Identification of specific metabolic pathways as druggable targets regulating the sensitivity to cyanide poisoning. by Patrick Y Sips, Xu Shi, Gabriel Musso, Anjali K Nath, Yanbin Zhao, Jason Nielson, Jordan Morningstar, Amy E Kelly, Brittney Mikell, Eva Buys, Vikhyat Bebarta, Jared Rutter, V Jo Davisson, Sari Mahon, Matthew Brenner, Gerry R Boss, Randall T Peterson, Robert E Gerszten, Calum A MacRae

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Leveraging advanced technologies for early detection and diagnosis of oral cancer: Warning alarm by Saantosh Saravanan, N. Aravindha Babu, Lakshmi T, Mukesh Kumar Dharmalingam Jothinathan

“…Oral cancer as a global health problem becomes more difficult to deal with, especially since developing countries are the hardest hit due to massive rates of mortality and morbidity. …”
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Identification of hub immune-related genes and construction of predictive models for systemic lupus erythematosus by bioinformatics combined with machine learning by Su Zhang, Weitao Hu, Yuchao Tang, Hongjie Lin, Xiaoqing Chen

“…Three machine learning algorithms were applied to DE-IRGs to screen for hub DE-IRGs. …”
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The use of patient-reported outcome measures to improve patient-related outcomes – a systematic review by Joshua M. Bonsel, Ademola J. Itiola, Anouk S. Huberts, Gouke J. Bonsel, Hannah Penton

“…Conclusions The use of PROMs at the individual level has matured considerably. Monitoring/screening applications seem promising particularly for diseases for which treatment algorithms rely on the experienced symptom burden by patients. …”
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Ultrasound-based machine learning model to predict the risk of endometrial cancer among postmenopausal women by Yi-Xin Li, Yu Lu, Zhe-Ming Song, Yu-Ting Shen, Wen Lu, Min Ren

“…This non-invasive approach offers a more accurate and reliable tool for EC screening in postmenopausal women. …”
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Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural Network by Aythem Khairi Kareem, Mohammed M. AL-Ani, Ahmed Adil Nafea

“…Results strongly suggest that 1D CNNs have shown improved accuracy in the classification of ASD compared to traditional machine learning algorithms, on all these datasets with higher accuracy of 99.45%, 98.66%, and 90% for Autistic Spectrum Disorder Screening in Data for Adults, Children, and Adolescents respectively as they are better suited for the analysis of time series data commonly used in the diagnosis of this disorder …”
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NLP for computational insights into nutritional impacts on colorectal cancer care by Shengnan Gong, Xiaohong Jin, Yujie Guo, Jie Yu

“…Colorectal cancer (CRC) is one of the most prominent cancers globally, with its incidence rising among younger adults due to improved screening practices. However, existing algorithms for CRC prediction are frequently trained on datasets that primarily reflect older persons, thus limiting their usefulness in more diverse populations. …”
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From Molecules to Medicines: The Role of AI-Driven Drug Discovery Against Alzheimer’s Disease and Other Neurological Disorders by Mashael A. Alghamdi

“…Artificial intelligence (AI) tools are of considerable interest in modern drug discovery processes and, by exploiting machine learning (ML) algorithms and deep learning (DL) tools, as well as data analytics, can expedite the identification of new drug targets and potential lead molecules. …”
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Remote Sensing Techniques for Assessing Snow Avalanche Formation Factors and Building Hazard Monitoring Systems by Natalya Denissova, Serik Nurakynov, Olga Petrova, Daniker Chepashev, Gulzhan Daumova, Alena Yelisseyeva

“…The analysis involved screening relevant studies on remote sensing, avalanche dynamics, and data processing techniques. …”
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Utilizing machine learning models for predicting outcomes in acute pancreatitis: development and validation in three retrospective cohorts by Kaier Gu, Yang Liu

“…Six ML algorithms were employed to construct predictive models. …”
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Interpretable model based on MRI radiomics to predict the expression of Ki-67 in breast cancer by Li Zhang, Qinglin Du, Mengyi Shen, Xin He, Dingyi Zhang, Xiaohua Huang

