AI-enabled drug prediction and gene network analysis reveal therapeutic use of vorinostat for Rett Syndrome in preclinical models by Richard Novak, Tiffany Lin, Shruti Kaushal, Megan Sperry, Frederic Vigneault, Erica Gardner, Sahil Loomba, Kostyantyn Shcherbina, Vishal Keshari, Alexandre Dinis, Anish Vasan, Vasanth Chandrasekhar, Takako Takeda, Rahul Nihalani, Sevgi Umur, Jerrold R. Turner, Michael Levin, Donald E. Ingber

“…This is further complicated by the lack of sufficiently broad and biologically relevant drug screens, and the inherent complexity in identifying clinically relevant targets responsible for diverse phenotypes that involve multiple organs. Methods Here, we use computational drug prediction that combines artificial intelligence, human gene regulatory network analysis, and in vivo screening in a CRISPR-edited, Xenopus laevis tadpole model of Rett syndrome to carry out target-agnostic drug discovery. …”
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Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases by William DeGroat, Habiba Abdelhalim, Elizabeth Peker, Neev Sheth, Rishabh Narayanan, Saman Zeeshan, Bruce T. Liang, Zeeshan Ahmed

“…Using SHapley Additive exPlanations, we created risk assessments for patients, offering further contextualization of these predictions in a clinical setting. Across the cohort, RPL36AP37 and HBA1 were scored as the most important biomarkers for predicting CVDs. …”
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Machine learning-based prognostic model of lactylation-related genes for predicting prognosis and immune infiltration in patients with lung adenocarcinoma by Mingjun Gao, Mengmeng Wang, Siding Zhou, Jiaqi Hou, Wenbo He, Yusheng Shu, Xiaolin Wang

“…Unsupervised consistent cluster analysis was performed to identify differentially expressed genes (DEGs) between the two clusters, and risk prediction models were constructed by Cox regression analysis and LASSO analysis. …”
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Prediction of apolipoprotein A-I and high-density lipoprotein cholesterol in the neurological impairment and relapse of neuromyelitis optica spectrum disorder by Yanyan Wang, Feng Wang, Teng Huang, Ziling Zeng, Li Jiao, Hao Sun, Xiaoyu Zhang, Baojie Wang, Rujia Liu, Shougang Guo, Shougang Guo

“…The area under the ROC curve (AUC) for ApoA-I in predicting the severity of neurological impairment was 0.647 (95% CI: 0.542–0.751), with a cutoff value of 1.165, a sensitivity of 59.4%, and a specificity of 67.6%. …”
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Utilizing X-ray radiography for non-destructive assessment of paddy rice grain quality traits by Murugesan Tharanya, Debarati Chakraborty, Anand Pandravada, Raman Babu, Mahantesh Gangashetti, Swapna Paidi, Sunita Choudhary, Kaliamoorthy Sivasakthi, Krithika Anbazhagan, Bhavani Vaditandra, Michael Waininger, Mareike Weule, Eva Hufnagel, Joelle Claußen, Jiří Vaněk, Thomas Wittenberg, Jana Kholova, Stefan Gerth

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Leveraging leaf spectroscopy to identify drought-tolerant soybean cultivars by Ramon Gonçalves de Paula, Martha Freire da Silva, Cibele Amaral, Guilherme de Sousa Paula, Laércio Junio da Silva, Herika Paula Pessoa, Felipe Lopes da Silva

“…Spectroscopy data and statistical approaches such as partial least square regression could be applied to rapidly collect and predict several physiological parameters at leaf-level, allowing phenotyping several genotypes in a high-throughput manner. …”
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Renal Intercalated Cells: Alien Cells Inside Us? by Miguel Luis Graciano

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Missense variants in SLC9A6 cause partial epilepsy without neurodevelopmental delay by Jun-Ping Jiao, Hong-Wei Zhang, Xi-Zhong Zhou, Shu-Juan Tian, Li Gao, Bing-Mei Li, Jun-Xia Luo, Jie Wang, Song Lan, Bin Li, Wei-Ping Liao

“…Conclusion Missense variants in SLC9A6 are associated with mild partial epilepsies. The genotype-phenotype correlation and molecular sub-regional effect of SLC9A6 help in explaining the mechanisms underlying phenotypic variations.…”
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Research on a Panoramic Image Stitching Method for Images of Corn Ears, Based on Video Streaming by Yi Huangfu, Hongming Chen, Zhonghao Huang, Wenfeng Li, Jie Shi, Linlin Yang

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Th1Th17CM Lymphocyte Subpopulation as a Predictive Biomarker of Disease Activity in Multiple Sclerosis Patients under Dimethyl Fumarate or Fingolimod Treatment by Bibiana Quirant-Sánchez, Silvia Presas-Rodriguez, María José Mansilla, Aina Teniente-Serra, José V. Hervás-García, Luis Brieva, Ester Moral-Torres, Antonio Cano, Elvira Munteis, Juan Navarro-Barriuso, Eva M. Martínez-Cáceres, Cristina Ramo-Tello

