| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Chemistry | 718 | 10 | 24 | 10 |
Computational Toxicology primarily focuses on research topics in In silico, Computational biology, Toxicity, Quantitative structure–activity relationship and Artificial intelligence. In silico research presented in the journal encompasses a variety of subjects, including Cosmetics, Risk analysis (engineering), ADME and Biochemical engineering. Computational Toxicology tackles research in Computational biology and various other disciplines, including Adverse Outcome Pathway and Context (language use).
The studies on Toxicity discussed can also contribute to research in the domains of Pharmacology and In vivo. Absorption (skin) and Pharmacokinetics are among the concentrations of Pharmacology that garnered much attention in it. Most of the works presented in Computational Toxicology deals with Quantitative structure–activity relationship but it intersects with the subject of Workflow.
The Artificial intelligence study featured in the journal draws connections with the study of Machine learning. While Machine learning is the focus of Computational Toxicology, it also provided insights into the studies of Risk assessment and Drug discovery. The journal tackles topics on In vitro toxicology, which can potentially contribute to the wider field of In vitro.
The journal articles mostly deal with topics like Risk analysis (engineering), Risk assessment, Quantitative structure–activity relationship, Workflow and Gap filling. The studies on Risk analysis (engineering) discussed at the most cited publications can also contribute to research in the domains of Manufactured nanomaterials, Liver toxicity, Cosmetics and Adaptation (computer science). The journal articles with studies in Quantitative structure–activity relationship featured incorporate elements of Hazard analysis and Computational model, Artificial intelligence.
The main points discussed in Computational Toxicology deals with Computational biology, In silico, Toxicity, Machine learning and Artificial intelligence. It holds forums on In silico that merges themes from other disciplines such as Hazard analysis, ADME and Metabolic pathway. It addresses concerns in the field of Hazard analysis by exploring it in line with topics in Pharmaceutical sciences which intersect with Risk analysis (engineering) subjects.
The journal focused on Machine learning research but expanded to cover Workflow. The study of Workflow encompasses disciplines such as Verification and validation, as well as fields such as Quantitative structure–activity relationship, all of which overlap with one another. The journal addresses concerns in Artificial intelligence which are intertwined with other disciplines, such as Algorithm, Categorical variable and Respiratory system.
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in Computational Toxicology (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in Computational Toxicology (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 13.46% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 44.44% were posted by at least one author from the top 10 institutions publishing in the journal. Another 2.22% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 8.89% of all publications and 44.44% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
R. Raveesha;A.M. Anusuya;A.V. Raghu;K. Yogesh Kumar
(2022)Arianna Bassan;Vinicius M. Alves;Alexander Amberg;Lennart T. Anger
(2021)Alicia Paini;Ivana Campia;Mark T.D. Cronin;David Asturiol
(2021)Arianna Bassan;Vinicius M. Alves;Alexander Amberg;Lennart T. Anger
(2021)Candice Johnson;Lennart T. Anger;Romualdo Benigni;David Bower
(2022)C. Yang;M.T.D. Cronin;K.B. Arvidson;B. Bienfait
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