Individual differences in response to alcohol exposure in zebrafish (Danio rerio). by Heloysa Araujo-Silva, Jaquelinne Pinheiro-da-Silva, Priscila F Silva, Ana C Luchiari

Get full text

Multi-Target Mechanism of Compound Qingdai Capsule for Treatment of Psoriasis: Multi-Omics Analysis and Experimental Verification by Qiao Y, Li C, Chen C, Wu P, Yang Y, Xie M, Liu N, Gu J

“…CQC ingredients-targets network was constructed using these ingredients and their targets. Screening of CQC anti-psoriasis core targets using machine learning algorithm. …”
Get full text

Optimized Landing Site Selection at the Lunar South Pole: A Convolutional Neural Network Approach by Yongjiu Feng, Haoteng Li, Xiaohua Tong, Pengshuo Li, Rong Wang, Shurui Chen, Mengrong Xi, Jingbo Sun, Yuhao Wang, Huaiyu He, Chao Wang, Xiong Xu, Huan Xie, Yanmin Jin, Sicong Liu

“…Although intelligent algorithms have been increasingly investigated for this purpose, the application of deep learning techniques in landing site selection remains unexplored. …”
Get full text

Systematic elucidation of the effective constituents and potential mechanisms of Scrophulariae Radix against neoplasm based on LC-MS, network pharmacology, and molecular docking ap... by Shu-jie Yu, Xiao-bin Kong, Xin Jin, Meng-yi Shan, Gang Cheng, Pei-lu Wang, Wen-long Li, Pei-yuan Zhao, Yun-jie Sheng, Bing-qian He, Qi Shi, Hua-qiang Li, Qi-ming Zhao, Lu-ping Qin, Lu-ping Qin, Xiong-yu Meng, Xiong-yu Meng

“…As a result, the material–liquid ratio was significantly reduced from 100 g/mL to 32 g/mL, and the extraction efficiency was 1.332%, which was close to the predicted value of 1.346% in the response surface method, indicating that the algorithm model had a good fit. Next, a total of 738 compounds, including 161 terpenoids, 144 phenolic acids, 51 alkaloids, 24 flavonoids, 34 saccharides, 32 lignans and coumarins, 45 amino acids and derivatives, 23 organic acids, 134 lipids, 22 nucleotides and derivatives, and 59 other ingredients, were characterized from Scrophulariae Radix based on the accurate precursor and product ions, retention time, standards, fragmentation patterns, and previous publications. …”
Get full text

Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.) by Zhu Yang, Zhu Yang, Wenjie Kan, Wenjie Kan, Ziqi Wang, Caiguo Tang, Yuan Cheng, Yuan Cheng, Dacheng Wang, Dacheng Wang, Yameng Gao, Lifang Wu, Lifang Wu

“…Additionally, the least absolute shrinkage and selection operator (LASSO) regression algorithm in machine learning was used to screen transcription factors such as bHLH, WRKY, and MYB that influenced the expression of TaAkPHY genes. …”
Get full text

Identification of three T cell-related genes as diagnostic and prognostic biomarkers for triple-negative breast cancer and exploration of potential mechanisms by Zhi-Chuan He, Zheng-Zheng Song, Zhe Wu, Peng-Fei Lin, Xin-Xing Wang

“…Differentially expressed genes (DEGs) between TNBC and other BRCA subtypes were intersected with T cell-related genes to identify candidate biomarkers. Machine learning algorithms were used to screen for key hub genes, which were then used to construct a logistic regression (LR) model. …”
Get full text

A Deep Learning Method for Pneumoconiosis Staging on Chest X-Ray Under Label Noise by Wenjian Sun, Dongsheng Wu, Jiang Shen, Yang Luo, Hao Wang, Li Min, Chunbo Luo

“…The ambiguous properties of small opacities in pneumoconiosis chest radiographs can cause diagnostic drift, which in turn leads to the presence of noisy labels in the datasets collected from hospitals that can negatively impact the generalization of deep learning models. To tackle this issue, we propose COFINE, a novel coarse-to-fine noise-tolerant deep learning method for the staging of pneumoconiosis chest radiographs, which comprises two procedures: coarse screening and fine learning. …”
Get full text

A nicotinamide metabolism-related gene signature for predicting immunotherapy response and prognosis in lung adenocarcinoma patients by Meng Wang, Wei Li, Fang Zhou, Zheng Wang, Xiaoteng Jia, Xingpeng Han

“…Four independent prognostic NMRGs (GJB3, CPA3, DKK1, KRT6A) were screened and used to construct a RiskScore model, which exhibited a strong predictive performance. …”
Get full text

Machine learning analysis of FOSL2 and RHoBTB1 as central immunological regulators in knee osteoarthritis synovium by Kun Gao, Zhenyu Huang, Zhouwei Liao, Yanfei Wang, Dayu Chen

“…We employed several machine learning algorithms, including least absolute shrinkage and selection operator and support vector machine–recursive feature elimination, to screen for key genes. …”
Get full text

Integrated multi-omics analysis and machine learning identify G protein-coupled receptor-related signatures for diagnosis and clinical benefits in soft tissue sarcoma by Duo Wang, Duo Wang, Duo Wang, Jihao Tu, Jihao Tu, Jianfeng Liu, Jianfeng Liu, Yuting Piao, Yuting Piao, Yiming Zhao, Yiming Zhao, Ying Xiong, Ying Xiong, Jianing Wang, Jianing Wang, Xiaotian Zheng, Xiaotian Zheng, Bin Liu, Bin Liu

