Determination of high-confidence germline genetic variants in next-generation sequencing through machine learning models: an approach to reduce the burden of orthogonal confirmatio... by Muqing Yan, Qiandong Zeng, Zhenxi Zhang, Patricia Okamoto, Stanley Letovsky, Angela Kenyon, Natalia Leach, Jennifer Reiner

“…Conclusions Machine-learning models can be trained to classify SNVs into high or low-confidence categories with high precision, thus reducing the level of confirmatory testing required. …”
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Adverse childhood experiences: terms, concepts, and study methods by Diana S. Shumskaia, Anna V. Trusova, Alexander O. Kibitov

“…However, to date, there is no unique and universally accepted approach to assessment of ACE, which may lead to controversy and low reproducibility of results.The purpose of this review is to examine and compare various methods of assessing and interpreting of ACE in order to identify optimal research algorithms within the current interpretation of the biopsychosocial model in psychiatry.This article reviews current terms, concepts, and methods of studying ACE. …”
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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

“…The machine learning algorithms used to build the model are Decision Tree, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). …”
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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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T2 <italic>Candida</italic> Panel: A Game Changer in Diagnosis of Fungal Infections by Devesh N Joshi, Bhaskar Shenoy

“…In low-prevalence settings, the positive predictive value of T2MR might not be enough to justify the initiation of antifungal treatment in itself.…”
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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

“…Moreover, IL16 was high-expressed but PRKCB was low-expressed in STAD cells, and silencing IL16 suppressed the invasion and migration of STAD cells. …”
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Prognostic, oncogenic roles, and pharmacogenomic features of AMD1 in hepatocellular carcinoma by Youliang Zhou, Yi Zhou, Jiabin Hu, Yao Xiao, Yan Zhou, Liping Yu

“…Methods Using the Cancer Genome Atlas (TCGA) liver hepatocellular carcinoma datasets, gene enrichment and immunological traits were compared between groups with high and low AMD1 expression. After altering AMD1 expression in HCC cells, cell viability, the clonal formation rate, and migration and invasion ability were detected. …”
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Machine-Learning-Based Classification of Electronic Devices Using an IoT Smart Meter by Paulo Eugênio da Costa Filho, Leonardo Augusto de Aquino Marques, Israel da S. Felix de Lima, Ewerton Leandro de Sousa, Márcio Eduardo Kreutz, Augusto V. Neto, Eduardo Nogueira Cunha, Dario Vieira

“…This study investigates the implementation of artificial intelligence (AI) algorithms on resource-constrained edge devices, such as ESP32 and Raspberry Pi, within the context of smart grid (SG) applications. …”
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Investigating Spatial Effects through Machine Learning and Leveraging Explainable AI for Child Malnutrition in Pakistan by Xiaoyi Zhang, Muhammad Usman, Ateeq ur Rehman Irshad, Mudassar Rashid, Amira Khattak

“…Third, XGBoost and Random Forest machine learning algorithms were employed to examine and validate the importance of the spatial lag component. …”
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A multimodal approach for enhanced disease management in cauliflower crops: integration of spectral sensors, machine learning models and targeted spraying technology by Rohit ANAND, Roaf Ahmad PARRAY, Indra MANI, Tapan Kumar KHURA, Harilal KUSHWAHA, Brij Bihari SHARMA, Susheel SARKAR, Samarth GODARA, Shideh MOJERLOU, Hasan MIRZAKHANINAFCHI

“…The chosen model was integrated with a low-volume sprayer (50‒150 L·ha‒1), equipped with an electronic control unit for targeted spraying based on sensor-detected regions. …”
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MOF-MoS2 nanosheets doped PEDOT:PSS for organic electrochemical transistors in enhanced glucose sensing and machine learning-based concentration prediction by Yali Sun, Yun Li, Yang Zhou, Ting Cai, Yuxuan Chen, Chao Zou, Han Song, Shenghuang Lin, Shenghua Liu

“…One of the main reasons is the relatively low electronic and ionic conductivity of the channel material. …”
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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

