HemoFuse: multi-feature fusion based on multi-head cross-attention for identification of hemolytic peptides.

Hemolytic peptides are therapeutic peptides that damage red blood cells. However, therapeutic peptides used in medical treatment must exhibit low toxicity to red blood cells to achieve the desired therapeutic effect. Therefore, accurate prediction of the hemolytic activity of therapeutic peptides is essential for the development of peptide therapies.

NeuroPred-ResSE: Predicting neuropeptides by integrating residual block and squeeze-excitation attention mechanism.

Neuropeptides play crucial roles in regulating neurological function acting as signaling molecules, which provide new opportunity for developing drugs for the treatment of neurological diseases. Therefore, it is very necessary to develop a rapid and accurate prediction model for neuropeptides. Although a few prediction tools have been developed, there is room for improvement in prediction accuracy by using deep learning approach.

A deep learning model for anti-inflammatory peptides identification based on deep variational autoencoder and contrastive learning.

As a class of biologically active molecules with significant immunomodulatory and anti-inflammatory effects, anti-inflammatory peptides have important application value in the medical and biotechnology fields due to their unique biological functions. Research on the identification of anti-inflammatory peptides provides important theoretical foundations and practical value for a deeper understanding of the biological mechanisms of inflammation and immune regulation, as well as for the development of new drugs and biotechnological applications. Therefore, it is necessary to develop more advanced computational models for identifying anti-inflammatory peptides.

iACVP-MR: Accurate Identification of Anti-coronavirus Peptide based on Multiple Features Information and Recurrent Neural Network.

Background: Over the years, viruses have caused human illness and threatened human health. Therefore, it is pressing to develop anti-coronavirus infection drugs with clear function, low cost, and high safety. Anti-coronavirus peptide (ACVP) is a key therapeutic agent against coronavirus.

EACVP: An ESM-2 LM Framework Combined CNN and CBAM Attention to Predict Anti-coronavirus Peptides.

Background: The novel coronavirus pneumonia (COVID-19) outbreak in late 2019 killed millions worldwide. Coronaviruses cause diseases such as severe acute respiratory syndrome (SARS-Cov) and SARS-COV-2. Many peptides in the host defense system have antiviral activity.

AACFlow: an end-to-end model based on attention augmented convolutional neural network and flow-attention mechanism for identification of anticancer peptides.

Motivation: Anticancer peptides (ACPs) have natural cationic properties and can act on the anionic cell membrane of cancer cells to kill cancer cells. Therefore, ACPs have become a potential anticancer drug with good research value and prospect.

Results: In this article, we propose AACFlow, an end-to-end model for identification of ACPs based on deep learning.

iAVPs-ResBi: Identifying antiviral peptides by using deep residual network and bidirectional gated recurrent unit.

Human history is also the history of the fight against viral diseases. From the eradication of viruses to coexistence, advances in biomedicine have led to a more objective understanding of viruses and a corresponding increase in the tools and methods to combat them. More recently, antiviral peptides (AVPs) have been discovered, which due to their superior advantages, have achieved great impact as antiviral drugs.

TMSC-m7G: A transformer architecture based on multi-sense-scaled embedding features and convolutional neural network to identify RNA N7-methylguanosine sites.

RNA N7-methylguanosine (m7G) is a crucial chemical modification of RNA molecules, whose principal duty is to maintain RNA function and protein translation. Studying and predicting RNA N7-methylguanosine sites aid in comprehending the biological function of RNA and the development of new drug therapy regimens. In the present scenario, the efficacy of techniques, specifically deep learning and machine learning, stands out in the prediction of RNA N7-methylguanosine sites, leading to improved accuracy and identification efficiency.

Hexapartite steering based on a four-wave-mixing process with a spatially structured pump.

Multipartite Einstein-Podolsky-Rosen (EPR) steering has been widely studied, for realizing safer quantum communication. The steering properties of six spatially separated beams from the four-wave-mixing process with a spatially structured pump are investigated. Behaviors of all (1+i)/(i+1)-mode (i=1,2,3) steerings are understandable, if the role of the corresponding relative interaction strengths are taken into account.

Fibroblast exosomal TFAP2C induced by chitosan oligosaccharides promotes peripheral axon regeneration via the miR-132-5p/CAMKK1 axis.

Chitosan and its degradation product, oligosaccharides, have been shown to facilitate peripheral nerve regeneration. However, the underlying mechanisms are not well understood. In this study, we analyzed the protein expression profiles in sciatic nerves after injury using proteomics.

Identification of DNA N4-methylcytosine sites based on multi-source features and gradient boosting decision tree.

N4-methylcytosine (4 mC) is an important and common methylation which widely exists in prokaryotes. It plays a crucial role in correcting DNA replication errors and protecting host DNA against degradation by restrictive enzymes. Hence, the accurate identification for 4 mC sites is greatly significant for understanding biological functions and treating gene diseases.

Data driven adaptive Gaussian mixture model for solving Fokker-Planck equation.

The Fokker-Planck (FP) equation provides a powerful tool for describing the state transition probability density function of complex dynamical systems governed by stochastic differential equations (SDEs). Unfortunately, the analytical solution of the FP equation can be found in very few special cases. Therefore, it has become an interest to find a numerical approximation method of the FP equation suitable for a wider range of nonlinear systems.

Estimation of critical closing pressure using intravascular blood pressure of the common carotid artery.

Purpose: Radial artery, femoral artery, and aortic arterial blood pressures (ABPs) can be used to estimate cerebral critical closing pressure (CrCP) and resistance-area product (RAP). However, the use of the common carotid artery (CCA) intravascular blood pressure to estimate CrCP is unclear. Thus, using continuous ABP monitoring, we compared the CrCP and RAP estimated from CCA measurements with the corresponding values acquired from the radial artery.

