An inductive learning-based method for predicting drug-gene interactions using a multi-relational drug-disease-gene graph by Jian He, Yanling Wu, Linxi Yuan, Jiangguo Qiu, Menglong Li, Xuemei Pu, Yanzhi Guo

“…In view of this, this study, for the first time, proposed an inductive learning-based model for the precise identification of unseen DGIs. …”
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Spatial parameters for transportation: A multi-modal approach for modelling the urban spatial structure using deep learning and remote sensing by Dorothee Stiller, Michael Wurm, Thomas Stark, Pablo d'Angelo, Karsten Stebner, Stefan Dech, Hannes Taubenböck

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Engineered feature embeddings meet deep learning: A novel strategy to improve bone marrow cell classification and model transparency by Jonathan Tarquino, Jhonathan Rodríguez, David Becerra, Lucia Roa-Peña, Eduardo Romero

“…Cytomorphology evaluation of bone marrow cell is the initial step to diagnose different hematological diseases. …”
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Retracted: Prenatal Exposure to Classical Music Improves Learning and Memory in Adult Rats <subtitle>Possible Anxiolytic or Antistress Effects</subtitle> by Yuantian Zhang, Morvarid Vatanpour, Marjan Vatanpour, Sepideh Tayyebi, Omid Baghani, Maliheh Abbasnejad

“…Background: Exposure to music during pregnancy enhances brain development and improves learning in neonatal rats. Methods: In these experiments, we examined the effects of exposure to silence, hard rock, classical, and rap music in utero plus 60 days postpartum on learning and memory in adult Wistar rats. …”
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Temporal waveform denoising using deep learning for injection laser systems of inertial confinement fusion high-power laser facilities by Wei Chen, Xinghua Lu, Wei Fan, Xiaochao Wang

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Syngeneic Transplantation of Olfactory Ectomesenchymal Stem Cells Restores Learning and Memory Abilities in a Rat Model of Global Cerebral Ischemia by Antoine D. Veron, Cécile Bienboire-Frosini, Stéphane D. Girard, Kevin Sadelli, Jean-Claude Stamegna, Michel Khrestchatisky, Jennifer Alexis, Patrick Pageat, Pietro Asproni, Manuel Mengoli, François S. Roman

“…OE-MSCs were collected from syngeneic F344 rats. After a two-step global cerebral ischemia, inducing hippocampal lesions, learning abilities were evaluated using an olfactory associative discrimination task. …”
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Prognostic risk modeling of endometrial cancer using programmed cell death-related genes: a comprehensive machine learning approach by Tianshu Chen, Yuhan Yang, Zhizhong Huang, Feng Pan, Zhendi Xiao, Kunxue Gong, Wenguang Huang, Liu Xu, Xueqin Liu, Caiyun Fang

“…This study aimed to develop a robust predictive model integrating programmed cell death-related genes and advanced machine learning techniques. Methods Utilizing transcriptomic data from TCGA-UCEC and GSE119041 datasets, we employed a comprehensive approach involving 117 machine learning algorithms. …”
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A deep learning and IoT-driven framework for real-time adaptive resource allocation and grid optimization in smart energy systems by Arvind R. Singh, M. S. Sujatha, Akshay D. Kadu, Mohit Bajaj, Hailu Kendie Addis, Kota Sarada

“…The framework also provides a foundation for future advancements, including integration with edge computing, cybersecurity measures, and reinforcement learning enhancements, marking a significant step forward in smart grid optimization.…”
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A Risk Prediction Model (CMC-AKIX) for Postoperative Acute Kidney Injury Using Machine Learning: Algorithm Development and Validation by Ji Won Min, Jae-Hong Min, Se-Hyun Chang, Byung Ha Chung, Eun Sil Koh, Young Soo Kim, Hyung Wook Kim, Tae Hyun Ban, Seok Joon Shin, In Young Choi, Hye Eun Yoon

“…This model’s integration into a user-friendly website enhances its clinical utility, offering a significant step forward in personalized patient care and risk management.…”
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Characterization of Irrigated Rice Cultivation Cycles and Classification in Brazil Using Time Series Similarity and Machine Learning Models with Sentinel Imagery by Andre Dalla Bernardina Garcia, Ieda Del’Arco Sanches, Victor Hugo Rohden Prudente, Kleber Trabaquini

“…For the classification step in the proposed pipeline, we utilized five traditional machine learning models available on the Google Earth Engine platform to determine which had the best performance. …”
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Application of Machine Learning Techniques to Distinguish between Mare, Cryptomare, and Light Plains in Central Lunar South Pole−Aitken Basin by Frank C. Chuang, Matthew D. Richardson, Jennifer L. Whitten, Daniel P. Moriarty, Deborah L. Domingue

