Risk Prediction of Liver Injury in Pediatric Tuberculosis Treatment: Development of an Automated Machine Learning Model by Zeng Y, Lu H, Li S, Shi QZ, Liu L, Gong YQ, Yan P

“…Ying Zeng,1,* Hong Lu,1,* Sen Li,2,* Qun-Zhi Shi,1 Lin Liu,1 Yong-Qing Gong,1 Pan Yan1 1Department of Pharmacy, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, 410004, People’s Republic of China; 2Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, People’s Republic of China*These authors contributed equally to this workCorrespondence: Pan Yan, Department of Pharmacy, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, People’s Republic of China, Email 2022050025@usc.edu.cnPurpose: Drug-induced liver injury (DILI) is one of the most common and serious adverse drug reactions related to first-line anti-tuberculosis drugs in pediatric tuberculosis patients. …”
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Gene signatures derived from transcriptomic-causal networks stratify colorectal cancer patients for effective targeted therapy by Akram Yazdani, Heinz-Josef Lenz, Gianluigi Pillonetto, Raul Mendez-Giraldez, Azam Yazdani, Hanna Sanoff, Reza Hadi, Esmat Samiei, Alan P. Venook, Mark J. Ratain, Naim Rashid, Benjamin G. Vincent, Xueping Qu, Yujia Wen, Michael Kosorok, William F. Symmans, John Paul Y. C. Shen, Michael S. Lee, Scott Kopetz, Andrew B. Nixon, Monica M. Bertagnolli, Charles M. Perou, Federico Innocenti

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