Ensemble learning based on bi-directional gated recurrent unit and convolutional neural network with word embedding module for bioactive peptide prediction.

Bioactive peptides, as small protein fragments, are essential mediators of diverse physiological activities, such as antimicrobial, anti-inflammatory, anticancer, antioxidant, and immunomodulatory functions. Despite their substantial potential in pharmaceuticals and the food industry, conventional methods for peptide classification and activity prediction are limited by high costs, time-intensive procedures, and extensive data processing requirements. Here, we present BioPepPred-DLEmb, a novel computational model integrating Convolutional Neural Networks (CNNs) and Bidirectional Gated Recurrent Units (BiGRUs), augmented with natural language processing to encode amino acids into information-dense vectors.

Religious beliefs and social class identification: micro evidence from China.

Background: Residents' sense of social class identity is of great significance for enhancing self-happiness and maintaining social stability. As a spiritual force, religious beliefs can significantly influence residents' subjective perceptions.

Methods: Based on this, using data from the 2021 Chinese General Social Survey (CGSS 2021), this paper explores the impact of religious beliefs on residents' sense of social class identity through the probit model and Ordinary Least Squares (OLS) method, and analyzes potential mechanisms.

Agrimonia pilosa ledeb alleviates inflammatory responses in diabetic kidney disease by inhibiting the JNK/p38 signaling pathway.

Inflammatory response is a key for the emergence and progression of diabetic kidney disease (DKD). Studies have proved that Agrimonia pilosa Ledeb (APL) as a traditional Chinese herbal medicine has strong anti-oxidant and anti-inflammatory effects, but how APL plays on DKD hasn't been reported. This work explored the effects and potential regulatory mechanism of APL in DKD, aiming to inspire new ideas for developing novel drugs for DKD.

A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding.

Enhancers, genomic DNA elements, regulate neighboring gene expression crucial for biological processes like cell differentiation and stress response. However, current machine learning methods for predicting DNA enhancers often underutilize hidden features in gene sequences, limiting model accuracy. Hence, this article proposes the PDCNN model, a deep learning-based enhancer prediction method.

SpatialPPI: Three-dimensional space protein-protein interaction prediction with AlphaFold Multimer.

Rapid advancements in protein sequencing technology have resulted in gaps between proteins with identified sequences and those with mapped structures. Although sequence-based predictions offer insights, they can be incomplete due to the absence of structural details. Conversely, structure-based methods face challenges with respect to newly sequenced proteins.

Parsing Netlists of Integrated Circuits from Images via Graph Attention Network.

A massive number of paper documents that include important information such as circuit schematics can be converted into digital documents by optical sensors like scanners or digital cameras. However, extracting the netlists of analog circuits from digital documents is an exceptionally challenging task. This process aids enterprises in digitizing paper-based circuit diagrams, enabling the reuse of analog circuit designs and the automatic generation of datasets required for intelligent design models in this domain.

Essential genes identification model based on sequence feature map and graph convolutional neural network.

Background: Essential genes encode functions that play a vital role in the life activities of organisms, encompassing growth, development, immune system functioning, and cell structure maintenance. Conventional experimental techniques for identifying essential genes are resource-intensive and time-consuming, and the accuracy of current machine learning models needs further enhancement. Therefore, it is crucial to develop a robust computational model to accurately predict essential genes.

Monitoring of Single-Track Melting States Based on Photodiode Signal during Laser Powder Bed Fusion.

Single track is the basis for the melt pool modeling and physics work in laser powder bed fusion (LPBF). The melting state of a single track is closely related to defects such as porosity, lack of fusion, and balling, which have a significant impact on the mechanical properties of an LPBF-created part. To ensure the reliability of part quality and repeatability, process monitoring and feedback control are emerging to improve the melting states, which is becoming a hot topic in both the industrial and academic communities.

Deep learning with explainability for characterizing age-related intrinsic differences in dynamic brain functional connectivity.

Although many deep learning models-based medical applications are performance-driven, i.e., accuracy-oriented, their explainability is more critical.