“…Other data were considered, but albedo and topography data were key in distinguishing between maria, cryptomaria, and light plains. A two-step image classification approach was applied to the data sets, an unsupervised K-Means algorithm followed by a supervised Maximum Likelihood Classification (MLC) algorithm. …”
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Short-term residential electricity consumption forecast considering the cumulative effect of temperature, dual decomposition technology and integrated deep learning by Lanlan Wang, Yong Lin, Tingting Song, Yuchun Chen, Kai Li, Junchao Ran

“…To meet this challenge, this paper designs the structure of this three-step residential electricity consumption forecasting. …”
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Data Collection for Automatic Depression Identification in Spanish Speakers Using Deep Learning Algorithms: Protocol for a Case-Control Study by Luis F Brenes, Luis A Trejo, Jose Antonio Cantoral-Ceballos, Daniela Aguilar-De León, Fresia Paloma Hernández-Moreno

“…ConclusionsThis research marks a significant step toward the objective and automated classification of depression in voice recordings. …”
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Petrophysical evaluation of clastic formations in boreholes with incomplete well log dataset by using joint inversion technique and machine learning algorithms by Felipe Santana-Román, Ambrosio Aquino López, Manuel Romero Salcedo (+), Raúl del Valle García, Oscar Campos Enriquez

“…., volumes of laminar, structural and disperse shale) in clastic rocks from incomplete well log data we followed three approaches which are based on a hierarchical model, and on a joint inversion technique: 1) Available well log data (excluding the incomplete well log) are used to train machine learning algorithms to generate a predictive model; 2) the first step of the second approach machine learning algorithms are used to generate the missing data which are subsequently included a joint inversion; 3) in the third approach, machine learning process is used to estimate the missing data which are subsequently included in the prediction of the petrophysical properties. …”
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Machine learning integration of multimodal data identifies key features of circulating NT-proBNP in people without cardiovascular diseases by Zhiyuan Ning, Xuanfei Jiang, Huan Huang, Honggang Ma, Ji Luo, Xiangyan Yang, Bing Zhang, Ying Liu

“…The optimal features predicting NT-proBNP levels were identified using univariate and step-forward multivariate models. Weighted least squares regression (WLS) was applied for supplementary analysis. …”
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Machine learning-based integration develops relapse related signature for predicting prognosis and indicating immune microenvironment infiltration in breast cancer by Junyi Li, Shixin Li, Dongpo Zhang, Yibing Zhu, Yue Wang, Xiaoxiao Xing, Juefei Mo, Yong Zhang, Daixiang Liao, Jun Li

“…To address these limitations, this study systematically analyzed RNA-seq high-throughput data and combined 10 machine learning algorithms to construct 117 models. The optimal algorithm combination, StepCox[both] and ridge regression, was identified, and an immune-related gene signature (IRGS) composed of 12 genes was developed. …”
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A groupwise multiresolution network for DCE-MRI image registration by Anika Strittmatter, Meike Weis, Frank G. Zöllner

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Development and Implementation of Short Courses to Support the Establishment of a Prehospital System in Sub-Saharan Africa: Lessons Learned from Tanzania by Hendry R. Sawe, Juma A. Mfinanga, Samwel Kisakeni, Patrick Shao, Paulina Nkondora, Libby White, Christina Bollinger, Irene B. Kulola, Upendo N. George, Michael S. Runyon, Erin Noste

“…We aim to describe the process of designing and implementing the multicadre/provider prehospital short courses. The lessons learned can help inform similar initiatives in low- and middle-income countries. …”
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Anticipated Acceptability of Blended Learning Among Lay Health Care Workers in Malawi: Qualitative Analysis Guided by the Technology Acceptance Model by Tiwonge E Mbeya-Munkhondya, Caroline J Meek, Mtisunge Mphande, Tapiwa A Tembo, Mike J Chitani, Milenka Jean-Baptiste, Caroline Kumbuyo, Dhrutika Vansia, Katherine R Simon, Sarah E Rutstein, Victor Mwapasa, Vivian Go, Maria H Kim, Nora E Rosenberg

“…Our next step is to integrate these findings into a blended learning package and examine perceived acceptability of the package in the context of a cluster randomized controlled trial.…”
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Automated Recipe Generation AI Tool from Food Images using Machine Learning. Approach for Ingredient Extraction and Cooking Instructions by Lwamaza, Eddy, Nuwasiima, Happy

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