“…Based on clinical-imaging features and DCE-MRI radiomics, the interpretable machine learning model can accurately predict the expression of Ki-67 in BC. Combining the SHAP algorithm with the model improves its interpretability, which may assist clinicians in formulating more accurate treatment strategies.…”
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Peripherex Home Visual Field Demonstrates High Test-Retest Reliability, Validity by Schweitzer J, Ibach M, Berdahl J, Daoud M, Daoud YA, Kempinski Y, Goldberg JL

“…These data support in-home use of the PRX VFT by the eye care provider for screening and monitoring in glaucoma, and may be extended to the screening and monitoring of other retinal, optic nerve and neurological diseases.Keywords: visual field test, glaucoma, home healthcare, prospective clinical trial…”
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Development of an MRI based artificial intelligence model for the identification of underlying atrial fibrillation after ischemic stroke: a multicenter proof-of-concept analysisRes... by Zijie Zhang, Yang Ding, Kaibin Lin, Wenli Ban, Luyue Ding, Yudong Sun, Chuanliang Fu, Yihang Ren, Can Han, Xue Zhang, Xiaoer Wei, Shundong Hu, Yuwu Zhao, Li Cao, Jun Wang, Saman Nazarian, Ying Cao, Lan Zheng, Min Zhang, Jianliang Fu, Jingbo Li, Xiang Han, Dahong Qian, Dong Huang

“…Between January 1, 2018 and December 31, 2021, 510 patients were retrospectively enrolled for algorithm development and performance was measured using fivefold cross-validation. …”
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Predicting onset of myopic refractive error in children using machine learning on routine pediatric eye examinations only by Yonina Ron, Tchelet Ron, Naomi Fridman, Anat Goldstein

“…These models utilize easily accessible, non-invasive data gathered during standard eye clinic visits, deliberately excluding more complex measurements such as axial length or corneal curvature. …”
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Design of public space guide system based on augmented reality technology by Pu Jiao, Limin Ran

“…The research is based on imaging techniques using augmented reality technology and camera image capture. Then, it uses screen error algorithms and scale-invariant feature transformation operators to test the quality of scene spatial models. …”
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The Effect of Arctic Sea‐Ice Loss on Extratropical Cyclones by Stephanie Hay, Matthew D. K. Priestley, Hao Yu, Jennifer L. Catto, James A. Screen

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Recent Trends and Advances in Utilizing Digital Image Processing for Crop Nitrogen Management by Bhashitha Konara, Manokararajah Krishnapillai, Lakshman Galagedara

“…A total of 95 articles remained after the screening and selection process. Interest in integrating machine learning and deep learning algorithms with DIP has increased, with the frequently used algorithms—Random Forest, Support Vector Machine, Extreme Gradient Boost, and Convolutional Neural Networks—achieving higher prediction accuracy levels. …”
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Biomarker-driven drug repurposing for NAFLD-associated hepatocellular carcinoma using machine learning integrated ensemble feature selection by Subhajit Ghosh, Sukhen Das Mandal, Subarna Thakur

“…The incidence of non-alcoholic fatty liver disease (NAFLD), encompassing the more severe non-alcoholic steatohepatitis (NASH), is rising alongside the surges in diabetes and obesity. …”
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Prevalence associations of various risk factors and arterial hypertension in male open urban population (by a one stage epidemiological study) by Е. V. Akimova, M. Yu. Akimov, E. I. Gakova, M. М. Kayumova, V. V. Gafarov, V. A. Kuznetsova

“…For the analysis of AH the data from cardiological screening was used, and surveying by psychosocial methods in algorithms of MONICA-MOPSY.Results. …”
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Detectability of Liver Steatosis and Fibrosis with Transient Elastography and Controlled Attenuation Parameter in Residents of St. Petersburg by V. P. Kovyazina, K. L. Raikhelson, M. K. Prashnova, E. V. Pazenko, L. K. Palgova, E. A. Kondrashina

“…Petersburg warrants improved diagnostic algorithms and routine preventive measures. Transient elastography with the controlled attenuation parameter estimation provides a convenient non-invasive screening for hepatic fibrosis and steatosis.…”
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