Establishment and validation of an immune-related nomogram for the prognosis of pancreatic adenocarcinoma by Kan Wang, Yunkun Lu, Yanfei Cao, Ping Feng, Qiu Wu, Peng Xiao, Yimin Ding

“…This study aims to improve prognosis prediction to guide therapeutic decision-making, and to identify novel targets for immunotherapy of PDAC. …”
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Development of Recurrent Neural Networks for Thermal/Electrical Analysis of Non-Residential Buildings Based on Energy Consumptions Data by Elisa Belloni, Flavia Forconi, Gabriele Maria Lozito, Martina Palermo, Michele Quercio, Francesco Riganti Fulginei

“…Simplifying input variables can enhance the applicability of artificial intelligence techniques in predicting energy and thermal performance. This study proposes a neural network-based approach to characterize the thermal–energy relationship in commercial buildings, aiming to provide an efficient and scalable solution for performance prediction. …”
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Identification of a Hypoxia-Angiogenesis lncRNA Signature Participating in Immunosuppression in Gastric Cancer by Zicheng Wang, Xisong Liang, Hao Zhang, Zeyu Wang, Xun Zhang, Ziyu Dai, Zaoqu Liu, Jian Zhang, Peng Luo, Jiarong Li, Quan Cheng

“…As a result, we found that HARM predicted patient survival with high accuracy and was correlated with higher stages of gastric cancer. …”
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Determination of Pear Cultivars (Pyrus communis L.) Based on Colour Change Levels by Using Data Mining by Dilara Gerdan, Abdullah Beyaz, Mustafa Vatandaş

“…The relationship between fruit hardness and colour change were also demonstrated. The predictions were done supervised machine learning algorithms (Decision Tree and Neural Networks with Meta-Learning Techniques; Majority Voting and Random Forest) by using KNIME Analytics software. …”
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Hybridize Machine Learning Methods and Optimization Techniques to Analyze and Repair Welding Defects via Digital Twin of Jidoka Simulator by Ahmed M. Abed, Tamer S. Gaafar

“…Hybridising the Random-Forest algorithm with Dingo optimisation and called Regulated Random Forest (RRF) to precisely identify defect clusters and then predict the welding defect growth rate (<inline-formula> <tex-math notation="LaTeX">$\boldsymbol {{R}_{s}}$ </tex-math></inline-formula>) using the Cat-boost optimiser, which is enhanced by a beetle search mechanism called CatBAS. …”
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G-OnRamp: Generating genome browsers to facilitate undergraduate-driven collaborative genome annotation. by Luke Sargent, Yating Liu, Wilson Leung, Nathan T Mortimer, David Lopatto, Jeremy Goecks, Sarah C R Elgin

“…Despite advances in computational gene prediction algorithms, most eukaryotic genomes still benefit from manual gene annotation. …”
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Deep learning radiomics based on MRI for differentiating tongue cancer T - staging by Zhaoyi Lu, Bowen Zhu, Hang Ling, Xi Chen

“…Abstract Objective To develop a deep learning-based MRI model for predicting tongue cancer T-stage. Methods This retrospective study analyzed clinical and MRI data from 579 tongue cancer patients (Xiangya Cancer Hospital and Jiangsu Province Hospital). …”
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Study on the interaction characteristics between pot seedling and planter based on hanging cup transplanter by Hongbin Bai, Fandi Zeng, Qiang Su, Ji Cui, Xuying Li

“…Comparative analysis reveals that the GA-BP algorithm demonstrates superior performance in ensuring model accuracy and stability, exhibiting better fitting performance with relative error rates between target and predicted values ranging from 2.25 to 10.54%. …”
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Dengue Contingency Planning: From Research to Policy and Practice. by Silvia Runge-Ranzinger, Axel Kroeger, Piero Olliaro, Philip J McCall, Gustavo Sánchez Tejeda, Linda S Lloyd, Lokman Hakim, Leigh R Bowman, Olaf Horstick, Giovanini Coelho

“…Additionally, a computer-assisted early warning system, which enables countries to identify and respond to context-specific variables that predict forthcoming dengue outbreaks, has been developed.…”
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Screening and validation of diagnostic markers for keloids via bioinformatics analysis by Ze Wang, Bo Hu, Wenfei Li, Tengxiao Ma, Lei Li

“…Drug small molecules and compounds were predicted online, and molecular docking was performed. …”
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Machine learning insights into scapular stabilization for alleviating shoulder pain in college students by Omar M. Mabrouk, Doaa A. Abdel Hady, Tarek Abd El-Hafeez

“…This study investigates the prediction of the impact of scapular stability exercises in treating non-specific shoulder pain, leveraging advanced machine learning techniques for comprehensive evaluation and analysis. …”
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ENDOCRINE PANCREATIC FUNCTION IN ACUTE PANCREATITIS by P. V. Novokhatny

“…Serological tests of pancreatic polypeptide promising for early diagnosis and prediction of the outcome of acute pancreatitis.…”
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Klasifikasi Multilabel Pada Gaya Belajar Siswa Sekolah Dasar Menggunakan Algoritma Machine Learning by I Kadek Nicko Ananda, Ni Putu Novita Puspa Dewi, Ni Wayan Marti, Luh Joni Erawati Dewi

