GTransCYPs: an improved graph transformer neural network with attention pooling for reliably predicting CYP450 inhibitors.

State‑of‑the‑art medical studies proved that predicting CYP450 enzyme inhibitors is beneficial in the early stage of drug discovery. However, accurate machine learning-based (ML) in silico methods for predicting CYP450 inhibitors remains challenging. Here, we introduce GTransCYPs, an improved graph neural network (GNN) with a transformer mechanism for predicting CYP450 inhibitors.

ERL-ProLiGraph: Enhanced representation learning on protein-ligand graph structured data for binding affinity prediction.

Predicting Protein-Ligand Binding Affinity (PLBA) is pivotal in drug development, as accurate estimations of PLBA expedite the identification of promising drug candidates for specific targets, thereby accelerating the drug discovery process. Despite substantial advancements in PLBA prediction, developing an efficient and more accurate method remains non-trivial. Unlike previous computer-aid PLBA studies which primarily using ligand SMILES and protein sequences represented as strings, this research introduces a Deep Learning-based method, the Enhanced Representation Learning on Protein-Ligand Graph Structured data for Binding Affinity Prediction (ERL-ProLiGraph).

Advances in Protein-Ligand Binding Affinity Prediction via Deep Learning: A Comprehensive Study of Datasets, Data Preprocessing Techniques, and Model Architectures.

Background: Drug discovery is a complex and expensive procedure involving several timely and costly phases through which new potential pharmaceutical compounds must pass to get approved. One of these critical steps is the identification and optimization of lead compounds, which has been made more accessible by the introduction of computational methods, including deep learning (DL) techniques. Diverse DL model architectures have been put forward to learn the vast landscape of interaction between proteins and ligands and predict their affinity, helping in the identification of lead compounds.

Ensemble Approach to Combining Episode Prediction Models Using Sequential Circadian Rhythm Sensor Data from Mental Health Patients.

Managing mood disorders poses challenges in counseling and drug treatment, owing to limitations. Counseling is the most effective during hospital visits, and the side effects of drugs can be burdensome. Patient empowerment is crucial for understanding and managing these triggers.

ResBiGAAT: Residual Bi-GRU with attention for protein-ligand binding affinity prediction.

Protein-ligand interaction plays a crucial role in drug discovery, facilitating efficient drug development and enabling drug repurposing. Several computational algorithms, such as Graph Neural Networks and Convolutional Neural Networks, have been proposed to predict the binding affinity using the three-dimensional structure of ligands and proteins. However, there are limitations due to the need for experimental characterization of the three-dimensional structure of protein sequences, which is still lacking for some proteins.

Melittin derived peptide-drug conjugate, M-DM1, inhibits tumor progression and induces effector cell infiltration in melanoma by targeting M2 tumor-associated macrophages.

Background: Melanoma has the highest mortality rate among all the types of skin cancer. In melanoma, M2-like tumor-associated macrophages (TAMs) are associated with the invasiveness of tumor cells and a poor prognosis. Hence, the depletion or reduction of M2-TAMs is a therapeutic strategy for the inhibition of tumor progression.

Human activity recognition for analyzing stress behavior based on Bi-LSTM.

Background: Stress is one of the critical health factors that could be detected by Human Activity Recognition (HAR) which consists of physical and mental health. HAR can raise awareness of self-care and prevent critical situations. Recently, HAR used non-invasive wearable physiological sensors.

Optimal Feature Selection-Based Dental Caries Prediction Model Using Machine Learning for Decision Support System.

The high frequency of dental caries is a major public health concern worldwide. The condition is common, particularly in developing countries. Because there are no evident early-stage signs, dental caries frequently goes untreated.

Predicting Site Energy Usage Intensity Using Machine Learning Models.

Climate change is a shift in nature yet a devastating phenomenon, mainly caused by human activities, sometimes with the intent to generate usable energy required in humankind's daily life. Addressing this alarming issue requires an urge for energy consumption evaluation. Predicting energy consumption is essential for determining what factors affect a site's energy usage and in turn, making actionable suggestions to reduce wasteful energy consumption.

