Jamu is the traditional Indonesian herbal medicine system that is considered to have many benefits such as serving as a cure for diseases or maintaining sound health. A Jamu medicine is generally made from a mixture of several herbs. Natural antibiotics can provide a way to handle the problem of antibiotic resistance. This research aims to discover the potential of herbal plants as natural antibiotic candidates based on a machine learning approach. Our input data consists of a list of herbal formulas with plants as their constituents. The target class corresponds to bacterial diseases that can be cured by herbal formulas. The best model has been observed by implementing the Random Forest (RF) algorithm. For 10-fold cross-validations, the maximum accuracy, recall, and precision are 91.10%, 91.10%, and 90.54% with standard deviations 1.05, 1.05, and 1.48, respectively, which imply that the model obtained is good and robust. This study has shown that 14 plants can be potentially used as natural antibiotic candidates. Furthermore, according to scientific journals, 10 of the 14 selected plants have direct or indirect antibacterial activity.

Objectives: This study implements a multi-dimensional methodology to systematically identify potential natural antibiotics derived from the medicinal plants utilized in Ayurvedic practices. Methods: Two primary analytical techniques are employed to explore the antibiotic potential of the medicinal plants. The initial approach utilizes a supervised network analysis, which involves the application of distance measurement algorithms to scrutinize the interconnectivity and relational patterns within the network derived from Ayurvedic formulae. Results: 39 candidate plants with potential natural antibiotic properties were identified. The second approach leverages advanced machine learning techniques, particularly focusing on feature extraction and pattern recognition. This approach yielded a list of 32 plants exhibiting characteristics indicative of natural antibiotics. A key finding of this research is the identification of 17 plants that were consistently recognized by both analytical methods. These plants are well-documented in existing literature for their antibacterial properties, either directly or through their bioactive compounds, which suggests a strong validation of the study’s methodology. By synergizing network analysis with machine learning, this study provides a rigorous and multi-faceted examination of Ayurvedic medicinal plants, significantly contributing to the identification of natural antibiotic candidates. Conclusions: This research not only reinforces the potential of traditional medicine as a source for new therapeutics but also demonstrates the effectiveness of combining classical and contemporary analytical techniques to explore complex biological datasets.

The use of herbal medicines in recent decades has increased because their side effects are considered lower than conventional medicine. Unani herbal medicines are often used in Southern Asia. These herbal medicines are usually composed of several types of medicinal plants to treat various diseases. Research on herbal medicine usually focuses on insight into the composition of plants used as ingredients. However, in the present study, we extended to the level of metabolites that exist in the medicinal plants. This study aimed to develop a predictive model of the Unani therapeutic usage based on its constituent metabolites using deep learning and data-intensive science approaches. Furthermore, the best prediction model was then utilized to extract important metabolites for each therapeutic usage of Unani. In this study, it was observed that the deep neural network approach provided a much better prediction model than other algorithms including random forest and support vector machine. Moreover, according to the best prediction model using the deep neural network, we identified 118 important metabolites for nine therapeutic usages of Unani.

The Unani Tibb is a medical system of Greek descent that has undergone substantial dissemination since the 11th century and is currently prevalent in modern South and Central Asia, particularly in primary health care. The ingredients of Unani herbal medicines are primarily derived from plants. Our research aimed to address the pressing issues of antibiotic resistance, multi-drug resistance, and the emergence of superbugs by examining the molecular-level effects of Unani ingredients as potential new natural antibiotic candidates. We utilized a machine learning approach to tackle these challenges, employing decision trees, kernels, neural networks, and probability-based methods. We used 12 machine learning algorithms and several techniques for preprocessing data, such as Synthetic Minority Over-sampling Technique (SMOTE), Feature Selection, and Principal Component Analysis (PCA). To ensure that our model was optimal, we conducted grid-search tuning to tune all the hyperparameters of the machine learning models. The application of Multi-Layer Perceptron (MLP) with SMOTE pre-processing techniques resulted in an impressive accuracy precision and recall values. This analysis identified 20 important metabolites as essential components of the formula, which we predicted as natural antibiotics. In the final stage of our investigation, we verified our prediction by conducting a literature search for journal validation or by analyzing the structural similarity with known antibiotics using asymmetric similarity.

Generative image models have revolutionized artificial intelligence by enabling the synthesis of high-quality, realistic images. These models utilize deep learning techniques to learn complex data distributions and generate novel images that closely resemble the training dataset. Recent advancements, particularly in diffusion models, have led to remarkable improvements in image fidelity, diversity, and controllability. In this work, we investigate the application of a conditional latent diffusion model in the healthcare domain. Specifically, we trained a latent diffusion model using unlabeled histopathology images. Initially, these images were embedded into a lower-dimensional latent space using a Vector Quantized Generative Adversarial Network (VQ-GAN). Subsequently, a diffusion process was applied within this latent space, and clustering was performed on the resulting latent features. The clustering results were then used as a conditioning mechanism for the diffusion model, enabling conditional image generation. Finally, we determined the optimal number of clusters using cluster validation metrics and assessed the quality of the synthetic images through quantitative methods. To enhance the interpretability of the synthetic image generation process, expert input was incorporated into the cluster assignments.

Background/Objectives: Drug development for complex diseases such as NAFLD is often lengthy and expensive. Drug repurposing, the process of finding new therapeutic uses for existing drugs, presents a promising alternative to traditional approaches. This study aims to identify potential repurposed drugs for NAFLD by leveraging disease–disease relationships and drug–target data from the BioSNAP database. Methods: A bipartite network was constructed between drugs and their target genes, followed by the application of the BiClusO bi-clustering algorithm to identify high-density clusters. Clusters with significant associations with NAFLD risk genes were considered to predict potential drug candidates. Another set of candidates was determined based on disease similarity. Results: A novel ranking methodology was developed to evaluate and prioritize these candidates, supported by a comprehensive literature review of their effectiveness in NAFLD treatment. Conclusions: This research demonstrates the potential of drug repurposing to accelerate the development of therapies for NAFLD, offering valuable insights into novel treatment strategies for complex diseases.