Proteomic Analysis of Silk Fibroin Reveals Diverse Biological Function of Different Degumming Processing From Different Origin.

Silk, as a kind of natural fibrin, has been prepared into various biomaterials due to its excellent biocompatibility and mechanicalness. However, there are some controversies on the biocompatibility of silk fibroin (SF), especially when it coexists with sericin. In this study, two kinds of silk from Jiangsu and Zhejiang were degummed with two concentrations of NaCO solution, respectively, to obtain four kinds of silk fibroin.

Exosomal miR-673-5p from fibroblasts promotes Schwann cell-mediated peripheral neuron myelination by targeting the TSC2/mTORC1/SREBP2 axis.

Peripheral myelination is a complicated process, wherein Schwann cells (SCs) promote the formation of the myelin sheath around the axons of peripheral neurons. Fibroblasts are the second resident cells in the peripheral nerves; however, the precise function of fibroblasts in SC-mediated myelination has rarely been examined. Here, we show that exosomes derived from fibroblasts boost myelination-related gene expression in SCs.

iEnhancer-MFGBDT: Identifying enhancers and their strength by fusing multiple features and gradient boosting decision tree.

Enhancer is a non-coding DNA fragment that can be bound with proteins to activate transcription of a gene, hence play an important role in regulating gene expression. Enhancer identification is very challenging and more complicated than other genetic factors due to their position variation and free scattering. In addition, it has been proved that genetic variation in enhancers is related to human diseases.

M6A-GSMS: Computational identification of N-methyladenosine sites with GBDT and stacking learning in multiple species.

N-methyladenosine (mA) is one of the most abundant forms of RNA methylation modifications currently known. It involves a wide range of biological processes, including degradation, stability, alternative splicing, etc. Therefore, the development of convenient and efficient mA prediction technologies are urgent.

iPromoter-ET: Identifying promoters and their strength by extremely randomized trees-based feature selection.

Promoter is a region of DNA that determines the transcription of a particular gene. There are several σ factors in the RNA polymerase, which has the function of identifying the promoter and facilitating the binding of the RNA polymerase to the promoter. Owing to the importance of promoter in genome research, it is an urgent task to develop computational tool for effectively identifying promoters and their strength facing the avalanche of DNA sequences discovered in the post-genomic age.

The Influence of the Surface Topographical Cues of Biomaterials on Nerve Cells in Peripheral Nerve Regeneration: A Review.

The surface topographies of artificial implants including surface roughness, surface groove size and orientation, and surface pore size and distribution have a great influence on the adhesion, migration, proliferation, and differentiation of nerve cells in the nerve regeneration process. Optimizing the surface topographies of biomaterials can be a key strategy for achieving excellent cell performance in various applications such as nerve tissue engineering. In this review, we offer a comprehensive summary of the surface topographies of nerve implants and their effects on nerve cell behavior.

Application of Machine Learning Techniques in Drug-target Interactions Prediction.

Background: Drug-Target interactions are vital for drug design and drug repositioning. However, traditional lab experiments are both expensive and time-consuming. Various computational methods which applied machine learning techniques performed efficiently and effectively in the field.

Application of conductive PPy/SF composite scaffold and electrical stimulation for neural tissue engineering.

Electrical stimulation (ES) with conductive polymers can dramatically enhance neurite outgrowth and promote neural regeneration. However, besides ES, the practical applications of neural repair is also highly dependent on the nerve cell functionality and response to substrate conductivity. Therefore, the combination of the ES and suitable materials, such as tissue scaffolds, has been applied to facilitate treatment of neural injuries and demonstrated great potential in peripheral nerve regeneration.

Identifying DNase I hypersensitive sites using multi-features fusion and F-score features selection via Chou's 5-steps rule.

DNase I hypersensitive sites (DHSs) are regarded as those regions of chromatin that are sensitive to cleavage by the DNase I enzyme. Identification of DNase I hypersensitive sites will provide useful insights for discovering DNA's functional elements from the non-coding sequences in the biomedical research. Because of the significance for DNase I hypersensitive sites, it is indispensable to develop an accurate, fast, robust, and high-throughput automated computational model.

Modulation of myelin formation by combined high affinity with extracellular matrix structure of electrospun silk fibroin nanoscaffolds.

Remyelination is a major therapeutic goal in peripheral nerve regeneration, serving to restore function of demyelinated axons and provide neuroprotection. In order to apply myelin biogenesis strategies to peripheral nerve defects, the tissue engineered substitutes might be amenable to the promotion of this repair process. Electrospun nanofibers are considered as promising scaffolds for tissue engineering due to extracellular matrix mimicking factor and enhanced electrostatic interaction resulting in a controllable 3 D nanofibrous membrane.

Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou's PseAAC.

The prediction of subcellular localization of an apoptosis protein is still a challenging task, and existing methods mainly based on protein primary sequences. In this study, we propose a novel model called MACC-PSSM by integrating Moran autocorrelation and cross correlation with PSSM. Then a 3600-dimensional feature vector is constructed to predict apoptosis protein subcellular localization.

Identify Gram-negative bacterial secreted protein types by incorporating different modes of PSSM into Chou's general PseAAC via Kullback-Leibler divergence.

Gram-negative bacterial secreted proteins are crucial for bacterial pathogenesis by making bacteria interact with their environments. Therefore, identification of bacterial secreted proteins becomes a significant process for the research of various diseases and the corresponding drugs. In this paper, we develop a feature design model named ACCP-KL-NMF by fusing PSSM-based auto-cross correlation analysis for features extraction and nonnegative matrix factorization algorithm based on Kullback-Leibler divergence for dimensionality reduction.