“…Peripheral blood biomarkers able to predict disease activity in multiple sclerosis (MS) patients have not been identified yet. …”
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Updated overall survival data and predictive biomarkers of autologous NK cells plus Sintilimab as second-line treatment for advanced non-small cell lung cancer by Lin Jia, Naifei Chen, Xiao Chen, Chao Niu, Ziling Liu, Kewei Ma, Lei Yang, Yuguang Zhao, Wei Song, Jin Lu, Chen Chen, Xiaofeng Cong, Xu Wang, Yinghui Xu, Guozhen Cui, Zengguang Liu, Rongrong Chen, Huan Yin, Na Zhang, Jiuwei Cui

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Developing Transferable Fourier Transform Mid-Infrared Spectroscopy Predictive Models for Buffalo Milk: A Spatio-Temporal Application Strategy Analysis Across Dairy Farms by Han Jiang, Peipei Wen, Yikai Fan, Yi Zhang, Chunfang Li, Chu Chu, Haitong Wang, Yue Zheng, Chendong Yang, Guie Jiang, Jianming Li, Junqing Ni, Shujun Zhang

“…Thus, a large number of phenotype data can be provided for buffalo production management and genetic breeding. …”
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Photothermal Integration of Multi-Spectral Imaging Data via UAS Improves Prediction of Target Traits in Oat Breeding Trials by David Evershed, Jason Brook, Sandy Cowan, Irene Griffiths, Sara Tudor, Marc Loosley, John H. Doonan, Catherine J. Howarth

“…The modelling and prediction of important agronomic traits using remotely sensed data is an evolving science and an attractive concept for plant breeders, as manual crop phenotyping is both expensive and time consuming. …”
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Identification of a novel immunogenic cell death-related classifier to predict prognosis and optimize precision treatment in hepatocellular carcinoma by Dongjing Zhang, Bingyun Lu, Qianqian Ma, Wen Xu, Qi Zhang, Zhiqi Xiao, Yuanheng Li, Ren Chen, An-jiang Wang

“…High ICD score indicated an immune-excluded TME phenotype, with lower anticancer immunity and shorter survival time. …”
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Predicting cognitive frailty in community-dwelling older adults: a machine learning approach based on multidomain risk factors by Catherine Park, Namhee Kim, Chang Won Won, Miji Kim

“…Participants exhibiting at least one frailty phenotype and MMSE score ≤ 24 were classified as having CF. …”
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Cross-sectional analysis of eating disorder risk and risk correlates in candidates for bariatric surgery from the BariPredict cohort by Dana AlTarrah, Lulwa Al-Abdullah, Mohammed Alhusayan, Dulce Canha, Sulaiman Almazeedi, Ahmad Al-Serri, Maryam Abulhasan, Talia Alsomly, Naif Almutawa, Mohammed Al-Onaizi, Guy Fagherazzi, Fawaz Alzaid

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Fertility, Hatchability, and Prediction of Egg Weight from Egg Quality Indices of Nigerian Indigenous and Exotic Helmeted Guinea Fowls by Praise Jegede, Abdulmojeed Yakubu, Ibrahim S. Musa, Samuel T. Vincent, Ayoola J. Shoyombo, Olayinka O. Alabi, Mathew Wheto, Ayotunde O. Adebambo, Mustapha A. Popoola

“…The CHAID algorithm was more consistent in egg weight prediction, with egg width as the primary explanatory variable. …”
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Genomic prediction of genotyped and non-genotyped Jeju black cattle using single- and multi-trait methods for carcass traits by Md Azizul Haque, Eun-Bi Jang, Sepill Park, Yun-Mi Lee, Jong-Joo Kim

“…Phenotypic data were collected from 2045 JBC, and 3759 JBC individuals were genotyped using the Affymetrix 160K SNP Axiom array. …”
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Spatial multi-omics analysis of tumor-stroma boundary cell features for predicting breast cancer progression and therapy response by Yuanyuan Wu, Youyang Shi, Zhanyang Luo, Xiqiu Zhou, Yonghao Chen, Xiaoyun Song, Sheng Liu

“…Additionally, Lasso regression analysis was employed to develop a malignant body signature (MBS), which was subsequently validated using the TCGA dataset for prognosis prediction and treatment response assessment.ResultsOur research indicates that the tumor boundary is characterized by a rich reconstruction of the extracellular matrix (ECM), immunomodulatory regulation, and the epithelial-to-mesenchymal transition (EMT), underscoring its significance in tumor progression. …”
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Circulating T cell status and molecular imaging may predict clinical benefit of neoadjuvant PD-1 blockade in oral cancer by Tanja D de Gruijl, Rieneke van de Ven, K Hakki Karagozoglu, Pim de Graaf, Ronald Boellaard, Ruud H Brakenhoff, C René Leemans, Gerben J C Zwezerijnen, C Willemien Menke-van der Houven van Oordt, Iris H C Miedema, Niels E Wondergem, Jan-Jaap Hendrickx, Simone E J Eerenstein, Rolf J Bun, Dorien C Mulder, Jens Voortman, Albert D Windhorst, J Pascal Hagers, Laura A N Peferoen, Elisabeth Bloemena

“…Secondary objectives included clinical and pathological response, and immune profiling of peripheral blood mononuclear cells (PBMCs) for response prediction. Baseline tumor biopsies and postnivolumab resection specimens were evaluated by histopathology.Results Grade III or higher adverse events were not observed and treatment was not delayed in relation to nivolumab administration and other study procedures. …”
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