Access and benefit sharing biological materials for machines: Artificial intelligence, machine learning and deep learning by Charles Lawson, Elizabeth Englezos, Michelle Rourke

“…Building in ‘explainability’ to the AI algorithms may be a solution, at least in part, to delivering on fair and equitable ABS. …”
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Utilizing Machine Learning Techniques for Cancer Prediction and Classification based on Gene Expression Data by Mariwan Mahmood Hama Aziz, Sozan Abdullah Mahmood

“…Detecting cancer early by examining gene expression data is vital for providing effective patient care. …”
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Best practices and joint calling of the HumanExome BeadChip: the CHARGE Consortium. by Megan L Grove, Bing Yu, Barbara J Cochran, Talin Haritunians, Joshua C Bis, Kent D Taylor, Mark Hansen, Ingrid B Borecki, L Adrienne Cupples, Myriam Fornage, Vilmundur Gudnason, Tamara B Harris, Sekar Kathiresan, Robert Kraaij, Lenore J Launer, Daniel Levy, Yongmei Liu, Thomas Mosley, Gina M Peloso, Bruce M Psaty, Stephen S Rich, Fernando Rivadeneira, David S Siscovick, Albert V Smith, Andre Uitterlinden, Cornelia M van Duijn, James G Wilson, Christopher J O'Donnell, Jerome I Rotter, Eric Boerwinkle

“…., minor allele frequency [MAF] <0.01) is the difficulty that automated clustering algorithms have to accurately detect and assign genotype calls. …”
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An artificial intelligence model integrating culprit lesion diagnosis and risk assessment for acute coronary syndromeResearch in context by Peng Peng Xu, Bin Hu, Fan Zhou, Zhi Han Xu, Qian Chen, Tong Yuan Liu, Bang Jun Guo, Chang Sheng Zhou, Xin Wei Tao, Hong Yan Qiao, Jia Ni Zou, Xiang Ming Fang, Wen Cai Huang, Long Jiang Zhang

“…In the derivation cohort, the random forest (RF) model demonstrated the best diagnostic performance and robustness among the five ML algorithms for detecting ACS culprit lesions, achieving an area under the receiver operating characteristic curve (AUC) of 0.85 across the training (95% confidence interval [Cl]: 0.82–0.89), validation (95% Cl: 0.80–0.90), and test sets (95% Cl: 0.78–0.92). …”
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Sensor-Based Yield Prediction in Durum Wheat Under Semi-Arid Conditions Using Machine Learning Across Zadoks Growth Stages by Süreyya Betül Rufaioğlu, Ali Volkan Bilgili, Erdinç Savaşlı, İrfan Özberk, Salih Aydemir, Amjad Mohamed Ismael, Yunus Kaya, João P. Matos-Carvalho

“…Five different machine learning algorithms (Random Forest, Gradient Boosting, AdaBoost, LightGBM, and XGBoost) were tested individually for each stage, and the model performances were evaluated using statistical metrics such as R²%, RMSE t/ha, and MAE t/ha. …”
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Toward an Accurate Liver Disease Prediction Based on Two-Level Ensemble Stacking Model by Marghany Hassan Mohamed, Botheina Hussein Ali, Ahmed Ibrahim Taloba, Ahmad O. Aseeri, Mohamed Abd Elaziz, Shaker El-Sappagah, Nora Mahmoud El-Rashidy

“…The difficulty of detecting liver disease at an early stage goes back to its limited number of symptoms. …”
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A real-world pharmacovigilance study of Sorafenib based on the FDA Adverse Event Reporting System by Dongdong Zhang, Dongdong Zhang, Dongdong Zhang, Ying Cai, Ying Cai, Ying Cai, Yixin Sun, Peiji Zeng, Wei Wang, Wenhui Wang, Xiaohua Jiang, Yifan Lian, Yifan Lian, Yifan Lian

“…Disproportionality analysis was performed using robust algorithms for effective data mining to quantify the signals associated with Sorafenib-related AEs.ResultsIn total, we identifued 18,624 patients (82,857 AEs in the Sorafenib population) from the collected reports and examined, the occurrence of Sorafenib-induced AEs in 26 organ systems. …”
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Transforming heart transplantation care with multi-omics insights by Zhengbang Zou, Jianing Han, Zhiyuan Zhu, Shanshan Zheng, Xinhe Xu, Sheng Liu

“…Single–cell omics technologies and machine learning algorithms further resolve cellular heterogeneity and improve predictive modeling, thereby enhancing the clinical translatability of multi-omics data. …”
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STING is significantly increased in high-grade glioma with high risk of recurrence by Meishi Zhong, Manmei Long, Chenjie Han, Saiyan Ji, Qingyuan Yang

“…The expression of HRR proteins, immune proteins, and genetic alteration-related proteins were detected using immunostaining. Integrated analysis showed that ATRX is positively correlated with STING in high-grade glioma (HGG) with wild-type ATRX and IDH1. …”
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Integrating bioinformatics and machine learning for comprehensive analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in pediatric septic... by Peng Lyu, Na Xie, Xu-peng Shao, Shuai Xing, Xiao-yue Wang, Li-yun Duan, Xue Zhao, Jia-min Lu, Rong-fei Liu, Duo Zhang, Wei Lu, Kai-liang Fan