MMDCP: Multi-Modal Dental Caries Prediction for Decision Support System Using Deep Learning.

In recent years, healthcare has gained unprecedented attention from researchers in the field of Human health science and technology. Oral health, a subdomain of healthcare described as being very complex, is threatened by diseases like dental caries, gum disease, oral cancer, etc. The critical point is to propose an identification mechanism to prevent the population from being affected by these diseases.

Anticancer effect of verteporfin on non-small cell lung cancer via downregulation of ANO1.

Anoctamin 1 (ANO1) is a calcium-activated chloride channel found in various cell types and is overexpressed in non-small cell lung cancer (NSCLC), a major cause of cancer-related mortality. With the rising interest in development of druggable compounds for NSCLC, there has been a corresponding rise in interest in ANO1, a novel drug target for NSCLC. However, as ANO1 inhibitors that have been discovered simultaneously exhibit both the functions of an inhibition of ANO1 channel as well as a reduction of ANO1 protein levels, it is unclear which of the two functions directly causes the anticancer effect.

GCRNN: graph convolutional recurrent neural network for compound-protein interaction prediction.

Background: Compound-protein interaction prediction is necessary to investigate health regulatory functions and promotes drug discovery. Machine learning is becoming increasingly important in bioinformatics for applications such as analyzing protein-related data to achieve successful solutions. Modeling the properties and functions of proteins is important but challenging, especially when dealing with predictions of the sequence type.

Prediction of trehalose-metabolic pathway and comparative analysis of KEGG, MetaCyc, and RAST databases based on complete genome of Variovorax sp. PAMC28711.

Background: Metabolism including anabolism and catabolism is a prerequisite phenomenon for all living organisms. Anabolism refers to the synthesis of the entire compound needed by a species. Catabolism refers to the breakdown of molecules to obtain energy.

LLAD: Life-Log Anomaly Detection Based on Recurrent Neural Network LSTM.

Life-Log is a term used for the daily monitoring of health conditions and recognizing anomalies from data generated by sensor devices. The development of smart sensors enables collection of health data, which can be considered as a solution to risks associated with personal healthcare by raising awareness regarding health conditions and wellness. Therefore, Life-Log analysis methods are important for real-life monitoring and anomaly detection.

Sex-specific metabolic interactions between liver and adipose tissue in MCD diet-induced non-alcoholic fatty liver disease.

Higher susceptibility to metabolic disease in male exemplifies the importance of sexual dimorphism in pathogenesis. We hypothesized that the higher incidence of non-alcoholic fatty liver disease in males involves sex-specific metabolic interactions between liver and adipose tissue. In the present study, we used a methionine-choline deficient (MCD) diet-induced fatty liver mouse model to investigate sex differences in the metabolic response of the liver and adipose tissue.

Dynamic access control model for privacy preserving personalized healthcare in cloud environment.

When sharing and storing healthcare data in a cloud environment, access control is a central issue for preserving data privacy as a patient's personal health data may be accessed without permission from many stakeholders. Specifically, dynamic authorization for the access of data is required because personal health data is stored in cloud storage via wearable devices. Therefore, we propose a dynamic access control model for preserving the privacy of personal healthcare data in a cloud environment.

Life data monitoring and analysis model for personalized healthcare.

As the focus of personal healthcare shifts from patient treatment to early detection and prevention, it is becoming increasingly important to manage personal wellness in our daily lives. Personal health monitoring of physical activities and status can be used to show users the distribution of their daily activities, making it easier for people to assess their health, adopt better lifestyles, and potentially decrease the occurrence of chronic diseases. In this paper, we propose a CA5W1HOnto-based life data monitoring model that provides basic monitored information from various devices and ensures preventive and proactive service for personalized healthcare.

Carotenogenesis in Haematococcus lacustris: role of protein tyrosine phosphatases.

