Predicting lncRNA-protein interactions using a hybrid deep learning model with dinucleotide-codon fusion feature encoding.

Long non-coding RNAs (lncRNAs) play crucial roles in numerous biological processes and are involved in complex human diseases through interactions with proteins. Accurate identification of lncRNA-protein interactions (LPI) can help elucidate the functional mechanisms of lncRNAs and provide scientific insights into the molecular mechanisms underlying related diseases. While many sequence-based methods have been developed to predict LPIs, efficiently extracting and effectively integrating potential feature information that reflects functional attributes from lncRNA and protein sequences remains a significant challenge.

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.

A Multi-Scale Target Detection Method Using an Improved Faster Region Convolutional Neural Network Based on Enhanced Backbone and Optimized Mechanisms.

Currently, existing deep learning methods exhibit many limitations in multi-target detection, such as low accuracy and high rates of false detection and missed detections. This paper proposes an improved Faster R-CNN algorithm, aiming to enhance the algorithm's capability in detecting multi-scale targets. This algorithm has three improvements based on Faster R-CNN.

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.

A miRNA-disease association prediction model based on tree-path global feature extraction and fully connected artificial neural network with multi-head self-attention mechanism.

Background: MicroRNAs (miRNAs) emerge in various organisms, ranging from viruses to humans, and play crucial regulatory roles within cells, participating in a variety of biological processes. In numerous prediction methods for miRNA-disease associations, the issue of over-dependence on both similarity measurement data and the association matrix still hasn't been improved. In this paper, a miRNA-Disease association prediction model (called TP-MDA) based on tree path global feature extraction and fully connected artificial neural network (FANN) with multi-head self-attention mechanism is proposed.

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.

Detection of Chylous Plasma Based on Machine Learning and Hyperspectral Techniques.

Chylous blood is the main cause of unqualified and scrapped blood among volunteer blood donors. Therefore, a diagnostic method that can quickly and accurately identify chylous blood before donation is needed. In this study, the GaiaSorter "Gaia" hyperspectral sorter was used to extract 254 bands of plasma images, ranging from 900 nm to 1700 nm.

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.

A lncRNA-disease association prediction model based on the two-step PU learning and fully connected neural networks.

Long non-coding RNAs (lncRNAs) have been shown to play a regulatory role in various processes of human diseases. However, lncRNA experiments are inefficient, time-consuming and highly subjective, so that the number of experimentally verified associations between lncRNA and diseases is limited. In the era of big data, numerous machine learning methods have been proposed to predict the potential association between lncRNA and diseases, but the characteristics of the associated data were seldom explored.

Biological Activity Predictions of Ligands Based on Hybrid Molecular Fingerprinting and Ensemble Learning.

The biological activity predictions of ligands are an important research direction, which can improve the efficiency and success probability of drug screening. However, the traditional prediction method has the disadvantages of complex modeling and low screening efficiency. Machine learning is considered an important research direction to solve these traditional method problems in the near future.

Prediction of the Aqueous Solubility of Compounds Based on Light Gradient Boosting Machines with Molecular Fingerprints and the Cuckoo Search Algorithm.

Aqueous solubility is one of the most important physicochemical properties in drug discovery. At present, the prediction of aqueous solubility of compounds is still a challenging problem. Machine learning has shown great potential in solubility prediction.

Brain Magnetic Resonance Imaging Features of Nicotine-Dependent Individuals and Its Correlation with Polymorphisms of Dopamine D Receptor Gene.

The aim of this research was developed to provide a scientific basis for individualized prevention, clinical diagnosis, and corrective treatment of nicotine addiction. The objects were 214 cases in the smoke group and 43 cases in the control group. According to the Fagerstrom Nicotine Dependence Test (FTND), the smokers were divided into mild nicotine dependence group (FTND < 6 points, 138 cases) and nicotine severe dependence group (≥6 points, 76 cases).

Femtosecond Pulsed Fiber Laser by an Optical Device Based on NaOH-LPE Prepared WSe Saturable Absorber.

We report on all-optical devices prepared from WSe combined with drawn tapered fibers as saturable absorbers to achieve ultrashort pulse output. The saturable absorber with a high damage threshold and high saturable absorption characteristics is prepared for application in erbium-doped fiber lasers by the liquid phase exfoliation method for WSe, and the all-optical device exhibited strong saturable absorption characteristics with a modulation depth of 15% and a saturation intensity of 100.58 W.

Highly Sensitive Photothermal Fiber Sensor Based on MXene Device and Vernier Effect.

