Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors. by Anna Cichonska, Balaguru Ravikumar, Elina Parri, Sanna Timonen, Tapio Pahikkala, Antti Airola, Krister Wennerberg, Juho Rousu, Tero Aittokallio

“…Here, we therefore introduce and carefully test a systematic computational-experimental framework for the prediction and pre-clinical verification of drug-target interactions using a well-established kernel-based regression algorithm as the prediction model. To evaluate its performance, we first predicted unmeasured binding affinities in a large-scale kinase inhibitor profiling study, and then experimentally tested 100 compound-kinase pairs. …”
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Machine learning models integrating intracranial artery calcification to predict outcomes of mechanical thrombectomy by Guangzong Li, Yuesen Zhang, Di Li, Manhong Zhao, Lin Yin

“…Preoperative clinical and radiological data, including a quantitative assessment of IAC, were systematically collected. Eleven ML algorithms were trained and validated using Python, and external validation and performance evaluations were conducted. …”
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Interdisciplinary medical education practices: building a case-driven interdisciplinary simulation system based on public datasets by Kangli Qiu, Tianshu Zeng, Wenfang Xia, Miaomiao Peng, Wen Kong

“…Finally, we used a modified version of the System Usability Scale (SUS) questionnaire to evaluate the interdisciplinary simulation system. Results Five cases of gout, gastritis, cirrhosis, inflammatory bowel disease, and chronic obstructive pulmonary disease were utilized to master the standard process of data analysis across various datasets from multiple dimensions of the model algorithm, data analysis, and result display. …”
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Binarized Neural Networks for Resource-Efficient Spike Sorting by Luca M. Meyer, Majid Zamani, Andreas Demosthenous

“…Subsequently, the deep binarized model and an equally sized full-precision model were trained and evaluated using experimentally obtained and synthetic spike waveforms. …”
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CropsDisNet: An AI-Based Platform for Disease Detection and Advancing On-Farm Privacy Solutions by Mohammad Badhruddouza Khan, Salwa Tamkin, Jinat Ara, Mobashwer Alam, Hanif Bhuiyan

“…The integration of a differential privacy algorithm into our CropsDisNet model could establish the benefits of automated crop disease classification without compromising on-farm data privacy by reducing training data leakage. …”
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Are vision transformers replacing convolutional neural networks in scene interpretation?: A review by N. Arockia Rosy, K. Balasubadra, K. Deepa

“…Technical advancements in computer vision have been overwhelmingly successful, primarily driven by the harnessing of deep learning algorithms. Recently, Vision Transformers (ViTs) have emerged as a viable alternative to conventional neural networks. …”
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Integrated multi-omics analysis and predictive modeling of heart failure using sepsis-related gene signature. by Yiping Lang, Tianyu Liang, Fei Li

“…The predictive model, incorporating six key genes, demonstrated superior performance compared to individual genes across both training and validation datasets with the AUC value of the risk score above 0.9, significantly higher than that of a single gene. …”
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Prediction of suspended sediment concentration in fluvial flows using novel hybrid deep learning model by Sadra Shadkani, Yousef Hemmatzadeh, Amirreza Pak, Soroush Abolfathi

“…The dataset was partitioned into training (70%, 2,747 d) and testing (30%, 1,178 d) subsets, with daily temporal resolution. …”
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Comparative performance of deep learning architectures for diabetic peripheral neuropathy detection using corneal confocal microscopy: a retrospective single-centre study by Yuyang Deng, Wenqu Chen, Weihuang Xu, Jianzhang Hu

“…Objectives This study aims to develop a deep learning algorithm (DLA) using the InceptionV3 architecture for effective diabetic peripheral neuropathy (DPN) screening via corneal confocal microscopy (CCM) images.Design Retrospective study.Setting Ophthalmology Centre of General Hospital.Participants 127 participants enrolled: 33 healthy participants, 57 diabetic patients with DPN (DPN+) and 37 diabetic patients without DPN (DPN−).Interventions Not applicable.Main outcome measures The CCM image dataset, which was collected from participants (with five images per eye), was randomly divided into training, validation and test subsets in a 7:1:2 ratio. …”
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CT-based radiomics features for the differential diagnosis of nodular goiter and papillary thyroid carcinoma: an analysis employing propensity score matching by Haiming Zhang, Zhenyu Li, Fengtao Zhang, Hengguo Li