In the present study, we examined the inhibitory effects of protein tyrosine phosphatase (PTPase) inhibitors, including sodium orthovanadate (SOV), ammonium molybdate (AM), and iodoacetamide (IA), on cell growth, accumulation of astaxanthin, and PTPase activity in the photosynthetic algae Haematococcus lacustris. PTPase activity was assayed spectrophotometrically and was found to be inhibited 60% to 90% after treatment with the inhibitors. SOV markedly abolished PTPase activity, significantly activating the accumulation of astaxanthin.

Statistical optimization of the growth factors for Chaetoceros neogracile using fractional factorial design and central composite design.

Statistical experimental designs; involving (i) a fractional factorial design (FFD) and (ii) a central composite design (CCD) were applied to optimize the culture medium constituents for production of a unique antifreeze protein by the Antartic microalgae Chaetoceros neogracile. The results of the FFD suggested that NaCl, KCl, MgCl2, and Na2SiO3 were significant variables that highly influenced the growth rate and biomass production. The optimum culture medium for the production of an antifreeze protein from C.

Selective control of the Prorocentrum minimum harmful algal blooms by a novel algal-lytic bacterium Pseudoalteromonas haloplanktis AFMB-008041.

In this study, we examined the algal-lytic activities and biological control mechanisms of Pseudoalteromonas haloplanktis AFMB-08041, which was isolated from surface seawater obtained at Masan Bay in Korea. In addition, we assessed whether AFMB-08041 could be used as a biocontrol agent to regulate harmful dinoflagellate Prorocentrum minimum. From these experiments, we found that the inoculation of AFMB-08041 at a final density of 2.

Inhibitory Effect of Algal Extracts on Mycelial Growth of the Tomato-Wilt Pathogen, Fusarium oxysporum f. sp. lycopersici.

The present study was undertaken to explore the inhibitory effect of cyanobacterial extracts of Nostoc commune FA-103 against the tomato-wilt pathogen, Fusarium oxysporum f. sp. lycopersici.

Purification and characterization of extracellular beta-glucosidase from Sinorhizobium kostiense AFK-13 and its algal lytic effect on Anabaena flos-aquae.

A beta-glucosidase from the algal lytic bacterium Sinorhizobium kostiense AFK-13, grown in complex media containing cellobiose, was purified to homogeneity by successive ammonium sulfate precipitation, and anion-exchange and gel-filtration chromatographies. The enzyme was shown to be a monomeric protein with an apparent molecular mass of 52 kDa and isoelectric point of approximately 5.4.

Purification and Characterization of a Keratinase from a Feather-Degrading Fungus, Aspergillus flavus Strain K-03.

A keratinolytic enzyme secreted by Aspergillus flavus K-03 cultured in feather meal basal medium (FMBM) containing 2% (w/v) chicken feather was purified and characterized. Keratinolytic enzyme secretion was the maximal at day 16 of the incubation period at pH 8 and 28℃. No relationship was detected between enzyme yield and increase of fungal biomass.

Inhibition of Aflatoxin Production of Aspergillus flavus by Lactobacillus casei.

Lactobacillus casei KC-324 was tested for its ability to inhibit aflatoxin production and mycelial growth of Aspergillus flavus ATCC 15517 in liquid culture. Aflatoxin B1 biosynthesis and mycelial growth were inhibited in both simultaneous culture and individual antagonism assays,suggesting that the inhibitory activity was due to extracellular metabolites produced in cell-free supernatant fluids of the cultured broth of L. casei KC-324.

Screening of Cyanobacteria (Blue-Green algae) from Rice Paddy Soil for Antifungal Activity against Plant Pathogenic Fungi.

Soil cyanobacteria isolated from the rice paddy fields of 10 different locations across Korea were evaluated by agar plate diffusion test for antifungal activity. Aqueous, petroleum ether, and methanol extracts from one hundred and forty two cyanobacterial strains belonging to the 14 genera were examined for antifungal properties against seven phytopathogenic fungi causing diseases in hot pepper (Capsicum annuum L). Of total cyanobacteria, nine cyanobacteria (6.