Prediction of DNA Methylation based on Multi-dimensional feature encoding and double convolutional fully connected convolutional neural network.

DNA methylation takes on critical significance to the regulation of gene expression by affecting the stability of DNA and changing the structure of chromosomes. DNA methylation modification sites should be identified, which lays a solid basis for gaining more insights into their biological functions. Existing machine learning-based methods of predicting DNA methylation have not fully exploited the hidden multidimensional information in DNA gene sequences, such that the prediction accuracy of models is significantly limited.

scRNASequest: an ecosystem of scRNA-seq analysis, visualization, and publishing.

Background: Single-cell RNA sequencing is a state-of-the-art technology to understand gene expression in complex tissues. With the growing amount of data being generated, the standardization and automation of data analysis are critical to generating hypotheses and discovering biological insights.

Results: Here, we present scRNASequest, a semi-automated single-cell RNA-seq (scRNA-seq) data analysis workflow which allows (1) preprocessing from raw UMI count data, (2) harmonization by one or multiple methods, (3) reference-dataset-based cell type label transfer and embedding projection, (4) multi-sample, multi-condition single-cell level differential gene expression analysis, and (5) seamless integration with cellxgene VIP for visualization and with CellDepot for data hosting and sharing by generating compatible h5ad files.

RNASequest: An End-to-End Reproducible RNAseq Data Analysis and Publishing Framework.

We present RNASequest, a customizable RNA sequencing (RNAseq) analysis, app management, and result publishing framework. Its three-in-one RNAseq data analysis ecosystem consists of (1) a reproducible, configurable expression analysis (EA) module, (2) multi-faceted result presentation in R Shiny, a Bookdown document and an online slide deck, and (3) a centralized data management system. In principle, following up our well-received omics data visualization tool Quickomics, RNASequest automates the differential gene expression analysis step, eases statistical model design by built-in covariates testing module, and further provides a web-based tool, ShinyOne, to manage apps powered by Quickomics and reports generated by running the pipeline on multiple projects in one place.

Voltage-dependent anion channel 2 (VDAC2) facilitates the accumulation of rice stripe virus in the vector Laodelphax striatellus.

Rice stripe virus (RSV) causes enormous losses in rice production and is transmitted by the small brown planthopper, Laodelphax striatellus, in a persistent-propagative manner. RSV accumulation within the gut lumen of the vector is indispensable for the successful transmission to rice and insects. In this study, we obtained a 1464 bp full-length cDNA of a voltage-dependent anion channel 2 from L.

Prediction of Antibody-Antigen Binding via Machine Learning: Development of Data Sets and Evaluation of Methods.

Background: The mammalian immune system is able to generate antibodies against a huge variety of antigens, including bacteria, viruses, and toxins. The ultradeep DNA sequencing of rearranged immunoglobulin genes has considerable potential in furthering our understanding of the immune response, but it is limited by the lack of a high-throughput, sequence-based method for predicting the antigen(s) that a given immunoglobulin recognizes.

Objective: As a step toward the prediction of antibody-antigen binding from sequence data alone, we aimed to compare a range of machine learning approaches that were applied to a collated data set of antibody-antigen pairs in order to predict antibody-antigen binding from sequence data.

Neutrophil extracellular traps facilitate cancer metastasis: cellular mechanisms and therapeutic strategies.

Background: The formation of neutrophil extracellular traps (NETs) was initially discovered as a novel immune response against pathogens. Recent studies have also suggested that NETs play an important role in tumor progression. This review summarizes the cellular mechanisms by which NETs promote distant metastasis and discusses the possible clinical applications targeting NETs.

Recommendations of scRNA-seq Differential Gene Expression Analysis Based on Comprehensive Benchmarking.

