Genotype Prediction from Retinal Fundus Images Using Deep Learning in Eyes with Age-Related Macular Degeneration by Avishai Halev, PhD, Denis Huang, MD, Shahbaz Rezaei, PhD, Sean Banks, BS, John D. McPherson, PhD, Suma P. Shankar, MD, PhD, Xin Liu, PhD, Glenn Yiu, MD, PhD

“…Attribution mapping was performed to identify fundus regions used to predict genotype from phenotype. Main Outcome Measures: Area under the receiver operating characteristic curve (AUROC), balanced accuracy, and average precision for predicting high-risk genotypes. …”
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Blood immunomap for prediction of responses to anti-PD-1 immunotherapy in metastatic non-small cell lung cancer by Maria Semitekolou, Nikolaos Paschalidis, Domenico Lo Tartaro, Aikaterini Tsitsopoulou, Panagiota Stamou, Alexandros Mavroudis, Effrosyni Markaki, Athina Varveri, Ioannis Morianos, Matthieu Lavigne, Charalampos Fotsitzoudis, Sofia Magkouta, Konstantina Dede, Ioannis Kalomenidis, Konstantinos Samitas, Konstantinos Potaris, Andrea Cossarizza, Dimitrios Mavroudis, Sara De Biasi, Panayiotis Verginis

“…Overall, our study pinpoints immune-related events which may benefit the quest for detection of predictive biomarkers of immunotherapy responses.…”
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YTHDC2 manipulates anti-tumoral macrophage polarization and predicts favorable outcomes in triple negative breast cancer by Hao Jin, Dongbo Zhang, Yufan Ma, Lanlan Meng, Songyin Huang, Hongjun Su, Jiannan Xu, Yandan Yao

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Hereditary non-polyposis colorectal cancer is predicted to contribute towards colorectal cancer in young South African blacks by L. Cronjé, P.J. Becker, A.C. Paterson, M. Ramsay

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Characterization, Prediction and Analysis of Cytosine Phospho Guanine Islands Genes Related to African Swine Fever Resistance in Pigs by O. O. Oluwole, A. C. Adeola, M. O. Olalere, O. A. Oladejo, A. K. Tiamiyu, A. A. Bolaji, B. A. Boladuro, T. O. Olorungbounmi

“…Consequently, the objective of this paper was to characterize, predict and analyze Cytosine phospho Guanine islands (CpGIs) genes related to African Swine Fever (ASF) resistance in pigs. …”
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Characterization, Prediction and Analysis of Cytosine Phospho Guanine Islands Genes Related to African Swine Fever Resistance in Pigs by O. O. Oluwole, A. C. Adeola, M. O. Olalere, O. A. Oladejo, A. K. Tiamiyu, A. A. Bolaji, B. A. Boladuro, T. O. Olorungbounmi

“…Consequently, the objective of this paper was to characterize, predict and analyze Cytosine phospho Guanine islands (CpGIs) genes related to African Swine Fever (ASF) resistance in pigs. …”
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mzGroupAnalyzer--predicting pathways and novel chemical structures from untargeted high-throughput metabolomics data. by Hannes Doerfler, Xiaoliang Sun, Lei Wang, Doris Engelmeier, David Lyon, Wolfram Weckwerth

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Radiomic features at contrast-enhanced CT predict proliferative hepatocellular carcinoma and its prognosis after transarterial chemoembolization by Haifeng He, Zhichao Feng, Junhong Duan, Wenzhi Deng, Zuowei Wu, Yizi He, Qi Liang, Yongzhi Xie

“…Abstract Proliferative hepatocellular carcinoma (HCC) is an aggressive phenotype associated with unfavorable clinical outcomes. …”
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Predicting oil accumulation by fruit image processing and linear models in traditional and super high-density olive cultivars by Giuseppe Montanaro, Antonio Carlomagno, Angelo Petrozza, Francesco Cellini, Ioanna Manolikaki, Georgios Koubouris, Vitale Nuzzo

“…The use of a general linear model here presented is a new computational option integrating current methods mostly based on artificial neural networks. RGB-based image phenotyping can effectively predict key quality traits in olive fruit supporting the transition of the olive sector towards a digital agriculture domain.…”
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344 Income predicts low back pain but not lumbar disc height: Data for the UK Biobank Image Dataset by Mary Bucklin, Ashrith Alavilli, Rana Ahmad, Scott Simmons, John Martin

“…Thus, disc degeneration may not be a factor in this low back pain phenotype.…”
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The MAR method versus the visual estimation method in predicting external blood loss: a randomized controlled study by Betul Akbuga Ozel, Gurkan Ozel, Elmas Burcu Mamak Ekinci, Ahmet Eren Demirtola, Gizem Sahin, Gokcen Aksoy, Ilke Yanikyurek, Mehmet Ufuk Karaaslan

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Machine learning for predicting the outcomes and risks of cardiovascular diseases in patients with hypertension: results of ESSE-RF in the Primorsky Krai by V. A. Nevzorova, N. G. Plekhova, L. G. Priseko, I. N. Chernenko, D. Yu. Bogdanov, M. V. Mokshina, N. V. Kulakova

