Harnessing photosynthetic and morpho-physiological traits for drought-resilient soybean: integrating field phenotyping and predictive approaches by Harmeet Singh-Bakala, Harmeet Singh-Bakala, Francia Ravelombola, Cheryl Adeva, Maiara Oliveira, Ru Zhang, Jessica Argenta, Grover Shannon, Feng Lin

“…By coupling with soil parameters, these traits were able to explain 74-79% of yield variance in predictive models.DiscussionThese findings suggest that SPAD, NPQt, FvP/FmP, and leaf thickness are valuable markers for identifying drought-tolerant genotypes. …”
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The benefits of ASCOD phenotyping strategy in patients with Ischemic stroke with extracranial arteries atherosclerosis by E. K. Sidorovich, S. A. Likhachev, Yu. S. Shabalina

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Deciphering the impact of sepsis phenotypes on improving clinical outcome predictions: a multicenter retrospective analysis based on critical care in China by Luyao Zhou, Weimin Zhang, Min Shao, Cui Wang, Yu Wang

“…K-Means clustering was utilized to identify and refine sepsis phenotypes, and their predictive performance was subsequently evaluated. …”
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Associations between milk infrared-predicted plasma biomarkers of stress resilience and fertility in dairy cattle: Insights for enhancing breeding programs and herd management by Alessio Cecchinato, Hugo Toledo-Alvarado, Lucio Flavio Macedo Mota, Vittoria Bisutti, Erminio Trevisi, Riccardo Negrini, Sara Pegolo, Stefano Schiavon, Luigi Gallo, Giovanni Bittante, Diana Giannuzzi

“…In conclusion, the predicted biomarkers investigated revealed to be promising novel phenotypes for assessing animal health and welfare, in the view of enhancing fertility in dairy cattle also through selective breeding, thus improving the overall efficiency of dairy herds.…”
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Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints by Benjamín J Sánchez, Cheng Zhang, Avlant Nilsson, Petri‐Jaan Lahtvee, Eduard J Kerkhoven, Jens Nielsen

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FCA-STNet: Spatiotemporal Growth Prediction and Phenotype Extraction from Image Sequences for Cotton Seedlings by Yiping Wan, Bo Han, Pengyu Chu, Qiang Guo, Jingjing Zhang

Subjects: “…growth prediction…”
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Genetic analysis of predicted vegetative biomass and biomass‐related traits from digital phenotyping of strawberry by Cheryl Dalid, Caiwang Zheng, Luis Osorio, Sujeet Verma, Amr Abd‐Elrahman, Xu Wang, Vance M. Whitaker

“…Abstract High‐throughput digital phenotyping (DP) has been widely explored in plant breeding to assess large numbers of genotypes with minimal manual labor and reduced cost and time. …”
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Leveraging genomics and temporal high‐throughput phenotyping to enhance association mapping and yield prediction in sesame by Idan Sabag, Ye Bi, Maitreya Mohan Sahoo, Ittai Herrmann, Gota Morota, Zvi Peleg

“…Moderate prediction accuracy was obtained when predicting new genotypes at each time point, and moderate to high values were obtained when forecasting future phenotypes. …”
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Chromatin Phenotype Karyometry Can Predict Recurrence in Papillary Urothelial Neoplasms of Low Malignant Potential by Rodolfo Montironi, Marina Scarpelli, Antonio Lopez-Beltran, Roberta Mazzucchelli, David Alberts, James Ranger-Moore, Hubert G. Bartels, Peter W. Hamilton, Janine Einspahr, Peter H. Bartels

“…Pathol. 57(2004), 1201–1207) had shown that a karyometric assessment of nuclei from papillary urothelial neoplasms of low malignant potential (PUNLMP) revealed subtle differences in phenotype which correlated with recurrence of disease. …”
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Enhancing drought prediction through machine learning: advanced techniques combining phenotypic and agrometeorological data by Efrem Yohannes Obsie, Yongguo Liu

“…Two machine learning algorithms, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), were evaluated as predictive models. Image-based phenotypic features were extracted using a CNN-based network, resulting in 512 features, while handcrafted methods generated 48 features. …”
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ThermOptCobra: Thermodynamically optimal construction and analysis of metabolic networks for reliable phenotype predictions by Pavan Kumar S, Nirav Pravinbhai Bhatt

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Phenotypic screening and genetic insights for predicting major vascular-related diseases using retinal imaging by Menglin Lu, Yiheng Mao, Hui Zhu, Yesheng Xu, Yu-Feng Yao, Fei Wu, Zhengxing Huang

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eVIP2: Expression-based variant impact phenotyping to predict the function of gene variants. by Alexis M Thornton, Lishan Fang, April Lo, Maria McSharry, David Haan, Casey O'Brien, Alice H Berger, Marios Giannakis, Angela N Brooks

“…Surprisingly, eVIP2 predicted G659fs to be a change-of-function mutation. …”
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Prediction of Skin Color Using Forensic DNA Phenotyping in Asian Populations: A Focus on Thailand by Gabriel Perez Palomeque, Supakit Khacha-ananda, Tawachai Monum, Klintean Wunnapuk

Subjects: “…forensic DNA phenotyping…”
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Predictive Value of the Triglyceride-Glucose Index for Metabolic-Associated Fatty Liver Disease in Individuals with Different Metabolic Obese Phenotypes by Lv D, Wang Z, Liu H, Meng C

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Integration of single-nuclei and spatial transcriptomics to decipher tumor phenotype predictive of relapse-free survival in Wilms tumor by Ran Yang, Ran Yang, Lulu Xie, Rui Wang, Yi Li, Yifei Lu, Baihui Liu, Shuyang Dai, Shan Zheng, Shan Zheng, Kuiran Dong, Kuiran Dong, Rui Dong, Rui Dong

“…A prognostic ensemble machine learning model was constructed based on the Scissor+ tumor signature to accurately predict patient RFS. TGFA was identified as the most significant feature in this model and validated by immunohistochemistry. …”
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Predictive Biomarkers and Novel Treatments for the Progressive Fibrosing Phenotype in Interstitial Lung Disease Associated with Connective Tissue Disease by Sang Wan Chung

“…<b>Objective:</b> This review aims to summarize key predictive biomarkers and current treatment strategies associated with the progressive fibrosing phenotype in SSc-ILD, RA-ILD, and IIM-ILD. …”
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SNPs and other features as they predispose to complex disease: genome-wide predictive analysis of a quantitative phenotype for hypertension. by Joong-Ho Won, Georg Ehret, Aravinda Chakravarti, Richard A Olshen

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An integrative, multi‐scale, genome‐wide model reveals the phenotypic landscape of Escherichia coli by Javier Carrera, Raissa Estrela, Jing Luo, Navneet Rai, Athanasios Tsoukalas, Ilias Tagkopoulos

“…Abstract Given the vast behavioral repertoire and biological complexity of even the simplest organisms, accurately predicting phenotypes in novel environments and unveiling their biological organization is a challenging endeavor. …”
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Machine learning-based prediction of restless legs syndrome using digital phenotypes from wearables and smartphone data by Jingyeong Jeong, Yoonseo Jeon, Hyungju Kim, Ji Won Yeom, Yu-Bin Shin, Sujin Kim, Seung Pil Pack, Heon-Jeong Lee, Taesu Cheong, Chul-Hyun Cho

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