deep-Sep: a deep learning-based method for fast and accurate prediction of selenoprotein genes in bacteria by Yao Xiao, Yan Zhang

“…In this study, we have developed a deep learning-based algorithm, deep-Sep, for quickly and precisely identifying selenoprotein genes in bacterial genomic sequences. …”
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Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms by Anjan Kumar Pradhan, Prasad Gandham, Kanniah Rajasekaran, Niranjan Baisakh

“…The top-ranked genes predicted to be key players in multiple stress resistance in maize need to be functional validated to ascertain their roles and further utilization in developing stress-resistant maize varieties.…”
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Secure Biometric Identification Using Orca Predators Algorithm With Deep Learning: Retinal Iris Image Analysis by Louai A. Maghrabi, Mohammed Altwijri, Sami Saeed Binyamin, Fouad Shoie Alallah, Diaa Hamed, Mahmoud Ragab

“…The retina and iris are exclusive and constant functional features of the human eye that can be employed for individual identification. …”
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Comparative Study of Cell Nuclei Segmentation Based on Computational and Handcrafted Features Using Machine Learning Algorithms by Rashadul Islam Sumon, Md Ariful Islam Mozumdar, Salma Akter, Shah Muhammad Imtiyaj Uddin, Mohammad Hassan Ali Al-Onaizan, Reem Ibrahim Alkanhel, Mohammed Saleh Ali Muthanna

“…The cell nucleus is a crucial aspect in segmenting to gain more insight into cell characteristics and functions that enable computer-aided pathology for early disease detection, such as prostate cancer, breast cancer, brain tumors, and other diagnoses. …”
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Predictive Modeling of soil salinity integrating remote sensing and soil variables: An ensembled deep learning approach by Sana Arshad, Jamil Hasan Kazmi, Endre Harsányi, Farheen Nazli, Waseem Hassan, Saima Shaikh, Main Al-Dalahmeh, Safwan Mohammed

“…However, the ensemble of improved FFNN and LSTM outperformed with the highest R2 and NSE = 0.84, and the lowest RMSE and MAE = 1.38 and 1.01, respectively, on the testing dataset. Optimized deep learning architectures with adjustments to the learning rate, dropout rate, and activation functions achieved the highest prediction accuracy with the lowest validation loss. …”
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A Comprehensive Deep Learning System With MGRF Modeling for Predicting Breast Cancer Response to Neoadjuvant Chemotherapy by Ahmed Sharafeldeen, Fatma Taher, Norah Saleh Alghamdi, Eman Alnaghy, Reham Alghandour, Khadiga M. Ali, Sameh Shamaa, Abdelrahman Gamal, Mohammed Ghazal, Sohail Contractor, Ayman El-Baz

“…Evaluated on a cohort of 109 BC patients using leave-one-subject-out (LOSO) cross-validation method, the system achieved an accuracy of 96.33%, a precision of 96.51%, a recall of 96.33%, an F1-score of 96.23%, and a Cohen’s kappa of 94.08%, outperforming its individual components, various pretrained deep learning models, and a state-of-the-art method. These results underscore the value of integrating the appearance model, functional (i.e., ADC) model, adaptive rescaling module, SE blocks, and clinical and molecular subtype markers for the precise prediction of NAC outcomes.…”
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Rapid diagnosis of rheumatoid arthritis and ankylosing spondylitis based on Fourier transform infrared spectroscopy and deep learning by Wei Shuai, Xue Wu, Chen Chen, Enguang Zuo, Xiaomei Chen, Zhengfang Li, Xiaoyi Lv, Lijun Wu, Cheng Chen

“…Objective: Rheumatoid arthritis and Ankylosing spondylitis are two common autoimmune inflammatory rheumatic diseases that negatively affect activities of daily living and can lead to structural and functional disability, reduced quality of life. Here, this study utilized Fourier transform infrared (FTIR) spectroscopy on dried serum samples and achieved early diagnosis of rheumatoid arthritis and ankylosing spondylitis based on deep learning models. …”
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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

“…Gene ontology and Kyoto encyclopedia of genes and genomes analyses were performed to explore the functions of common FR-related DEGs (FRDEGs). 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. …”
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Machine learning-based radiomics prognostic model for patients with proximal esophageal cancer after definitive chemoradiotherapy by Linrui Li, Zhihui Qin, Juan Bo, Jiaru Hu, Yu Zhang, Liting Qian, Jiangning Dong

