Definition and research guide
This page combines a MeSH definition, publication statistics and selected research abstracts. It is a starting point for reading the literature, not a medical textbook or a systematic evidence review.
Content available: 4 complete abstracts (788 words), 10 references. Full article text is not stored or reproduced here; follow the PMID, DOI or PMC links to read the original sources and their access terms.
Definition
In statistics, a technique for numerically approximating the solution of a mathematical problem by studying the distribution of some random variable, often generated by a computer. The name alludes to the randomness characteristic of the games of chance played at the gambling casinos in Monte Carlo. (From Random House Unabridged Dictionary, 2d ed, 1993)
Definition: NLM Medical Subject Headings (MeSH) scope note (Public domain (U.S. National Library of Medicine); attribution requested). View the MeSH record.
Also known as: Method, Monte Carlo.
Where it fits in the MeSH classification
- Broader topics
- Statistics as Topic; Operations Research
Research landscape
PubMed indexes 36,440 records tagged with the MeSH heading “Monte Carlo Method” (counts retrieved 2026-10-29). 8,000 of them (22.0%) were published from 2020 onward, and the busiest period so far is 2010-2019 with 15,153 records.
Reviews account for 919 records (2.5%) and meta-analyses for 148. Randomized controlled trials number 195 (0.5%) as a share of all records on the topic. Randomized trials make up only a small share of this literature, so much of what is known comes from laboratory, observational or descriptive work.
Show the data
| Period | Records |
|---|---|
| through 1999 | 3,318 |
| 2000-2009 | 9,969 |
| 2010-2019 | 15,153 |
| 2020 onward | 8,000 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 919 | 2.5% |
| Meta-analyses | 148 | 0.4% |
| Randomized controlled trials | 195 | 0.5% |
Method: PubMed query "Monte Carlo Method"[MeSH Terms] with publication-type and date filters, retrieved 2026-10-29.
Reading the evidence in clinical research
Research methods determine how much a result can be trusted. Randomized trials guard against confounding, systematic reviews and meta-analyses combine many studies, and observational designs are valuable but can show only association. Reporting standards, trial registration and pre-specified outcomes reduce bias. When reading about a method, ask what problem it is designed to solve and which problems it does not solve.
Evidence from open-access studies
These open-access studies come from PubMed’s best-match results for this heading (papers whose main topic it is), ranked here by citation count, with one recent addition. Each abstract is reproduced in full under its recorded open licence so that you can read the authors’ own words. They illustrate the literature; they are not a systematic summary of it. Citation counts measure attention, not reliability or clinical benefit.
| Study / authors | Year | Source label / design | Europe PMC citations | Licence / source |
|---|---|---|---|---|
| Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search | 2022 | Research article Design not recorded in structured metadata | 193 | cc by PMID 36224222 · DOI · PMC9556563 |
| Evaluation of a treatment planning system developed for clinical boron neutron capture therapy and validation against an independent Monte Carlo dose calculation system | 2021 | Research article Design not recorded in structured metadata | 38 | cc by PMID 34952608 · DOI · PMC8709965 |
| Toward a Monte Carlo approach to selecting climate variables in MaxEnt | 2021 | Research article Design not recorded in structured metadata | 21 | cc0 PMID 33657125 · DOI · PMC7928495 |
| Monte Carlo modeling of the formation and organization of ion channel clustering | 2026 | Research article Design not recorded in structured metadata | 0 | cc by PMID 42752396 · DOI · PMC13596815 |
Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search [1]
Abstract reading summary — source excerpts
- Opening excerpt
- AlphaFold can predict the structure of single- and multiple-chain proteins with very high accuracy.
- Closing excerpt
- ipynb .
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (135 words)
AlphaFold can predict the structure of single- and multiple-chain proteins with very high accuracy. However, the accuracy decreases with the number of chains, and the available GPU memory limits the size of protein complexes which can be predicted. Here we show that one can predict the structure of large complexes starting from predictions of subcomponents. We assemble 91 out of 175 complexes with 10-30 chains from predicted subcomponents using Monte Carlo tree search, with a median TM-score of 0.51. There are 30 highly accurate complexes (TM-score ≥0.8, 33% of complete assemblies). We create a scoring function, mpDockQ, that can distinguish if assemblies are complete and predict their accuracy. We find that complexes containing symmetry are accurately assembled, while asymmetrical complexes remain challenging. The method is freely available and accesible as a Colab notebook https://colab.research.google.com/github/patrickbryant1/MoLPC/blob/master/MoLPC.ipynb .