A photothermal fiber sensor based on a microfiber knot resonator () and the Vernier effect is proposed and demonstrated. An TiCT nanosheet was deposited onto the ring of an using an optical deposition method to prepare photothermal devices. An and a bare were used as the sensing part and reference part, respectively, of a Vernier-cascade system.

Regulatory role of miR-129 and miR-384-5p on apoptosis induced by oxygen and glucose deprivation in PC12 cell.

This study aimed to establish the role of miR-129 and miR-384-5p in cerebral ischemia-induced apoptosis. Using PC12 cells transfected with miR-129 or miR-384-5p mimics or inhibitors, oxygen glucose deprivation (OGD) conditions were applied for 4 h to simulate transient cerebral ischemia. Apoptotic phenotypes were assessed via lactate dehydrogenase (LDH) assay, MTT cell metabolism assay, and fluorescence-activated cell sorting (FACS).

Immediate effects of Zhongji point acupuncture on pelvic floor structure in female patients with stress urinary incontinence: a randomized, single-blind, and sham-controlled clinical trial protocol.

Background: Stress urinary incontinence (SUI) is defined as involuntary leakage of urine from the external urethra due to increased abdominal pressure, for example, upon sneezing, coughing, or exercise. Acupuncture is an effective therapy for patients with SUI, although objective evidence of its benefits or mechanism of action is limited. Patients with SUI often harbor structural changes of pelvic floor, the parameters of which are measurable from various perspectives and in multiple dimensions, dynamically and comprehensively, through transperineal ultrasound (TPUS).

Prediction Model of Organic Molecular Absorption Energies based on Deep Learning trained by Chaos-enhanced Accelerated Evolutionary algorithm.

As an important physical property of molecules, absorption energy can characterize the electronic property and structural information of molecules. Moreover, the accurate calculation of molecular absorption energies is highly valuable. Present linear and nonlinear methods hold low calculation accuracies due to great errors, especially irregular complicated molecular systems for structures.

Prediction of pKa Values for Neutral and Basic Drugs based on Hybrid Artificial Intelligence Methods.

The pKa value of drugs is an important parameter in drug design and pharmacology. In this paper, an improved particle swarm optimization (PSO) algorithm was proposed based on the population entropy diversity. In the improved algorithm, when the population entropy was higher than the set maximum threshold, the convergence strategy was adopted; when the population entropy was lower than the set minimum threshold the divergence strategy was adopted; when the population entropy was between the maximum and minimum threshold, the self-adaptive adjustment strategy was maintained.

Neuroprotective Effect of the Ginsenoside Rg1 on Cerebral Ischemic Injury In Vivo and In Vitro Is Mediated by PPAR-Regulated Antioxidative and Anti-Inflammatory Pathways.

The ginsenoside Rg1 exerts a neuroprotective effect during cerebral ischemia/reperfusion injury. Rg1 has been previously reported to improve PPAR expression and signaling, consequently enhancing its regulatory processes. Due to PPAR's role in the suppression of oxidative stress and inflammation, Rg1's PPAR-normalizing capacity may play a role in the observed neuroprotective action of Rg1 during ischemic brain injury.

Transcriptional and Epigenetic Regulation in Injury-Mediated Neuronal Dendritic Plasticity.

Injury to the nervous system induces localized damage in neural structures and neuronal death through the primary insult, as well as delayed atrophy and impaired plasticity of the delicate dendritic fields necessary for interneuronal communication. Excitotoxicity and other secondary biochemical events contribute to morphological changes in neurons following injury. Evidence suggests that various transcription factors are involved in the dendritic response to injury and potential therapies.

Molecular examination of bone marrow stromal cells and chondroitinase ABC-assisted acellular nerve allograft for peripheral nerve regeneration.

The present study aimed to evaluate the molecular mechanisms underlying combinatorial bone marrow stromal cell (BMSC) transplantation and chondroitinase ABC (Ch-ABC) therapy in a model of acellular nerve allograft (ANA) repair of the sciatic nerve gap in rats. Sprague Dawley rats (n=24) were used as nerve donors and Wistar rats (n=48) were randomly divided into the following groups: Group I, Dulbecco's modified Eagle's medium (DMEM) control group (ANA treated with DMEM only); Group II, Ch-ABC group (ANA treated with Ch-ABC only); Group III, BMSC group (ANA seeded with BMSCs only); Group IV, Ch-ABC + BMSCs group (Ch-ABC treated ANA then seeded with BMSCs). After 8 weeks, the expression of nerve growth factor, brain-derived neurotrophic factor and vascular endothelial growth factor in the regenerated tissues were detected by reverse transcription-quantitative polymerase chain reaction and immunohistochemistry.