“…Thereafter, the genes were identified utilizing machine-learning algorithms. The receiver operating characteristic curve was employed to assess the discrimination and effectiveness of the hub genes. …”
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D-SPDH: Improving 3D Robot Pose Estimation in Sim2Real Scenario via Depth Data by Alessandro Simoni, Guido Borghi, Lorenzo Garattoni, Gianpiero Francesca, Roberto Vezzani

“…In this scenario, the availability of vision-based systems capable of detecting in real-time the robot&#x2019;s pose is needed as a first step towards a safe and effective interaction to, for instance, avoid collisions. …”
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Health economics evaluation of diagnostic strategies for gastro-oesophageal reflux disease with reflux symptoms in China: a modelling study by Yang Chen, Feng Xie, Hongda Chen, Dong Wu, Xiaoqing Li, Xiaxiao Yan, Meiduo Ouzhu, Ziqi Guo, Chengzhen Lyu, Daiyu Yang

“…Objectives American College of Gastroenterology (ACG) and Chinese expert consensus recommended different algorithmic approaches for the diagnosis of gastro-oesophageal reflux disease (GERD) are not yet defined. …”
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Analysis of Regions of Homozygosity: Revisited Through New Bioinformatic Approaches by Susana Valente, Mariana Ribeiro, Jennifer Schnur, Filipe Alves, Nuno Moniz, Dominik Seelow, João Parente Freixo, Paulo Filipe Silva, Jorge Oliveira

“…Whole-exome sequencing (WES) improves HM by detecting ROHs and disease-causing variants. <b>Methods</b>: To streamline personalized multigene panel creation, using WES and ROHs, we developed a methodology integrating ROHMMCLI and HomozygosityMapper algorithms, and, optionally, Human Phenotype Ontology (HPO) terms, implemented in a Django Web application. …”
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Integration of agronomic information, vegetation indices (VIs), and meteorological data for phenological monitoring and yield estimation of rice (Oryza sativa L.) by Jorge A. Fernandez-Jibaja, Nilton Atalaya-Marin, Yeltsin A. Álvarez-Robledo, Victor H. Taboada-Mitma, Juancarlos Cruz-Luis, Daniel Tineo, Malluri Goñas, Darwin Gómez-Fernández

“…Among the regression algorithms tested, support vector regression (SVR) demonstrated the highest predictive accuracy (R² = 0.81) for the Bellavista variety at the maximum tillering stage. …”
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Imaging analysis using Artificial Intelligence to predict outcomes after endovascular aortic aneurysm repair: protocol for a retrospective cohort study by Athanasios Saratzis, Juliette Raffort, Robert J Hinchliffe, Jonathan Boyle, George A Antoniou, Arun Pherwani, Maarit Venermo, Fabien Lareyre, Stavros K Kakkos, Mario D’Oria, Bahaa Nasr, Matthias Trenner

“…Introduction Endovascular aortic aneurysm repair (EVAR) requires long-term surveillance to detect and treat postoperative complications. However, prediction models to optimise follow-up strategies are still lacking. …”
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Exploring the Global and Regional Factors Influencing the Density of <i>Trachurus japonicus</i> in the South China Sea by Mingshuai Sun, Yaquan Li, Zuozhi Chen, Youwei Xu, Yutao Yang, Yan Zhang, Yalan Peng, Haoda Zhou

“…Leveraging advanced machine learning algorithms and causal inference, our robust experimental design uncovered nine key global and regional factors affecting the distribution of <i>T. japonicus</i> density. …”
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The Application and Ethical Implication of Generative AI in Mental Health: Systematic Review by Xi Wang, Yujia Zhou, Guangyu Zhou

“…Studies on diagnosis and assessment (37/79, 47%) primarily used GenAI models to detect depression and suicidality through text data. …”
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Are Artificial Intelligence Models Listening Like Cardiologists? Bridging the Gap Between Artificial Intelligence and Clinical Reasoning in Heart-Sound Classification Using Explain... by Sami Alrabie, Ahmed Barnawi

“…First, we observed that automatic heart-sound segmentation algorithms—commonly used for data augmentation—produce varying outcomes, raising concerns about the accuracy of both the segmentation process and the resulting classification performance. …”
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Compensation of Speckle Noise in 2D Images from Triangulation Laser Profile Sensors Using Local Column Median Vectors with an Application in a Quality Control System by Paweł Rotter, Dawid Knapik, Maciej Klemiato, Maciej Rosół, Grzegorz Putynkowski

“…Consequently, conventional algorithms for removing fixed pattern noise yield unsatisfactory results when applied to profilometer images. …”
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Identification of biomarkers related to Escherichia coli infection for the diagnosis of gastrointestinal tumors applying machine learning methods by Tingting Ge, Wei Wang, Dandan Zhang, Xubo Le, Lumei Shi

“…Conclusions: Overall, we identified and validated 8 robust genes related to E. coli applying bioinformatics and machine learning algorithms, providing theoretical foundations for the relationship between E. coli-related dysbiosis and gastrointestinal tumors.…”
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