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 36224222 · DOI · PMC9556563
Evaluation of a treatment planning system developed for clinical boron neutron capture therapy and validation against an independent Monte Carlo dose calculation system [2]
Abstract reading summary — source excerpts
- Opening excerpt
- Boron neutron capture therapy (BNCT) for the treatment of unresectable, locally advanced, and recurrent carcinoma of the head and neck cancer has been approved by the Japanese government for reimbursement under the national health insurance as of June 2020.
- Closing excerpt
- 3 Gyw for the brain.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (175 words)
Boron neutron capture therapy (BNCT) for the treatment of unresectable, locally advanced, and recurrent carcinoma of the head and neck cancer has been approved by the Japanese government for reimbursement under the national health insurance as of June 2020. A new treatment planning system for clinical BNCT has been developed by Sumitomo Heavy Industries, Ltd. (Sumitomo), NeuCure® Dose Engine. To safely implement this system for clinical use, the simulated neutron flux and gamma ray dose rate inside a water phantom was compared against experimental measurements. Furthermore, to validate and verify the new planning system, the dose distribution inside an anthropomorphic head phantom was compared against a BNCT treatment planning system SERA and an in-house developed Monte Carlo dose calculation program. The simulated results closely matched the experimental results, within 5% for the thermal neutron flux and 10% for the gamma ray dose rate. The dose distribution inside the head phantom closely matched with SERA and the in-house developed dose calculation program, within 3% for the tumour and a difference of 0.3 Gyw for the brain.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 34952608 · DOI · PMC8709965
Toward a Monte Carlo approach to selecting climate variables in MaxEnt [3]
Abstract reading summary — source excerpts
- Opening excerpt
- MaxEnt is an important aid in understanding the influence of climate change on species distributions.
- Closing excerpt
- This points to the possibility of near-real-time multiprocessor implementations that could enable broader and more exploratory use of global climate model outputs in environmental niche modeling and aid in the discovery of viable predictors.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (289 words)
MaxEnt is an important aid in understanding the influence of climate change on species distributions. There is growing interest in using IPCC-class global climate model outputs as environmental predictors in this work. These models provide realistic, global representations of the climate system, projections for hundreds of variables (including Essential Climate Variables), and combine observations from an array of satellite, airborne, and in-situ sensors. Unfortunately, direct use of this important class of data in MaxEnt modeling has been limited by the large size of climate model output collections and the fact that MaxEnt can only operate on a relatively small set of predictors stored in a computer's main memory. In this study, we demonstrate the feasibility of a Monte Carlo method that overcomes this limitation by finding a useful subset of predictors in a larger, externally-stored collection of environmental variables in a reasonable amount of time. Our proposed solution takes an ensemble approach wherein many MaxEnt runs, each drawing on a small random subset of variables, converges on a global estimate of the top contributing subset of variables in the larger collection. In preliminary tests, the Monte Carlo approach selected a consistent set of top six variables within 540 runs, with the four most contributory variables of the top six accounting for approximately 93% of overall permutation importance in a final model. These results suggest that a Monte Carlo approach could offer a viable means of screening environmental predictors prior to final model construction that is amenable to parallelization and scalable to very large data sets. This points to the possibility of near-real-time multiprocessor implementations that could enable broader and more exploratory use of global climate model outputs in environmental niche modeling and aid in the discovery of viable predictors.
Abstract reproduced from the open-access article under the recorded cc0 licence; © the authors. PMID 33657125 · DOI · PMC7928495
Monte Carlo modeling of the formation and organization of ion channel clustering [4]
Abstract reading summary — source excerpts
- Opening excerpt
- The spatial organization of ion channels on cell membranes critically influences many key physiological processes, such as cardiac and neuronal excitability and cellular signaling, yet the mechanisms governing channel clustering remain poorly understood.