“…We developed a novel machine learning framework that incorporated 12 machine learning algorithms and their 127 combinations to construct a consensus GPRS to screen biomarkers with diagnostic significance and clinical translation, which was assessed by the internal and external validation datasets. …”
Get full text

Uso de inteligencia artificial para predecir complicaciones en cirugías de columna toracolumbar degenerativa: revisión sistemática by G. Ricciardi, J.I. Cirillo Totera, R. Pons Belmonte, L. Romero Valverde, F. López Muñoz, A. Manríquez Díaz

“…Due to heterogeneity in samples, outcomes of interest, and algorithm evaluation metrics, a meta-analysis was not performed. …”
Get full text

[Translated article] Use of artificial intelligence to predict complications in degenerative thoracolumbar spine surgery: A systematic review by G. Ricciardi, J.I. Cirillo Totera, R. Pons Belmonte, L. Romero Valverde, F. López Muñoz, A. Manríquez Díaz

“…In 5 (41.6%) articles, the effectiveness of artificial intelligence predictive models was compared with conventional techniques. …”
Get full text

Accuracy and interpretability of smartwatch electrocardiogram for early detection of atrial fibrillation: A systematic review and meta‐analysis by Dr. Muhammad Iqhrammullah, Prof. Asnawi Abdullah, Dr. Hermansyah, Fahmi Ichwansyah, Prof. Dr. Ir. Hafnidar A. Rani, Meulu Alina, Artha M. T. Simanjuntak, Derren D. C. H. Rampengan, dr. Seba Talat Al‐Gunaid, dr. Naufal Gusti, dr. Arditya Damarkusuma, Edza Aria Wikurendra

“…Methods Data derived from indexed literature in the Scopus, Scilit, PubMed, Google Scholar, Web of Science, IEEE, and Cochrane Library databases (as of June 1, 2024) were systematically screened and extracted. The quantitative synthesis was performed using a two‐level mixed‐effects logistic regression model, as well as a proportional analysis with Freeman‐Tukey double transformation on a restricted maximum‐likelihood model. …”
Get full text

Nitrogen content estimation of apple trees based on simulated satellite remote sensing data by Meixuan Li, Xicun Zhu, Xicun Zhu, Xinyang Yu, Cheng Li, Dongyun Xu, Ling Wang, Dong Lv, Yuyang Ma

“…Support Vector Machine (SVM) and Backpropagation Neural Network (BPNN) algorithms were used to construct and screen the optimal models for apple tree nitrogen content estimation.ResultsResults showed that visible light, red edge, near-infrared, and yellow edge bands were sensitive bands for estimating apple tree nitrogen content. …”
Get full text

Artificial Intelligence Powered Automated and Early Diagnosis of Acute Lymphoblastic Leukemia Cancer in Histopathological Images: A Robust SqueezeNet-Enhanced Machine Learning Fram... by Vineet Mehan

“…Combining DL and ML algorithms addresses the complexity of understanding histopathological images and the classification process. …”
Get full text

Generative and predictive neural networks for the design of functional RNA molecules by Aidan T. Riley, James M. Robson, Aiganysh Ulanova, Alexander A. Green

“…We pair these predictive models with generative adversarial RNA design networks (GARDN), allowing the generative modelling of a diverse range of functional RNA molecules with targeted experimental attributes. …”
Get full text

The systemic oxidative stress index predicts clinical outcomes of esophageal squamous cell carcinoma receiving neoadjuvant immunochemotherapy by Jifeng Feng, Jifeng Feng, Liang Wang, Xun Yang, Qixun Chen, Qixun Chen

“…Then, a new staging that included TNM and SOSI based on RPA algorithms was produced. In terms of prognostication, the RPA model performed significantly better than TNM classification.ConclusionSOSI is a simple and useful score based on available SOS-related indices. …”
Get full text

Machine learning identification of key genes in cardioembolic stroke and atherosclerosis: their association with pan-cancer and immune cells by Tianxiang Zhang, Chunhui Yuan, Mo Chen, Jinjiang Liu, Wei Shao, Ning Cheng

“…Two machine learning algorithms, Least Absolute Shrinkage and Selection Operator (LASSO) regression and Support Vector Machine Recursive Feature Elimination (SVM-RFE), were used to screen for overlapping FRDEGs in CS and AS. …”
Get full text

Analysis and Validation of Autophagy-Related Gene Biomarkers and Immune Cell Infiltration Characteristic in Bronchopulmonary Dysplasia by Integrating Bioinformatics and Machine Lea... by Xiao S, Ding Y, Du C, Lv Y, Yang S, Zheng Q, Wang Z, Zheng Q, Huang M, Xiao Q, Ren Z, Bi G, Yang J

“…Subsequently, the hub genes were identified by Lasso and Cytoscape with three machine-learning algorithms (MCC, Degree and MCODE). In addition, hub genes were validated with ROC, single-cell sequence and IHC in hyperoxia mice. …”
Get full text

Elucidating the dynamic tumor microenvironment through deep transcriptomic analysis and therapeutic implication of MRE11 expression patterns in hepatocellular carcinoma by Ruiqiu Chen, Chaohui Xiao, Zizheng Wang, Guineng Zeng, Shaoming Song, Gong Zhang, Lin Zhu, Penghui Yang, Rong Liu

“…Publicly available single-cell RNA sequencing (scRNA-seq) data and spatial transcriptomics were utilized to explore MRE11’s dynamic mechanisms in the tumor microenvironment (TME) of both primary and post-immunotherapy cases. We also screened for differentially expressed genes and constructed a robust HCC prognosis model using 101 machine-learning algorithms. …”
Get full text