficulties, there is still a need to enhance their efficiency and accuracy.

Convolution neural network with batch normalization and inception-residual modules for Android malware classification.

Deep learning technology is changing the landscape of cybersecurity research, especially the study of large amounts of data. With the rapid growth in the number of malware, developing of an efficient and reliable method for classifying malware has become one of the research priorities. In this paper, a new method, BIR-CNN, is proposed to classify of Android malware.

TWIST1 upregulates miR-214 to promote epithelial-to-mesenchymal transition and metastasis in lung adenocarcinoma.

Epithelial-to-mesenchymal transition (EMT) is essential for the progression of non-invasive tumor cells into malignancy and metastasis. We found that miR-214 was increased in lung adenocarcinoma (LAD) and positively associated with metastasis, which was mediated by EMT. However, the mechanism whereby the overexpression of microRNAs (miRNAs), such as miR-214, promote EMT in LAD remains unclear.

Disease Sequences High-Accuracy Alignment Based on the Precision Medicine.

High-accuracy alignment of sequences with disease information contributes to disease treatment and prevention. The results of multiple sequence alignment depend on the parameters of the objective function, including gap open penalties (GOP), gap extension penalties (GEP), and substitution matrix (SM). Firstly, the theory parameter formulas relating to GOP, GAP, and SM are inferred, combining unaligned sequence length, number, and identity.

Determination of optimal parameters of MAFFT program based on BAliBASE3.0 database.

Background: Multiple sequence alignment (MSA) is one of the most important research contents in bioinformatics. A number of MSA programs have emerged. The accuracy of MSA programs highly depends on the parameters setting, mainly including gap open penalties (GOP), gap extension penalties (GEP) and substitution matrix (SM).

[Efficacy evaluation of high intensity focused ultrasound combined with intra-arterial infusion of gemcitabine in the treatment of pancreatic cancer].

Objective: The aim of this study was to discuss the clinical effectiveness of high intensity focused ultrasound (HIFU) combined with gemcitabine administered by intra-arterial infusion on intermediate and advanced pancreatic cancer.

Methods: Forty-eight patients with intermediate and advanced pancreatic cancer were divided into two groups. Twenty-four patients of the experimental group were treated by HIFU combined with gemcitabine, and 24 patients of the the HIFU group were treated by HIFU alone.

Aberrant microRNAs expression in CD133⁺/CD326⁺ human lung adenocarcinoma initiating cells from A549.

Increasing evidence demonstrates that miRNAs are involved in the dysregulation of tumor initiating cells (TICs) in various tumors. Due to a lack of definitive markers, cell sorting is not an ideal separation method for lung adenocarcinoma initiating cells. In this study, we combined paclitaxel with serum-free medium cultivation (inverse-induction) to enrich TICs from A549 cells, marked by CD133/CD326, defined features of stemness.

[Isolation and identification of cancer stem cells from primary human ovarian cancer tissues].

Objective: To isolate and identify the cancer stem cells from primary human ovarian cancer tissues.

Methods: Fresh tumor tissues from five cases of pathologically diagnosed ovarian cancers were taken, minced and then digested with collagenase and hyaluronidase to obtain single cell suspension. The erythrocytes were removed with ACK Lysis buffer.

Quantitative sequence-kinetics relationship in antigen-antibody interaction kinetics based on a set of descriptors [corrected].

The interaction between recombinant Fab57P and the coat protein of tobacco mosaic virus was studied using quantitative structure-activity relationship (QSAR) method. The development of quantitative multivariate model has shown to be a promising approach for unraveling protein-protein interactions by designed mutations in peptide sequence. This approach makes it possible to stereo-chemically determine which residue properties contribute most to the interaction.

Metabolic flux estimation using particle swarm optimization with penalty function.

Metabolic flux estimation through 13C trace experiment is crucial for quantifying the intracellular metabolic fluxes. In fact, it corresponds to a constrained optimization problem that minimizes a weighted distance between measured and simulated results. In this paper, we propose particle swarm optimization (PSO) with penalty function to solve 13C-based metabolic flux estimation problem.

Binary particle swarm optimization algorithm with mutation for multiple sequence alignment.

Multiple sequence alignment (MSA) is a fundamental and challenging problem in the analysis of biologic sequence. The MSA problem is hard to be solved directly, for it always results in exponential complexity with the scale of the problem. In this paper, we propose mutation-based binary particle swarm optimization (M-BPSO) for MSA solving.

New descriptors of amino acids and their application to peptide QSAR study.

A new set of descriptors was derived from a matrix of three structural variables of the natural amino acid, including van der Waal's volume, net charge index and hydrophobic parameter of side residues. They were selected from many properties of amino acid residues, which have been validated being the key factors to influence the interaction between peptides and its protein receptor. They were then applied to structure characterization and QSAR analysis on bitter tasting di-peptide, agiotensin-converting enzyme inhibitor and bactericidal peptides by using multiple linear regression (MLR) method.