NaCTR: Natural product-derived compound-based drug discovery pipeline from traditional oriental medicine by search space reduction.

The drug discovery pipelines require enormous time and cost, albeit their infamously high risk of failures. Reducing such risk has therefore been the utmost goal in the process. Recently, natural products (NPs) in traditional oriental medicine (TOM) have come into the spotlight for their efficacy and safety supported throughout the history.

Drug repurposing for Parkinson's disease by biological pathway based edge-weighted network proximity analysis.

Parkinson's disease is the second most frequent neurodegenerative disease, and its severity is increasing with extended life expectancy. Most of current treatments provide symptomatic relief; however, disease progression is not inhibited. There are multiple trials for treatments that target the causes of the disease but they were flawed.

Transient colonizing microbes promote gut dysbiosis and functional impairment.

Species composition of the healthy adult gut microbiota tends to be stable over time. Destabilization of the gut microbiome under the influence of different factors is the main driver of the microbial dysbiosis and subsequent impacts on host physiology. Here, we used metagenomics data from a Swedish longitudinal cohort, to determine the stability of the gut microbiome and uncovered two distinct microbial species groups; persistent colonizing species (PCS) and transient colonizing species (TCS).

Data Mining and in Silico Analysis of Ethiopian Traditional Medicine: Unveiling the Therapeutic Potential of Rumex abyssinicus Jacq.

Multicomponent traditional medicine prescriptions are widely used in Ethiopia for disease treatment. However, inconsistencies across practitioners, cultures, and locations have hindered the development of reliable therapeutic medicines. Systematic analysis of traditional medicine data is crucial for identifying consistent and reliable medicinal materials.

Global compositional and functional states of the human gut microbiome in health and disease.

The human gut microbiota is of increasing interest, with metagenomics a key tool for analyzing bacterial diversity and functionality in health and disease. Despite increasing efforts to expand microbial gene catalogs and an increasing number of metagenome-assembled genomes, there have been few pan-metagenomic association studies and in-depth functional analyses across different geographies and diseases. Here, we explored 6014 human gut metagenome samples across 19 countries and 23 diseases by performing compositional, functional cluster, and integrative analyses.

CancerGATE: Prediction of cancer-driver genes using graph attention autoencoders.

Discovery of the cancer type specific-driver genes is important for understanding the molecular mechanisms of each cancer type and for providing proper treatment. Recently, graph deep learning methods became widely used in finding cancer-driver genes. However, previous methods had limited performance in individual cancer types due to a small number of cancer-driver genes used in training and biases toward the cancer-driver genes used in training the models.

Designing combinational herbal drugs based on target space analysis.

Background: Traditional oriental medicines (TOMs) are a medical practice that follows different philosophies to pharmaceutical drugs and they have been in use for many years in different parts of the world. In this study, by integrating TOM formula and pharmaceutical drugs, we performed target space analysis between TOM formula target space and small-molecule drug target space. To do so, we manually curated 46 TOM formulas that are known to treat Anxiety, Diabetes mellitus, Epilepsy, Hypertension, Obesity, and Schizophrenia.

Community cohesion looseness in gene networks reveals individualized drug targets and resistance.

Community cohesion plays a critical role in the determination of an individual's health in social science. Intriguingly, a community structure of gene networks indicates that the concept of community cohesion could be applied between the genes as well to overcome the limitations of single gene-based biomarkers for precision oncology. Here, we develop community cohesion scores which precisely quantify the community ability to retain the interactions between the genes and their cellular functions in each individualized gene network.

Interpretation of SNP combination effects on schizophrenia etiology based on stepwise deep learning with multi-precision data.

Schizophrenia genome-wide association studies (GWAS) have reported many genomic risk loci, but it is unclear how they affect schizophrenia susceptibility through interactions of multiple SNPs. We propose a stepwise deep learning technique with multi-precision data (SLEM) to explore the SNP combination effects on schizophrenia through intermediate molecular and cellular functions. The SLEM technique utilizes two levels of precision data for learning.

Combination of UHPLC-MS/MS with context-specific network and cheminformatic approaches for identifying bioactivities and active components of propolis.

Discovering new bioactivities and identifying active compounds of food materials are major fields of study in food science. However, the process commonly requires extensive experiments and can be technically challenging. In the current study, we employed network biology and cheminformatic approaches to predict new target diseases, active components, and related molecular mechanisms of propolis.

Identifying prognostic subgroups of luminal-A breast cancer using deep autoencoders and gene expressions.

Luminal-A breast cancer is the most frequently occurring subtype which is characterized by high expression levels of hormone receptors. However, some luminal-A breast cancer patients suffer from intrinsic and/or acquired resistance to endocrine therapies which are considered as first-line treatments for luminal-A breast cancer. This heterogeneity within luminal-A breast cancer has required a more precise stratification method.

Reverse tracking from drug-induced transcriptomes through multilayer molecular networks reveals hidden drug targets.

Identifying molecular targets of a drug is an essential process for drug discovery and development. The recent in-silico approaches are usually based on the structure information of chemicals and proteins. However, 3D structure information is hard to obtain and machine-learning methods using 2D structure suffer from data imbalance problem.

Large-scale prediction of adverse drug reactions-related proteins with network embedding.

Motivation: Adverse drug reactions (ADRs) are a major issue in drug development and clinical pharmacology. As most ADRs are caused by unintended activity at off-targets of drugs, the identification of drug targets responsible for ADRs becomes a key process for resolving ADRs. Recently, with the increase in the number of ADR-related data sources, several computational methodologies have been proposed to analyze ADR-protein relations.

In silico activity and ADMET profiling of phytochemicals from Ethiopian indigenous aloes using pharmacophore models.

