Target repositioning using multi-layer networks and machine learning: The case of prostate cancer by Milan Picard, Marie-Pier Scott-Boyer, Antoine Bodein, Mickaël Leclercq, Julien Prunier, Olivier Périn, Arnaud Droit

“…Using prostate cancer as a case study, the best features were identified and utilized to train machine learning algorithms to predict 5 novel promising therapeutic targets for prostate cancer: IGF2R, C5AR, RAB7, SETD2 and NPBWR1.…”
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Temperature-Responsive Microrobot for High-Temperature Sensing in Constrained Environments by Shaobo Ding, Junmin Liu, Jiaxu Dong, Rencheng Zhuang, Enbo Shi, Shutong Wang, Yuhang Xiao, Dekai Zhou, Longqiu Li, Xiaocong Chang

“…By inputting the observed color features into the trained model, the surrounding temperature can be quantitatively determined. …”
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Deep Learning Models and Fusion Classification Technique for Accurate Diagnosis of Retinopathy of Prematurity in Preterm Newborn by Nazar Salih, Mohamed Ksantini, Nebras Hussein, Donia Ben Halima, ali Abdul Razzaq, Sohaib Ahmed

“…   Retinopathy of prematurity (ROP) is the most common cause of irreversible childhood blindness, and its diagnosis and treatment rely on subjective grading based on retinal vascular features. However, this method is laborious and error-prone, so automated approaches are desirable for greater precision and productivity. …”
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Accurate deep-learning model to differentiate dementia severity and diagnosis using a portable electroencephalography device by Masahiro Hata, Takufumi Yanagisawa, Yuki Miyazaki, Hisaki Omori, Atsuya Hirashima, Yuta Nakagawa, Mitsuhiro Eto, Kenji Yoshiyama, Hideki Kanemoto, Byambadorj Nyamradnaa, Shusuke Yoshimoto, Kotaro Ezure, Shun Takahashi, Manabu Ikeda

“…We analyzed EEG data from 233 participants, including 119 HVs and 114 patients, and transformed the signals into frequency-domain features using a short-time Fourier transform. A customized transformer-based model was trained and evaluated using 10-fold cross-validation and a holdout dataset. …”
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Non-stereotypy (to species) in mysticete downsweeps by Paul Nguyen Hong Duc, Christine Erbe, Shyam Madhusudhana, Daniel Wilkes, Lachlan Gill, Cristina Tollefsen, Narissa de Bruin, Aiyana Erbeking, Curt Jenner, Micheline Jenner, Angela Recalde-Salas, Chandra P. Salgado Kent, Chandra P. Salgado Kent, Kautilya Srivastava, Kautilya Srivastava, Chong Wei, Robert McCauley

“…In an attempt to optimize a detector for Eastern Indian Ocean pygmy blue whale (EIOPBW) downsweeps, we tried a spectrogram correlator based on confirmed templates and a neural network trained on general blue whale D-calls followed by clustering algorithms. …”
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Development of an explainable prediction model for portal vein system thrombosis post-splenectomy in patients with cirrhosis by Caixia Dong, Guodong Li, Rui Zhou, Dou Qu, Duwei Dai, Junxia Zhao, Lingbo An, Xiaojie Song, Jiazhen Zhu, Zong Fang Li

“…The predictive model integrated 37 candidate predictors based on accessible clinical data, including demographic characteristics, disease features, imaging results, laboratory values, perioperative details and postoperative prophylactic therapies, and finally, eight predictors were selected for model construction. …”
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Classification of ductile cast iron specimens: a machine learning approach by A. De Santis, D. Iacoviello, V. Di Cocco, F. Iacoviello

“…In this paper an automatic procedure based on a machine learning approach is proposed to classify ductile cast iron specimens according to the American Society for Testing and Materials guidelines. …”
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AxonFinder: Automated segmentation of tumor innervating neuronal fibers by Kaoutar Ait-Ahmad, Cigdem Ak, Guillaume Thibault, Young Hwan Chang, Sebnem Ece Eksi

“…We present a novel deep learning-based approach for the automated segmentation of axons, AxonFinder, leveraging a U-Net model with a ResNet-101 encoder, based on a multiplexed imaging approach. …”
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Machine Learning Approach to Investigating Macrophage Polarization on Various Titanium Surface Characteristics by Changzhong Chen, Zhenhuan Xie, Songyu Yang, Haitong Wu, Zhisheng Bi, Qing Zhang, Yin Xiao