To guide analysts to select the right tool and parameters in differential gene expression analyses of single-cell RNA sequencing (scRNA-seq) data, we developed a novel simulator that recapitulates the data characteristics of real scRNA-seq datasets while accounting for all the relevant sources of variation in a multi-subject, multi-condition scRNA-seq experiment: the cell-to-cell variation within a subject, the variation across subjects, the variability across cell types, the mean/variance relationship of gene expression across genes, library size effects, group effects, and covariate effects. By applying it to benchmark 12 differential gene expression analysis methods (including cell-level and pseudo-bulk methods) on simulated multi-condition, multi-subject data of the 10x Genomics platform, we demonstrated that methods originating from the negative binomial mixed model such as glmmTMB and NEBULA-HL outperformed other methods. Utilizing NEBULA-HL in a statistical analysis pipeline for single-cell analysis will enable scientists to better understand the cell-type-specific transcriptomic response to disease or treatment effects and to discover new drug targets.

Construction of full-length infectious cDNA clones of two Korean isolates of turnip mosaic virus breaking resistance in Brassica napus.

In this work, two new turnip mosaic virus (TuMV) strains (Canola-12 and Canola-14) overcoming resistance in canola (Brassica napus) were isolated from a B. napus sample that showed typical TuMV-like symptoms and was collected in the city of Gimcheon, South Korea, in 2020. The complete genome sequence was determined and an infectious clone was made for each isolate.

Reassortment of Infectious Clones of Radish Mosaic Virus Shows that Systemic Necrosis in Is Determined by RNA1.

Three infectious clones of radish mosaic virus (RaMV) were generated from isolates collected in mainland Korea (RaMV-Gg) and Jeju Island (RaMV-Aa and RaMV-Bb). These isolates differed in sequences and pathogenicity. Examination of the wild-type isolates and reassortants between the genomic RNA1 and RNA2 of these three isolates revealed that severe symptoms were associated with RNA1 of isolates Aa or Gg causing systemic necrosis in , or with RNA1 of isolate Bb for induction of veinal necrosis and severe mosaic symptoms in radish.

A multi-task two-path deep learning system for predicting the invasiveness of craniopharyngioma.

Background And Objective: Craniopharyngioma is a kind of benign brain tumor in histography. However, it might be clinically aggressive and have severe manifestations, such as increased intracranial pressure, hypothalamic-pituitary dysfunction, and visual impairment. It is considered challenging for radiologists to predict the invasiveness of craniopharyngioma through MRI images.

Application of Artificial Intelligence in Diagnosis of Craniopharyngioma.

Craniopharyngioma is a congenital brain tumor with clinical characteristics of hypothalamic-pituitary dysfunction, increased intracranial pressure, and visual field disorder, among other injuries. Its clinical diagnosis mainly depends on radiological examinations (such as Computed Tomography, Magnetic Resonance Imaging). However, assessing numerous radiological images manually is a challenging task, and the experience of doctors has a great influence on the diagnosis result.

Brain Functional Connectivity Analysis via Graphical Deep Learning.

Objective: Graphical deep learning models provide a desirable way for brain functional connectivity analysis. However, the application of current graph deep learning models to brain network analysis is challenging due to the limited sample size and complex relationships between different brain regions.

Method: In this work, a graph convolutional network (GCN) based framework is proposed by exploiting the information from both region-to-region connectivities of the brain and subject-subject relationships.

Ensemble Manifold Regularized Multi-Modal Graph Convolutional Network for Cognitive Ability Prediction.

Objective: Multi-modal functional magnetic resonance imaging (fMRI) can be used to make predictions about individual behavioral and cognitive traits based on brain connectivity networks.

Methods: To take advantage of complementary information from multi-modal fMRI, we propose an interpretable multi-modal graph convolutional network (MGCN) model, incorporating both fMRI time series and functional connectivity (FC) between each pair of brain regions. Specifically, our model learns a graph embedding from individual brain networks derived from multi-modal data.

Functional connectome fingerprinting: Identifying individuals and predicting cognitive functions via autoencoder.

Functional network connectivity has been widely acknowledged to characterize brain functions, which can be regarded as "brain fingerprinting" to identify an individual from a pool of subjects. Both common and unique information has been shown to exist in the connectomes across individuals. However, very little is known about whether and how this information can be used to predict the individual variability of the brain.