“…To assess the prospects of using artificial intelligence technologies in predicting the outcomes and risks of cardiovascular diseases (CVD) in patients with hypertension (HTN).Material and methods. …”
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Erector Spinae Muscle to Epicardial Visceral Fat Ratio on Chest CT Predicts the Severity of Coronavirus Disease 2019 by Takashi Shimada, Tomoki Maetani, Shotaro Chubachi, Naoya Tanabe, Takanori Asakura, Ho Namkoong, Hiromu Tanaka, Shuhei Azekawa, Shiro Otake, Kensuke Nakagawara, Takahiro Fukushima, Mayuko Watase, Yusuke Shiraishi, Hideki Terai, Mamoru Sasaki, Soichiro Ueda, Yukari Kato, Norihiro Harada, Shoji Suzuki, Shuichi Yoshida, Hiroki Tateno, Kaoruko Shimizu, Susumu Sato, Yoshitake Yamada, Masahiro Jinzaki, Toyohiro Hirai, Yukinori Okada, Ryuji Koike, Makoto Ishii, Akinori Kimura, Seiya Imoto, Satoru Miyano, Seishi Ogawa, Takanori Kanai, Koichi Fukunaga

“…ABSTRACT Background Chest computed tomography (CT) is a valuable tool for diagnosing and predicting the severity of coronavirus disease 2019 (COVID‐19) and assessing extrapulmonary organs. …”
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Hybrid model for predicting microsatellite instability in colorectal cancer using hematoxylin & eosin-stained images and clinical features by Hangping Wei, Xiaowei Zhang, Zhen Zhou, Jianbin Xie, Weidong Han, Xiaofang Dong

“…BackgroundMicrosatellite instability (MSI) is a crucial molecular phenotype in colorectal cancer (CRC), which aids in determining treatment strategies and predicting prognosis. …”
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Constitutively nuclear FOXO3a localization predicts poor survival and promotes Akt phosphorylation in breast cancer. by Jie Chen, Ana R Gomes, Lara J Monteiro, San Yu Wong, Lai Han Wu, Ting-Ting Ng, Christina T Karadedou, Julie Millour, Ying-Chi Ip, Yuen Nei Cheung, Andrew Sunters, Kelvin Y K Chan, Eric W-F Lam, Ui-Soon Khoo

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Protocol to analyze deep-learning-predicted functional scores for noncoding de novo variants and their correlation with complex brain traits by Enrique Mondragon-Estrada, Sarah U. Morton

“…We describe steps for score prediction, statistical comparison, phenotype correlation, and functional enrichment analysis. …”
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Efficacy and safety of antipsychotics in adolescents with an acute psychotic episode in relation to the activity of CYP3A and CYP2D6 isoenzymes by D. V. Ivashchenko, A. S. Osipov, E. V. Nazarova, M. A. Ovchinnikova, N. I. Buromskaya, V. V. Smirnov, P. V. Shimanov, R. V. Deitch, T. A. Fainshtein, E. N. Shagovenko, K. A. Akmalova, A. A. Kachanova, E. A. Grishina, L. M. Savchenko, Yu. S. Shevchenko, D. A. Sychev

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Retinal Vascular Morphology Reflects and Predicts Cerebral Small Vessel Disease: Evidences from Eye–Brain Imaging Analysis by Ning Wu, Mingze Xu, Shuohua Chen, Shouling Wu, Jing Li, Ying Hui, Xiaoshuai Li, Zhenchang Wang, Han Lv

“…Support vector machine models were developed to predict SVD severity using retinal and demographic data. …”
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Ge-SAND: an explainable deep learning-driven framework for disease risk prediction by uncovering complex genetic interactions in parallel by Lihang Ye, Liubin Zhang, Bin Tang, Junhao Liang, Ruijie Tan, Hui Jiang, Wenjie Peng, Nan Lin, Kun Li, Chao Xue, Miaoxin Li

“…Results We developed Ge-SAND (Genomic Embedding Self-Attention Neurodynamic Decoder), an explainable deep learning-driven framework designed to uncover complex genetic interactions at scales exceeding 106 in parallel for accurate disease risk prediction. Ge-SAND leverages genotype and genomic positional information to identify both intra- and interchromosomal interactions associated with disease phenotypes, providing comprehensive insights into pathogenic mechanisms crucial for disease risk prediction. …”
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Mutational status may supersede tumor size in predicting the presence of aggressive pathologic features in well differentiated thyroid cancer by Koorosh Semsar-Kazerooni, Grégoire B. Morand, Alexandra E. Payne, Sabrina D. da Silva, Véronique-Isabelle Forest, Michael P. Hier, Marc P. Pusztaszeri, Michael Tamilia, Richard J. Payne

“…As a result of this finding, this study suggests that molecular alterations may better predict aggressive pathologic features than the size of the tumor. …”
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