“…Feature selection was carried out utilizing the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm to generate the radiomics score (Rad-score). …”
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A soft voting ensemble classifier for early prediction and diagnosis of occurrences of major adverse cardiovascular events for STEMI and NSTEMI during 2-year follow-up in patients... by Syed Waseem Abbas Sherazi, Jang-Whan Bae, Jong Yun Lee

“…We generated each ML-based model with the best hyper-parameters, evaluated by 5-fold stratified cross-validation, and then verified by test dataset. …”
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CLUSTERING ANALYSIS FOR GROUPING SUB-DISTRICTS IN BOJONEGORO DISTRICT WITH THE K-MEANS METHOD WITH A VARIETY OF APPROACHES by Denny Nurdiansyah, Mochamad Nizar Palefi Ma'ady, Yuana Sukmawaty, Muchammad Chandra Cahyo Utomo, Tia Mutiani

“…The performance of the cluster method is evaluated by measuring the average distance within the cluster. …”
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Unsupervised method for representation transfer from one brain to another by Daiki Nakamura, Shizuo Kaji, Ryota Kanai, Ryusuke Hayashi

“…Additionally, we reconstructed images from individuals’ data without training personalized decoders by performing brain representation transfer. …”
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Classification of Structural and Functional Development Stage of Cardiomyocytes Using Machine Learning Techniques by V. R. Bondarev, K. O. Ivanko, N. G. Ivanushkina

“…The model is evaluated based on the confusion matrix and the heat maps of different convolutional layers are analyzed. …”
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An End-to-End Solution for Large-Scale Multi-UAV Mission Path Planning by Jiazhan Gao, Liruizhi Jia, Minchi Kuang, Heng Shi, Jihong Zhu

“…Additionally, we integrate a Multi-Start Greedy Rollout Baseline to evaluate diverse trajectories via parallelized greedy searches, thereby reducing policy gradient variance and improving training stability. …”
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Anomaly Detection Dataset for Industrial Control Systems by Alireza Dehlaghi-Ghadim, Mahshid Helali Moghadam, Ali Balador, Hans Hansson

“…Using Machine Learning (ML) for Intrusion Detection Systems (IDS) is a promising approach for ICS cyber protection, but the lack of suitable datasets for evaluating ML algorithms is a challenge. Although a few commonly used datasets may not reflect realistic ICS network data, lack necessary features for effective anomaly detection, or be outdated. …”
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Enhancing PV feed-in power forecasting through federated learning with differential privacy using LSTM and GRU by Pascal Riedel, Kaouther Belkilani, Manfred Reichert, Gerd Heilscher, Reinhold von Schwerin

“…Consequently, federated LSTM and GRU models are compared with centralized and local baseline models using rolling 5-fold cross-validation to evaluate their respective performances. By leveraging advanced FL algorithms such as FedYogi and FedAdam, we propose a method that not only predicts sequential energy data with high accuracy, achieving an R2 of 97.68%, but also adheres to stringent privacy standards, offering a scalable solution for the challenges of smart grids analytics, thus clearly showing that the proposed approach is promising and worth being pursued further.…”
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HyCoViT: Hybrid Convolution Vision Transformer With Dynamic Dropout for Enhanced Medical Chest X-Ray Classification by Omid Almasi Naghash, Nam Ling, Xiang Li

“…To address overfitting in data-scarce scenarios, we introduce a Dynamic Dropout (DD) algorithm that adaptively adjusts the dropout rate during training. …”
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Comparative Analysis of Automated Machine Learning for Hyperparameter Optimization and Explainable Artificial Intelligence Models by Muhammad Salman Khan, Tianbo Peng, Hanzlah Akhlaq, Muhammad Adeel Khan

“…This study addresses critical gaps in the current literature by conducting a comprehensive comparative analysis of AutoML frameworks for hyperparameter optimization and evaluating the effectiveness of various explainability techniques for enhancing model interpretability. …”
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Informational Approaches in Modelling Social and Economic Relations: Study on Migration and Access to Services in the European Union by Florentina-Loredana Dragomir-Constantin, Camelia Madalina Beldiman, Monica Laura Zlati

“…The applied methodology includes attribute distribution analysis, identification of hidden patterns through clustering algorithms (K-Means and Expectation-Maximisation) and training of classifiers using regression decision trees with linear leaf models (M5P) corresponding to interdependent data processing and integration modules, exploratory analysis module, machine learning and decision-making modules, oriented to support public policies through explainable scenarios and predictive-evaluative structures. …”
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