“…Morphological changes following EVAR will be analysed and compared based on preoperative and postoperative CT angiography (CTA) images (within 1 to 12 months, and at the last follow-up) using the AI-based software PRAEVAorta 2 (Nurea). Deep learning algorithms will be applied to stratify the risk of postoperative outcomes into low or high-risk categories. …”
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Exploring the Current Status of Risk Stratification in Hypertrophic Cardiomyopathy: From Risk Models to Promising Techniques by Alexandros Kasiakogias, Christos Kaskoutis, Christos-Konstantinos Antoniou, Stavros Georgopoulos, Dimitrios Tsiachris, Petros Arsenos, Alexandrina Kouroutzoglou, Dimitrios Klettas, Charalambos Vlachopoulos, Konstantinos Tsioufis, Konstantinos Gatzoulis

“…Some research suggests that integrating electrophysiological studies into traditional risk assessment models may further optimize risk prediction and significantly improve accuracy in detecting high risk patients. Novel cardiac imaging techniques, better understanding of the genetic substrate and artificial intelligence-based algorithms may prove promising for risk refinement. …”
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The Effect of Hearing Aid Amplification on Gait Parameters: A Pilot Study Using Ear-Worn Motion Sensors by Ann-Kristin Seifer, Arne Küderle, Kaja Strobel, Ronny Hannemann, Björn M. Eskofier

“…Additionally, we showed the potential of ear-worn sensors for detecting relevant gait changes. To achieve this, we used a hearing-aid-integrated accelerometer and our open-source EarGait framework comprising gait-related algorithms specifically developed for ear-worn sensors. …”
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Estimating vegetation indices and biophysical parameters for Central European temperate forests with Sentinel-1 SAR data and machine learning by Daniel Paluba, Bertrand Le Saux, Francesco Sarti, Přemysl Štych

“…In the comparison of ML models, the traditional ML algorithms, Random Forest Regressor and Extreme Gradient Boosting (XGB) slightly outperformed the Automatic Machine Learning (AutoML) approach, auto-sklearn, for all forest parameters, achieving high accuracies (R2 between 70% and 86%) and low errors (0.055–0.29 of mean absolute error). …”
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Precision analysis of NGC 2158 with Gaia DR3 by Nasser M. Ahmed, A. L. Tadross

“…The mass function MF for the cluster under study has been constructed using a step function with two power lows, $$\alpha _1$$ and $$\alpha _2$$ , rather than the single power low suggested by Salpeter. …”
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Use of ICT to Confront COVID-19 by Yousry Saber El Gamal

“…. , AI techniques, particularly machine learning algorithms, can also be used to correlate the patient’s data parameters with a specific drug’s usage. …”
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Anellovirus abundance as an indicator for viral metagenomic classifier utility in plasma samples by Gabriel Montenegro de Campos, Luan Gaspar Clemente, Alex Ranieri Jerônimo Lima, Eleonora Cella, Vagner Fonseca, João Paulo Bianchi Ximenez, Milton Yutaka Nishiyama, Enéas de Carvalho, Sandra Coccuzzo Sampaio, Marta Giovanetti, Maria Carolina Elias, Svetoslav Nanev Slavov

“…Nucleotide-based classifiers like CLARK and Kraken2 showed superior sensitivity, which is valuable for detecting emerging or rare viruses. At the same time, protein-based approaches such as DIAMOND and Kaiju proved robust for identifying known species with low variability.…”
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The prevalence of resistance-associated mutations to protease and reverse transcriptase inhibitors in treatment-naïve (HIV1)-infected individuals in Casablanca, Morocco by Khadija Bakhouch, Ahd Oulad-Lahcen, Rajae Bensghir, Mohamed Blaghen, Kamal Marhoum Elfilali, Sayeh Ezzikouri, Omar Abidi, Mohamed Hassar, Lahcen Wakrim

“…National Agency for AIDS Research (ANRS) and Stanford HIV database algorithms were used for interpretation of resistance data. …”
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A quantum machine learning framework for predicting drug sensitivity in multiple myeloma using proteomic data by M. Priyadharshini, B. Deevena Raju, A. Faritha Banu, P. Jagdish Kumar, V. Murugesh, Oleg Rybin

“…It demonstrates that quantum algorithms can perform complex biological data suggesting more reliable and accurate drug sensitivity predictions. …”
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