日期:
2020 年 — 2026 年
2020
2021
2022
2023
2024
2025
2026
影响因子:

Heart-Specific and Conditional Deletion of the Immt Gene Reveals Its Role in Regulating Mitochondrial Structure and Total Heart Metabolism.

心脏特异性和条件性 Immt 基因缺失揭示了其在调节线粒体结构和心脏整体代谢中的作用。

Kuwabara Yasuhide, Keezer Caitlin, Lin Suh-Chin J, Rajput Akanksha, Molkentin Jeffery D

The human glucocorticoid receptor variant rs6190 increases blood cholesterol and promotes atherosclerosis.

人类糖皮质激素受体变异体rs6190会增加血液胆固醇并促进动脉粥样硬化

Durumutla Hima Bindu, Haller April, Noble Greta, Prabakaran Ashok Daniel, McFarland Kevin, Latimer Hannah, Rajput Akanksha, Akinborewa Olukunle, Namjou-Khales Bahram, Hui David Y, Quattrocelli Mattia

Reconstructing the transcriptional regulatory network of probiotic L. reuteri is enabled by transcriptomics and machine learning

转录组学和机器学习能够重建益生菌罗伊氏乳杆菌的转录调控网络。

Josephs-Spaulding, Jonathan; Rajput, Akanksha; Hefner, Ying; Szubin, Richard; Balasubramanian, Archana; Li, Gaoyuan; Zielinski, Daniel C; Jahn, Leonie; Sommer, Morten; Phaneuf, Patrick; Palsson, Bernhard O

ppHiC: Interactive exploration of Hi-C results on the ProteinPaint web portal

ppHiC:在 ProteinPaint 网络门户上交互式探索 Hi-C 结果

Rajput, Akanksha; Reilly, Colleen; Peraza, Airen Zaldivar; Wang, Jian; Sioson, Edgar; Matt, Gavriel; Paul, Robin; Lu, Congyu; Acic, Aleksandar; Gangwani, Karishma; Zhou, Xin

Advanced transcriptomic analysis reveals the role of efflux pumps and media composition in antibiotic responses of Pseudomonas aeruginosa.

高级转录组分析揭示了外排泵和培养基成分在铜绿假单胞菌抗生素反应中的作用

Rajput Akanksha, Tsunemoto Hannah, Sastry Anand V, Szubin Richard, Rychel Kevin, Chauhan Siddharth M, Pogliano Joe, Palsson Bernhard O

Machine learning from Pseudomonas aeruginosa transcriptomes identifies independently modulated sets of genes associated with known transcriptional regulators.

利用铜绿假单胞菌转录组的机器学习方法,可以识别与已知转录调控因子相关的独立调控基因集

Rajput Akanksha, Tsunemoto Hannah, Sastry Anand V, Szubin Richard, Rychel Kevin, Sugie Joseph, Pogliano Joe, Palsson Bernhard O

Biofilm-i: A Platform for Predicting Biofilm Inhibitors Using Quantitative Structure-Relationship (QSAR) Based Regression Models to Curb Antibiotic Resistance

Biofilm-i:一个利用基于定量构效关系 (QSAR) 回归模型预测生物膜抑制剂以遏制抗生素耐药性的平台

Rajput, Akanksha; Bhamare, Kailash T; Thakur, Anamika; Kumar, Manoj

Targeting non-structural proteins of Hepatitis C virus for predicting repurposed drugs using QSAR and machine learning approaches.

利用 QSAR 和机器学习方法,以丙型肝炎病毒的非结构蛋白为靶点,预测药物再利用潜力

Kamboj Sakshi, Rajput Akanksha, Rastogi Amber, Thakur Anamika, Kumar Manoj

Computational identification of repurposed drugs against viruses causing epidemics and pandemics via drug-target network analysis

利用药物靶点网络分析进行针对引起流行病和大流行病的病毒的再利用药物的计算识别

Rajput, Akanksha; Thakur, Anamika; Rastogi, Amber; Choudhury, Shubham; Kumar, Manoj

Pangenome Analytics Reveal Two-Component Systems as Conserved Targets in ESKAPEE Pathogens

泛基因组分析揭示双组分系统是ESKAPEE病原体的保守靶点

Rajput, Akanksha; Seif, Yara; Choudhary, Kumari Sonal; Dalldorf, Christopher; Poudel, Saugat; Monk, Jonathan M; Palsson, Bernhard O