In silico profiling is used in identification of active compounds and guide rational use of traditional medicines. Previous studies on Ethiopian indigenous aloes focused on documentation of phytochemical compositions and traditional uses. In this study, ADMET and drug-likeness properties of phytochemicals from Ethiopian indigenous aloes were evaluated, and pharmacophore-based profiling was done using Discovery Studio to predict therapeutic targets.

Highly Adsorptive Au-TiO Nanocomposites for the SERS Face Mask Allow the Machine-Learning-Based Quantitative Assay of SARS-CoV-2 in Artificial Breath Aerosols.

Human respiratory aerosols contain diverse potential biomarkers for early disease diagnosis. Here, we report the direct and label-free detection of SARS-CoV-2 in respiratory aerosols using a highly adsorptive Au-TiO nanocomposite SERS face mask and an ablation-assisted autoencoder. The Au-TiO SERS face mask continuously preconcentrates and efficiently captures the oronasal aerosols, which substantially enhances the SERS signal intensities by 47% compared to simple Au nanoislands.

Combining empirical knowledge, in silico molecular docking and ADMET profiling to identify therapeutic phytochemicals from Brucea antidysentrica for acute myeloid leukemia.

Acute myeloid leukemia (AML) is one of the deadly cancers. Chemotherapy is the first-line treatment and the only curative intervention is stem cell transplantation which are intolerable for aged and comorbid patients. Therefore, finding complementary treatment is still an active research area.

Computational Discovery of Cancer Immunotherapy Targets by Intercellular CRISPR Screens.

Cancer immunotherapy targets the interplay between immune and cancer cells. In particular, interactions between cytotoxic T lymphocytes (CTLs) and cancer cells, such as PD-1 () binding PD-L1 (), are crucial for cancer cell clearance. However, immune checkpoint inhibitors targeting these interactions are effective only in a subset of patients, requiring the identification of novel immunotherapy targets.

Plant phenotype relationship corpus for biomedical relationships between plants and phenotypes.

Medicinal plants have demonstrated therapeutic potential for applicability for a wide range of observable characteristics in the human body, known as "phenotype," and have been considered favorably in clinical treatment. With an ever increasing interest in plants, many researchers have attempted to extract meaningful information by identifying relationships between plants and phenotypes from the existing literature. Although natural language processing (NLP) aims to extract useful information from unstructured textual data, there is no appropriate corpus available to train and evaluate the NLP model for plants and phenotypes.

Predicting Ischemic Stroke in Patients with Atrial Fibrillation Using Machine Learning.

Background: Atrial fibrillation (AF) is a well-known risk factor for stroke. Predicting the risk is important to prevent the first and secondary attacks of cerebrovascular diseases by determining early treatment. This study aimed to predict the ischemic stroke in AF patients based on the massive and complex Korean National Health Insurance (KNHIS) data through a machine learning approach.

Paeonia lactiflora extract improves the muscle function of mdx mice, an animal model of Duchenne muscular dystrophy, via downregulating the high mobility group box 1 protein.

Ethnopharmacological Relevance: Paeonia lactiflora Pall. is an ethnopharmacological medicine with a long history of human use for treating various inflammatory diseases in many Asian countries.

Aim Of The Study: Duchenne muscular dystrophy (DMD) is an X-linked degenerative muscle disease affecting 1 in 3500 males and is characterized by severe muscle inflammation and a progressive decline in muscle function.

Context-aware multi-token concept recognition of biological entities.

Background: Concept recognition is a term that corresponds to the two sequential steps of named entity recognition and named entity normalization, and plays an essential role in the field of bioinformatics. However, the conventional dictionary-based methods did not sufficiently addressed the variation of the concepts in actual use in literature, resulting in the particularly degraded performances in recognition of multi-token concepts.

Results: In this paper, we propose a concept recognition method of multi-token biological entities using neural models combined with literature contexts.

A Deep Learning-Based Approach for Identifying the Medicinal Uses of Plant-Derived Natural Compounds.

Medicinal plants and their extracts have been used as important sources for drug discovery. In particular, plant-derived natural compounds, including phytochemicals, antioxidants, vitamins, and minerals, are gaining attention as they promote health and prevent disease. Although several methods have been developed to confirm the biological activities of natural compounds, there is still considerable room to reduce time and cost.

EGR1/GADD45α Activation by ROS of Non-Thermal Plasma Mediates Cell Death in Thyroid Carcinoma.

(1) Background: Nonthermal plasma (NTP) induces cell death in various types of cancer cells, providing a promising alternative treatment strategy. Although recent studies have identified new mechanisms of NTP in several cancers, the molecular mechanisms underlying its therapeutic effect on thyroid cancer (THCA) have not been elucidated. (2) Methods: To investigate the mechanism of NTP-induced cell death, THCA cell lines were treated with NTP-activated medium -(NTPAM), and gene expression profiles were evaluated using RNA sequencing.

Literature mining for context-specific molecular relations using multimodal representations (COMMODAR).

Biological contextual information helps understand various phenomena occurring in the biological systems consisting of complex molecular relations. The construction of context-specific relational resources vastly relies on laborious manual extraction from unstructured literature. In this paper, we propose COMMODAR, a machine learning-based literature mining framework for context-specific molecular relations using multimodal representations.

Paeonia lactiflora extract suppresses cisplatin-induced muscle wasting via downregulation of muscle-specific ubiquitin E3 ligases, NF-κB signaling, and cytokine levels.

Ethnopharmacological Relevance: The dried root of Paeonia lactiflora Pall. (Radix Paeoniae) has been traditionally used to treat various inflammatory diseases in many Asian countries.

Aim Of The Study: Cisplatin is a broad-spectrum anticancer drug used in diverse types of cancer.