“…Objective: Current laboratory studies on the effect of biomaterial properties on immune reactions are incomplete and based on a single or a few combination features of the biomaterial design. …”
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Gradient Nanostructures and Machine Learning Synergy for Robust Quantitative Surface‐Enhanced Raman Scattering by Xiaoyu Zhao, Yuxia Wang, Yuting Liu, Xinyi Chen, Mingyu Cheng, Yaxin Wang, Jiahong Wen, Renxian Gao, Kun Zhang, Fengyi Zhang, Rufei Cui, Yongjun Zhang, Zengyao Wang, Bin Ai

“…To address these limitations, a novel SERS platform based on gradient nanostructures is developed using shadow sphere lithography, enabling the acquisition of diverse spectral features from a single analyte concentration under identical conditions. …”
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Development of the system calculation and analytical indicators characterizing the regional passenger transport by E. А. Makarova, S. V. Muktepavel

“…Development of marketing shares and new offers for passengers’ service are based on studying regional features of demand and relevant state of transport services for different types of passenger traffics. …”
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Joint Class and Domain Continual Learning for Decentralized Federated Processes by Chiara Lanza, Tinne Tuytelaars, Marco Miozzo, Eduard Angelats, Paolo Dini

“…Thanks to KD, the learning process can be performed without any prior information about the model architecture, i.e., every local model can be based on different architectures, which can also differ from the global. …”
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Improving optimal prompt learning through multilayer fusion and latent dirichlet allocation by Qinghua Chen, Qinghua Chen, Qinghua Chen, Jessica Korneder, Osamah A. Rawashdeh, Yanfeng Wang, Wing-Yue Geoffrey Louie

“…Recent advances in few-shot learning have demonstrated the potential of prompt-based techniques with pre-trained models, eliminating the need for extensive fine-tuning. …”
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Food and drink advertising along school children’s transport routes in Victoria, Australia and policy implications by Sherly X Li, Claire Hardi, Rebecca Godwin, Rachael Jinnette, Belinda Morley, Helen Dixon, Jane Martin

“…Stations were chosen based on annual patronage, area-based socio-economic area (SEA) and regionality). …”
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Using deep learning models to decode emotional states in horses by Romane Phelipon, Lea Lansade, Misbah Razzaq

“…We manually label the images to train the models in a supervised manner. We perform data exploration and use different cropping methods, mainly based on Yolo and Faster R-CNN, to create two new datasets: 1) the cropped body, and 2) the cropped head dataset. …”
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Robust Predictive Maintenance for Robotics via Unsupervised Transfer Learning by Arash Golibagh Mahyari, Thomas locher

“…In this paper, we propose a novel solution based on transfer learning which addresses a well-known challenge in predictive maintenance algorithms by passing the knowledge of the trained model from one task to another in order to prevent the need for retraining and to eliminate such false alarms. …”
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Distribution of Remaining Oil and Enhanced Oil Recovery Strategy for Carboniferous Buried-Hill Reservoirs in Junggar Basin by Qijun Lv, Zhaowen Shi, Linsong Cheng, Chunjing Zan

“…Finally, a fracture model of the study area was established based on the collaborative constraints of fracture density and three-dimensional seismic attributes. …”
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Juvenile greylag geese (Anser anser) discriminate between individual siblings. by Isabella B R Scheiber, Aileen Hohnstein, Kurt Kotrschal, Brigitte M Weiss

“…Social relationships within a goose flock thus may indeed be based on recognition of unique features of individual conspecifics.…”
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Machine learning for prediction of 30-day mortality in patients with advanced cancer: comparing pan-cancer and single-cancer models by S. Bjerregaard-Michelsen, L.Ø. Poulsen, A. Bjerrum, M. Bøgsted, C. Vesteghem

“…Clinical data were used to train, validate, and test a pan-cancer model and 10 single-cancer models based on the eXtreme Gradient Boosting (XGBoost) algorithm. …”
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Machine learning predicts and provides insights into milk acidification rates of Lactococcus lactis. by Signe Tang Karlsen, Tammi Camilla Vesth, Gunnar Oregaard, Vera Kuzina Poulsen, Ole Lund, Gemma Henderson, Jacob Bælum

“…These conditions were added as features in each RF model. The four models were trained on 257 strains, and the correlation between the measured Vmax and the predicted Vmax was evaluated with Pearson Correlation Coefficients (PC) on a separate dataset of 85 strains. …”
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