“…Based on testing, the best performance was obtained on the Decision Tree model with a hamming loss of 0.014, which indicates a very low prediction error rate per individual label.  A recall value of 99% indicates that the model is able to detect almost all positive labels correctly, and an F1-score of 98% indicates that the model has excellent and balanced performance, without bias against both positive and negative label predictions. …”
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Deepmol: an automated machine and deep learning framework for computational chemistry by João Correia, João Capela, Miguel Rocha

“…DeepMol rapidly and automatically identifies the most effective data representation, pre-processing methods and model configurations for a specific molecular property/activity prediction problem. On 22 benchmark datasets, DeepMol obtained competitive pipelines compared with those requiring time-consuming feature engineering, model design and selection processes. …”
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Epigenetic age and fertility timeline: testing an epigenetic clock to forecast in vitro fertilization success rate by Davide Marinello, Marco Reschini, Giorgia Di Stefano, Giorgia Carullo, Maíra Casalechi, Letizia Tarantini, Benedetta Albetti, Valentina Bollati, Paola Viganò, Edgardo Somigliana, Letizia Li Piani

“…Conclusions Epigenetic clocks may enhance IVF success prediction, particularly in women between 31 and 35. …”
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Cervical cancer screening uptake and its associated factor in Sub-Sharan Africa: a machine learning approach by Fetlework Gubena Arage, Zinabu Bekele Tadese, Eliyas Addisu Taye, Tigist Kifle Tsegaw, Tsegasilassie Gebremariam Abate, Eyob Akalewold Alemu

“…These algorithms were employed to predict cervical cancer screening uptake. …”
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Strata pressure behavior law and regional-level intelligent early warning for high-intensity mining in extended working faces of medium-thick coal seams in Yuheng mining area by Feng CUI, Cheng ZONG, Xingping LAI, Tao XIE, Yunhu BAI, Yi ZHANG, Chong JIA, Hao WANG, Zhong LUO, Hao LIU

“…Achieving intelligent prediction of roof pressure is of great significance for ensuring the safe production of mines. …”
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Research advancements in the Use of artificial intelligence for prenatal diagnosis of neural tube defects by Maryam Yeganegi, Mahsa Danaei, Sepideh Azizi, Fatemeh Jayervand, Reza Bahrami, Seyed Alireza Dastgheib, Heewa Rashnavadi, Ali Masoudi, Amirmasoud Shiri, Kazem Aghili, Mahood Noorishadkam, Hossein Neamatzadeh

“…This review explores AI and machine learning (ML) in the early detection, prediction, and assessment of neural tube defects (NTDs) through prenatal ultrasound imaging. …”
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Diagnostic Value of F-FDG PET/CT Radiomics in Lymphoma: A Systematic Review and Meta-Analysis by Chaoying Liu MD, Jun Zhao PhD, Heng Zhang PhD, Xinye Ni PhD

“…Six meta-regressions were conducted on study performance, considering sample size, image modality, region of interest (ROI) selection, ROI segmentation, radiomics mode, and algorithms. Results In total, 20 studies classified as type 2a or above according to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement were included for this systematic review and meta-analysis. …”
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Cystic Fibrosis Newborn Screening: A Systematic Review-Driven Consensus Guideline from the United States Cystic Fibrosis Foundation by Meghan E. McGarry, Karen S. Raraigh, Philip Farrell, Faith Shropshire, Karey Padding, Cambrey White, M. Christine Dorley, Steven Hicks, Clement L. Ren, Kathryn Tullis, Debra Freedenberg, Q. Eileen Wafford, Sarah E. Hempstead, Marissa A. Taylor, Albert Faro, Marci K. Sontag, Susanna A. McColley

“…Newborn screening for cystic fibrosis (CF) has been universal in the US since 2010; however, there is significant variation among newborn screening algorithms. Systematic reviews were used to develop seven recommendations for newborn screening program practices to improve timeliness, sensitivity, and equity in diagnosing infants with CF: (1) The CF Foundation recommends the use of a floating immunoreactive trypsinogen (IRT) cutoff over a fixed IRT cutoff; (2) The CF Foundation recommends using a very high IRT referral strategy in CF newborn screening programs whose variant panel does not include all CF-causing variants in CFTR2 or does not have a variant panel that achieves at least 95% sensitivity in all ancestral groups within the state; (3) The CF Foundation recommends that CF newborn screening algorithms should not limit <i>CFTR</i> variant detection to the F508del variant or variants included in the American College of Medical Genetics-23 panel; (4) The CF Foundation recommends that CF newborn screening programs screen for all CF-causing <i>CFTR</i> variants in CFTR2; (5) The CF Foundation recommends conducting <i>CFTR</i> variant screening twice weekly or more frequently as resources allow; (6) The CF Foundation recommends the inclusion of a <i>CFTR</i> sequencing tier following IRT and <i>CFTR</i> variant panel testing to improve the specificity and positive predictive value of CF newborn screening; (7) The CF Foundation recommends that both the primary care provider and the CF specialist be notified of abnormal newborn screening results. …”
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