Michelle Skime

1.6k total citations
32 papers, 689 citations indexed

About

Michelle Skime is a scholar working on Biological Psychiatry, Experimental and Cognitive Psychology and Epidemiology. According to data from OpenAlex, Michelle Skime has authored 32 papers receiving a total of 689 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Biological Psychiatry, 10 papers in Experimental and Cognitive Psychology and 9 papers in Epidemiology. Recurrent topics in Michelle Skime's work include Tryptophan and brain disorders (12 papers), Mental Health Research Topics (9 papers) and Substance Abuse Treatment and Outcomes (7 papers). Michelle Skime is often cited by papers focused on Tryptophan and brain disorders (12 papers), Mental Health Research Topics (9 papers) and Substance Abuse Treatment and Outcomes (7 papers). Michelle Skime collaborates with scholars based in United States, Germany and Singapore. Michelle Skime's co-authors include Richard M. Weinshilboum, Joanna M. Biernacka, Mark A. Frye, Daniel K. Hall‐Flavin, William V. Bobo, Victor M. Karpyak, Drew Neavin, Liewei Wang, Arjun P. Athreya and Krishna C. Vadodaria and has published in prestigious journals such as Biological Psychiatry, Neuropsychopharmacology and Molecular Psychiatry.

In The Last Decade

Michelle Skime

31 papers receiving 684 citations

Peers

Michelle Skime
Yifeng Shen China
Shoko Tsuchimine Japan
Huafang Li China
Lucija Tudor Croatia
Paolo Olgiati Italy
Aram Kim South Korea
Małgorzata Maciukiewicz Canada
Heather Ryan United States
Timothy R. Powell United Kingdom
Inder Kaul United States
Yifeng Shen China
Michelle Skime
Citations per year, relative to Michelle Skime Michelle Skime (= 1×) peers Yifeng Shen

Countries citing papers authored by Michelle Skime

Since Specialization
Citations

This map shows the geographic impact of Michelle Skime's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Michelle Skime with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michelle Skime more than expected).

Fields of papers citing papers by Michelle Skime

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michelle Skime. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Michelle Skime. The network helps show where Michelle Skime may publish in the future.

Co-authorship network of co-authors of Michelle Skime

This figure shows the co-authorship network connecting the top 25 collaborators of Michelle Skime. A scholar is included among the top collaborators of Michelle Skime based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Michelle Skime. Michelle Skime is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
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Ho, Ming‐Fen, Cheng Zhang, Brandon J. Coombes, et al.. (2024). IL17RB genetic variants are associated with acamprosate treatment response in patients with alcohol use disorder: A proteomics-informed genomics study. Brain Behavior and Immunity. 120. 304–314. 1 indexed citations
3.
Ho, Ming‐Fen, Cheng Zhang, Irene Moon, et al.. (2024). Molecular mechanisms involved in alcohol craving, IRF3, and endoplasmic reticulum stress: a multi-omics study. Translational Psychiatry. 14(1). 165–165. 2 indexed citations
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Romanowicz, Magdalena, Michelle Skime, Paul E. Croarkin, et al.. (2023). Machine Learning Identifies Smartwatch-Based Physiological Biomarker for Predicting Disruptive Behavior in Children: A Feasibility Study. Journal of Child and Adolescent Psychopharmacology. 33(9). 387–392. 3 indexed citations
6.
Ho, Ming‐Fen, Cheng Zhang, Irene Moon, et al.. (2023). Epigenetic regulation of GABA catabolism in iPSC-derived neurons: The molecular links between FGF21 and histone methylation. Molecular Metabolism. 77. 101798–101798. 5 indexed citations
7.
Ho, Ming‐Fen, Cheng Zhang, Irene Moon, et al.. (2022). Single cell transcriptomics reveals distinct transcriptional responses to oxycodone and buprenorphine by iPSC-derived brain organoids from patients with opioid use disorder. Molecular Psychiatry. 29(6). 1636–1646. 21 indexed citations
8.
Ho, Ming‐Fen, Cheng Zhang, Irene Moon, et al.. (2022). Plasma TNFSF10 levels associated with acamprosate treatment response in patients with alcohol use disorder. Frontiers in Pharmacology. 13. 986238–986238. 7 indexed citations
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Biernacka, Joanna M., Brandon J. Coombes, Anthony Batzler, et al.. (2021). Genetic contributions to alcohol use disorder treatment outcomes: a genome-wide pharmacogenomics study. Neuropsychopharmacology. 46(12). 2132–2139. 29 indexed citations
11.
Athreya, Arjun P., Tanja Brückl, Elisabeth B. Binder, et al.. (2021). Prediction of short-term antidepressant response using probabilistic graphical models with replication across multiple drugs and treatment settings. Neuropsychopharmacology. 46(7). 1272–1282. 22 indexed citations
12.
Liu, Duan, Siamak MahmoudianDehkordi, Rima Kaddurah‐Daouk, et al.. (2021). Multi-omics driven predictions of response to acute phase combination antidepressant therapy: a machine learning approach with cross-trial replication. Translational Psychiatry. 11(1). 513–513. 33 indexed citations
13.
Vadodaria, Krishna C., Yuan Ji, Michelle Skime, et al.. (2019). Altered serotonergic circuitry in SSRI-resistant major depressive disorder patient-derived neurons. Molecular Psychiatry. 24(6). 808–818. 70 indexed citations
14.
Vadodaria, Krishna C., Yuan Ji, Michelle Skime, et al.. (2019). Serotonin-induced hyperactivity in SSRI-resistant major depressive disorder patient-derived neurons. Molecular Psychiatry. 24(6). 795–807. 57 indexed citations
15.
Karpyak, Victor M., Jennifer R. Geske, Daniel K. Hall‐Flavin, et al.. (2019). Sex-specific association of depressive disorder and transient emotional states with alcohol consumption in male and female alcoholics. Drug and Alcohol Dependence. 196. 31–39. 23 indexed citations
16.
Veldić, Marin, Vincent Millischer, John D. Port, et al.. (2019). Genetic variant in SLC1A2 is associated with elevated anterior cingulate cortex glutamate and lifetime history of rapid cycling. Translational Psychiatry. 9(1). 149–149. 20 indexed citations
17.
Ahmed, Ahmed T., Mark A. Frye, A. John Rush, et al.. (2018). Mapping depression rating scale phenotypes onto research domain criteria (RDoC) to inform biological research in mood disorders. Journal of Affective Disorders. 238. 1–7. 18 indexed citations
18.
Karpyak, Victor M., Joanna M. Biernacka, Jennifer R. Geske, et al.. (2016). Gender‐specific effects of comorbid depression and anxiety on the propensity to drink in negative emotional states. Addiction. 111(8). 1366–1375. 51 indexed citations
19.
Neavin, Drew, Duan Liu, Joanna M. Biernacka, et al.. (2016). TSPAN5, ERICH3 and selective serotonin reuptake inhibitors in major depressive disorder: pharmacometabolomics-informed pharmacogenomics. Molecular Psychiatry. 21(12). 1717–1725. 94 indexed citations
20.
Mrazek, David A., Joanna M. Biernacka, Donald E. McAlpine, et al.. (2014). Treatment Outcomes of Depression. Journal of Clinical Psychopharmacology. 34(3). 313–317. 32 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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