Deep learning captures the effect of epistasis in multifactorial diseases by Vladislav Perelygin, Alexey Kamelin, Alexey Kamelin, Nikita Syzrantsev, Layal Shaheen, Layal Shaheen, Anna Kim, Nikolay Plotnikov, Anna Ilinskaya, Valery Ilinsky, Alexander Rakitko, Alexander Rakitko, Maria Poptsova

“…For machine learning methods we used multilayer perceptron (MLP), convolutional neural network (CNN) and recurrent neural network (RNN), Lasso regression, random forest and gradient boosting models. …”
Get full text

Development of an artificial intelligence-based application for the diagnosis of sarcopenia: a retrospective cohort study using the health examination dataset by Chang-Won Jeong, Dong-Wook Lim, Si-Hyeong Noh, Sung Hyun Lee, Chul Park

“…Methods We developed an automated lumbar spine slice classification model using the CNN (EfficientNetV2) algorithm and an automated domain segmentation model to identify the subcutaneous fat, visceral fat, and muscle areas using the U-NET algorithm. …”
Get full text

Use of artificial intelligence for gestational age estimation: a systematic review and meta-analysis by Sabahat Naz, Sahir Noorani, Syed Ali Jaffar Zaidi, Abdu R. Rahman, Saima Sattar, Jai K. Das, Jai K. Das, Zahra Hoodbhoy

“…In studies using deep learning for 2D images, those employing CNN reported a mean error of 5.11 days (95% CI: 1.85, 8.37) in gestational age estimation, while one using DNN indicated a mean error of 5.39 days (95% CI: 5.10, 5.68). …”
Get full text

Identification of standing dead trees in Robinia pseudoacacia plantations across China’s Loess Plateau using multiple deep learning models by Li Zhang, Xiaodong Gao, Shuyi Zhou, Zhibo Zhang, Tianjie Zhao, Yaohui Cai, Xining Zhao

“…These images were then integrated with a comprehensive evaluation of multiple detection algorithms, including Faster R-CNN, EfficientDet, YOLOv4, YOLOv5, YOLOv8, YOLOv9, and a novel model, YOLOv9-ECA. …”
Get full text

Improving Autonomous Vehicle Cognitive Robustness in Extreme Weather With Deep Learning and Thermal Camera Fusion by Mehmood Nawaz, Sheheryar Khan, Muhammad Daud, Muhammad Asim, Ghazanfar Ali Anwar, Ali Raza Shahid, Ho Pui Aaron HO, Tom Chan, Daniel Pak Kong, Wu Yuan

“…The visual fusion framework employs a CNN (convolutional neural network) inspired by a domain image fusion algorithm. …”
Get full text

DLBWE-Cys: a deep-learning-based tool for identifying cysteine S-carboxyethylation sites using binary-weight encoding by Zhengtao Luo, Zhengtao Luo, Zhengtao Luo, Qingyong Wang, Qingyong Wang, Qingyong Wang, Yingchun Xia, Yingchun Xia, Yingchun Xia, Xiaolei Zhu, Xiaolei Zhu, Xiaolei Zhu, Shuai Yang, Shuai Yang, Shuai Yang, Zhaochun Xu, Zhaochun Xu, Lichuan Gu, Lichuan Gu, Lichuan Gu

“…In this study, we developed a new deep learning model, DLBWE-Cys, which integrates CNN, BiLSTM, Bahdanau attention mechanisms, and a fully connected neural network (FNN), using Binary-Weight encoding specifically designed for the accurate identification of cysteine S-carboxyethylation sites. …”
Get full text

Serum proteomic and metabolomic profiling of hepatocellular carcinoma patients co-infected with Clonorchis sinensis by Zeli Tang, Zeli Tang, Zeli Tang, Caibiao Wei, Xueling Deng, Qiumei Lin, Qiping Hu, Qiping Hu, Qiping Hu, Shitao Li, Jilong Wang, Yuhong Wu, Dengyu Liu, Dengyu Liu, Dengyu Liu, Min Fang, Min Fang, Tingzheng Zhan, Tingzheng Zhan, Tingzheng Zhan

“…Proteomic and metabolomic analyses revealed metabolic reprogramming caused by C. sinensis, with excessive depletion of argininosuccinate synthase (ASS) and D-glucose as potential factors in C. sinensis-associated HCC malignancy. Key molecules ILF2, CNN2, OLFM4, NOTCH3, and LysoPA were implicated in HCC progression. …”
Get full text

ReluformerN: Lightweight High-Low Frequency Enhanced for Hyperspectral Agricultural Lancover Classification by LIU Yi, ZHANG Yanjun

“…ReluformerN was experimented on three public high-spectral data sets (Indian Pines, WHU-Hi-LongKou and Salinas) for crop variety fine classification, and was compared with five popular classification networks (2D-CNN, HybirdSN, ViT, CTN and LSGA-VIT).[Results and Discussion]ReluformerN performed best in overall accuracy (OA), average accuracy (AA), and other accuracy evaluation indicators. …”
Get full text

Comparative Analysis of Prediction Models for Trawling Grounds of the Argentine Shortfin Squid <i>Illex argentinus</i> in the Southwest Atlantic High Seas Based on Vessel Position... by Delong Xiang, Yuyan Sun, Hanji Zhu, Jianhua Wang, Sisi Huang, Shengmao Zhang, Famou Zhang, Heng Zhang

“…Fishing ground levels were defined according to the density of fishing locations, and combined with oceanographic data (sea surface temperature, 50 m water temperature, sea surface salinity, sea surface height, and mixed layer depth). A CNN-Attention deep learning model was applied to each dataset to develop <i>Illex argentinus</i> trawling ground prediction models. …”
Get full text