“…Radiomics models were established by five machine learning approaches. The optimal radiomics model was selected using receiver operating curve analysis. …”
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Construction of a Wilms tumor risk model based on machine learning and identification of cuproptosis-related clusters by Jingru Huang, Yong Li, Xiaotan Pan, Jixiu Wei, Qiongqian Xu, Yin Zheng, Peng Chen, Jiabo Chen

“…The best-performing machine learning model was chosen, and a nomogram was created. …”
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Genomics and integrative clinical data machine learning scoring model to ascertain likely Lynch syndrome patients by Ramadhani Chambuso, Takudzwa Nyasha Musarurwa, Alessandro Pietro Aldera, Armin Deffur, Hayli Geffen, Douglas Perkins, Raj Ramesar

“…We developed a unique machine learning scoring model to ascertain likely-LS cases from a cohort of colorectal cancer (CRC) patients. …”
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Exploration of biomarkers for predicting the prognosis of patients with diffuse large B-cell lymphoma by machine-learning analysis by Shifen Wang, Hong Tao, Xingyun Zhao, Siwen Wu, Chunwei Yang, Yuanfei Shi, Zhenshu Xu, Dawei Cui

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LOST IN TRANSLATION: CULTURAL AND PEDAGOGICAL PITFALLS OF WORD-FOR-WORD LANGUAGE TRANSFER IN ITALIAN L2 LEARNING by Mohammad J. Jamali

“…Advocating a communicative, functional approach, this paper emphasizes the need for cultural literacy, awareness of traditions, idioms, and symbols. …”
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Pengukuran Kepuasan Pengguna E-Learning Menggunakan Metode Evaluasi Heuristik dan System Usability Scale by Emi Iryanti, La Ode Mohamad Zulfiqar, Sri Suning Kusumawardani, Indriana Hidayah

“…The results of the usability evaluation using HE, it was found that there is one principle that is considered a major problem by user experts, namely the principle of user control and freedom, where the system (e-learning) does not facilitate the undo and redo functions which causes confusion if the user click unwanted menu. …”
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Integrating bioinformatics and machine learning to identify glomerular injury genes and predict drug targets in diabetic nephropathy by Li Zhang, ZhenPeng Sun, Yao Yuan, Jie Sheng

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The identification and validation of histone acetylation-related biomarkers in depression disorder based on bioinformatics and machine learning approaches by Lu Zhang, Lu Zhang, YuJing Lv, Mengqing Ma, Jile Lv, Jie Chen, Shang Lei, Yi Man, Guimei Xing, Yu Wang

“…Candidate genes were selected by intersecting DEGs, key module genes, and HAC-RGs, followed by functional analysis. Two machine learning algorithms were used to identify hub genes, which were used for drug prediction, immunological infiltration studies, nomogram construction, and regulatory network building. …”
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Development and Validation of a Brain Aging Biomarker in Middle-Aged and Older Adults: Deep Learning Approach by Zihan Li, Jun Li, Jiahui Li, Mengying Wang, Andi Xu, Yushu Huang, Qi Yu, Lingzhi Zhang, Yingjun Li, Zilin Li, Xifeng Wu, Jiajun Bu, Wenyuan Li

“…Brain age gap exhibited clinical feasibility combined with Functional Activities Questionnaire, with improved discriminative capacity in models achieving lower MAEs (AUC of 0.945 vs 0.923 and 0.911; AUC of 0.935 vs 0.900 and 0.881). …”
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Improved estimation of two-phase capillary pressure with nuclear magnetic resonance measurements via machine learning by Oriyomi Raheem, Misael M. Morales, Wen Pan, Carlos Torres-Verdín

“…By predicting vector functions from vector input features, we effectively reduced prediction errors. …”
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Single-cell and machine learning approaches uncover intrinsic immune-evasion genes in the prognosis of hepatocellular carcinoma by Jiani Wang, Xiaopeng Chen, Donghao Wu, Changchang Jia, Qinghai Lian, Yuhang Pan, Jiumei Yang

“…Functional enrichment analysis was conducted to explore potential mechanisms. …”
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A Heterogeneity-Aware Semi-Decentralized Model for a Lightweight Intrusion Detection System for IoT Networks Based on Federated Learning and BiLSTM by Shuroog Alsaleh, Mohamed El Bachir Menai, Saad Al-Ahmadi

“…Researchers have applied many approaches to lightweight IDSs, including energy-based IDSs, machine learning/deep learning (ML/DL)-based IDSs, and federated learning (FL)-based IDSs. …”
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