MDD-LLM: Towards accuracy large language models for major depressive disorder diagnosis

Bootstrap inference and machine learning reveal core differential plasma metabolic connectome signatures in major depressive disorder

Association and causal mediation between marital status and depression in seven countries

Cherry growth modeling based on Prior Distance Embedding contrastive learning: Pre-training, anomaly detection, semantic segmentation, and temporal modeling

CerviFusionNet: A multi-modal, hybrid CNN-transformer-GRU model for enhanced cervical lesion multi-classification

A Novel Fusion Pruning-Processed Lightweight CNN for Local Object Recognition on Resource-Constrained Devices

HerbMet: Enhancing metabolomics data analysis for accurate identification of Chinese herbal medicines using deep learning

AbstractIntroductionChinese herbal medicines have been utilized for thousands of years to prevent and treat diseases. Accurate identification is crucial since their medicinal effects vary between species and varieties. Metabolomics is a promising approach to distinguish herbs. However, current metabolomics data analysis and modeling in Chinese herbal medicines are limited by small sample sizes, high dimensionality, and overfitting.ObjectivesThis study aims to use metabolomics data to develop HerbMet, a high‐performance artificial intelligence system for accurately identifying Chinese herbal medicines, particularly those from different species of the same genus.MethodsWe propose HerbMet, an AI‐based system for accurately identifying Chinese herbal medicines. HerbMet employs a 1D‐ResNet architecture to extract discriminative features from input samples and uses a multilayer perceptron for classification. Additionally, we design the double dropout regularization module to alleviate overfitting and improve model's performance.ResultsCompared to 10 commonly used machine learning and deep learning methods, HerbMet achieves superior accuracy and robustness, with an accuracy of 0.9571 and an F1‐score of 0.9542 for distinguishing seven similar Panax ginseng species. After feature selection by 25 different feature ranking techniques in combination with prior knowledge, we obtained 100% accuracy and an F1‐score for discriminating P. ginseng species. Furthermore, HerbMet exhibits acceptable inference speed and computational costs compared to existing approaches on both CPU and GPU.ConclusionsHerbMet surpasses existing solutions for identifying Chinese herbal medicines species. It is simple to use in real‐world scenarios, eliminating the need for feature ranking and selection in classical machine learning‐based methods.

MetDIT: Transforming and Analyzing Clinical Metabolomics Data with Convolutional Neural Networks

Precautions for study design and data interpretation of clinical metabolomics

Machine Learning Identifies Metabolic Signatures that Predict the Risk of Recurrent Angina in Remitted Patients after Percutaneous Coronary Intervention: A Multicenter Prospective Cohort Study

AbstractRecurrent angina (RA) after percutaneous coronary intervention (PCI) has few known risk factors, hampering the identification of high‐risk populations. In this multicenter study, plasma samples are collected from patients with stable angina after PCI, and these patients are followed‐up for 9 months for angina recurrence. Broad‐spectrum metabolomic profiling with LC‐MS/MS followed by multiple machine learning algorithms is conducted to identify the metabolic signatures associated with future risk of angina recurrence in two large cohorts (n = 750 for discovery set, and n = 775 for additional independent discovery cohort). The metabolic predictors are further validated in a third cohort from another center (n = 130) using a clinically‐sound quantitative approach. Compared to angina‐free patients, the remitted patients with future RA demonstrates a unique chemical endophenotype dominated by abnormalities in chemical communication across lipid membranes and mitochondrial function. A novel multi‐metabolite predictive model constructed from these latent signatures can stratify remitted patients at high‐risk for angina recurrence with over 89% accuracy, sensitivity, and specificity across three independent cohorts. Our findings revealed reproducible plasma metabolic signatures to predict patients with a latent future risk of RA during post‐PCI remission, allowing them to be treated in advance before an event.