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.

Investigation of gauze and medical bottle co-pyrolysis on the product formation, reactivity, and reaction pathway of char, liquid oil, and gas.

Effective in-site treatment of medical waste has become a weak link in hospitals. Pyrolysis technology is a treatment method for medical waste that can enable rapid disposal in hospital settings and relieve environmental pressure, while also producing high-value products and reducing disposal costs. In this work, the effects of feedstock ratio and temperature on product yield and components of gauze (GA) and medical bottles (MB) co-pyrolysis have been investigated.

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.

Genome-Wide Analysis of the AP2/ERF Gene Family: Identification and Expression Profiles in Response to Cold, Salt and Drought Stress.

The AP2/ERF gene family is one of the most conserved and important transcription factor families mainly occurring in plants with various functions in regulating plant biological and physiological processes. However, little comprehensive research has been conducted on the AP2/ERF gene family in (specifically, ), an important ornamental plant. The existing whole-genome sequence of provided data to investigate the AP2/ERF genes in on a genome-wide scale.

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.

Base-resolution prediction of transcription factor binding signals by a deep learning framework.

Transcription factors (TFs) play an important role in regulating gene expression, thus the identification of the sites bound by them has become a fundamental step for molecular and cellular biology. In this paper, we developed a deep learning framework leveraging existing fully convolutional neural networks (FCN) to predict TF-DNA binding signals at the base-resolution level (named as FCNsignal). The proposed FCNsignal can simultaneously achieve the following tasks: (i) modeling the base-resolution signals of binding regions; (ii) discriminating binding or non-binding regions; (iii) locating TF-DNA binding regions; (iv) predicting binding motifs.

Contribution of reaction of atmospheric amine with sulfuric acid to mixing particle formation from clay mineral.

During dust storm, mineral particle is frequently observed to be mixed with anthropogenic pollutants (APs) and forms mixing particle which arises more complex influences on regional climate than unmixed mineral particle. Even though mixing particle formation mechanism received significant attention recently, most studies focused on the heterogeneous reaction of inorganic APs on single composition of mineral. Here, the heterogeneous reaction mechanism of amine (a proxy of organic APs) with sulfuric acid (SA) on kaolinite (Kao, a proxy of mineral dust), and its contribution to mixing particle formation are investigated under variable atmospheric conditions.

Comparative transcriptome analyses reveal genes related to pigmentation in the petals of a flower color variation cultivar of Rhododendron obtusum.

Background: Rhododendron is an important woody ornamental plant, and breeding varieties with different colors is a key research goal. Although there have been a few reports on the molecular mechanisms of flower colors and color patterning in Rhododendron, it is still largely unknown what factors regulate flower pigmentation in Rhododendron.

Methods And Results: In this study, the flower color variation cultivar 'Yanzhi Mi' and the wild-type (WT) cultivar 'Dayuanyangjin' were used as research objects, and the pigments and transcriptomes of their petals during five flower development stages were analyzed and compared.

Transcriptome and microRNA Sequencing Identified miRNAs and Target Genes in Different Developmental Stages of the Vascular Cambium in Hooibrenk.

Hooibrenk is an important fast-growing coniferous timber species that is widely used in landscaping. Recently, research on timber quality has gained substantial attention in the field of tree breeding. Wood is the secondary xylem formed by the continuous inward division and differentiation of the vascular cambium; therefore, the development of the vascular cambium is particularly important for wood quality.

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.