- Closing excerpt
- These findings generate testable predictions about how channel density, trafficking dynamics, and interaction energies collectively alter ion channel spatial organization, in the setting of both physiological function and pathophysiological conditions.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (189 words)
The spatial organization of ion channels on cell membranes critically influences many key physiological processes, such as cardiac and neuronal excitability and cellular signaling, yet the mechanisms governing channel clustering remain poorly understood. In this study, we present a stochastic computational framework that models the dynamic organization of ion channels through Monte Carlo simulations incorporating membrane insertion, removal, channel-channel interactions, and diffusion processes. Our model reveals several fundamental principles of membrane domain formation. In single-channel systems, we demonstrate a biphasic relationship between interaction energy and cluster size, with optimal clustering occurring at intermediate interaction strengths, suggesting that excessively strong interactions can impede cluster growth by restricting channel mobility. In two-channel systems, we find that the interplay between homotypic and heterotypic interactions determines whether channels form mixed or segregated clusters, with asymmetric clustering behaviors emerging when homotypic interaction strengths differ between channel types. Simulations of three-channel systems demonstrate emergent organizational principles leading to hierarchical clustering patterns and specialized domain formation. These findings generate testable predictions about how channel density, trafficking dynamics, and interaction energies collectively alter ion channel spatial organization, in the setting of both physiological function and pathophysiological conditions.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42752396 · DOI · PMC13596815
Key reviews and meta-analyses
The most cited review-type papers among PubMed’s best-match results for this heading, listed with bibliographic details only. Follow the links for the full text and access terms.
- Research article Multiple imputation: a primer [5]
- Research article Range uncertainties in proton therapy and the role of Monte Carlo simulations [6]
- Research article Report of the AAPM Task Group No. 105: Issues associated with clinical implementation of Monte Carlo-based photon and electron external beam treatment planning [7]
- Research article Track structure modeling in liquid water: A review of the Geant4-DNA very low energy extension of the Geant4 Monte Carlo simulation toolkit [8]
- Research article A review of the use and potential of the GATE Monte Carlo simulation code for radiation therapy and dosimetry applications [9]
- Research article Track structures, DNA targets and radiation effects in the biophysical Monte Carlo simulation code PARTRAC [10]
References
- Bryant P, Pozzati G, Zhu W, Shenoy A, Kundrotas P, Elofsson A. Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search. . 2022. PMID 36224222 · DOI · PMC9556563
- Hu N, Tanaka H, Kakino R, Yoshikawa S, Miyao M, Akita K, et al.. Evaluation of a treatment planning system developed for clinical boron neutron capture therapy and validation against an independent Monte Carlo dose calculation system. . 2021. PMID 34952608 · DOI · PMC8709965
- Schnase JL, Carroll ML, Gill RL, Tamkin GS, Li J, Strong SL, et al.. Toward a Monte Carlo approach to selecting climate variables in MaxEnt. . 2021. PMID 33657125 · DOI · PMC7928495
- Moise N, Weinberg SH. Monte Carlo modeling of the formation and organization of ion channel clustering. . 2026. PMID 42752396 · DOI · PMC13596815
- Schafer JL. Multiple imputation: a primer. Stat Methods Med Res. 1999. PMID 10347857 · DOI
- Paganetti H. Range uncertainties in proton therapy and the role of Monte Carlo simulations. Phys Med Biol. 2012. PMID 22571913 · DOI · PMC3374500
- Chetty IJ, Curran B, Cygler JE, DeMarco JJ, Ezzell G, Faddegon BA, et al.. Report of the AAPM Task Group No. 105: Issues associated with clinical implementation of Monte Carlo-based photon and electron external beam treatment planning. Med Phys. 2007. PMID 18196810 · DOI
- Bernal MA, Bordage MC, Brown JMC, Davídková M, Delage E, El Bitar Z, et al.. Track structure modeling in liquid water: A review of the Geant4-DNA very low energy extension of the Geant4 Monte Carlo simulation toolkit. Phys Med. 2015. PMID 26653251 · DOI
- Sarrut D, Bardiès M, Boussion N, Freud N, Jan S, Létang JM, et al.. A review of the use and potential of the GATE Monte Carlo simulation code for radiation therapy and dosimetry applications. Med Phys. 2014. PMID 24877844 · DOI
- Friedland W, Dingfelder M, Kundrát P, Jacob P. Track structures, DNA targets and radiation effects in the biophysical Monte Carlo simulation code PARTRAC. Mutat Res. 2011. PMID 21281649 · DOI
About this article
- Sources
- NLM Medical Subject Headings (definition, synonyms); NCBI PubMed (counts, identifiers); Europe PMC (open-access abstracts and citation counts).
- Verification
- Every reference was resolved against PubMed on 2026-10-31; title, year and DOI matched.
- Review state
- automated source verified; no human expert review has been recorded yet.
- Version
- 1, last updated 2026-10-29.
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Evidence strength
EvidenceHelix's computed view of the study designs behind this topic: how common meta-analyses, randomized trials and reviews are, how recent the literature is, and a plain-language reading. It describes the design mix of the literature. It is not a quality appraisal of any study, and it is not medical advice.