1748-5673
Published by: Inderscience Publishers
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 852 | 9 | 18 | 5 |
The journal was organized to reinforce research efforts on Artificial intelligence, Computational biology, Pattern recognition, Data mining and Machine learning. The journal concentrates on Artificial intelligence topics that focus on Deep learning, Artificial neural network, Feature selection, Cluster analysis and Convolutional neural network. International Journal of Data Mining and Bioinformatics dives deep in exploring the relationship between the study of Artificial neural network and Classifier (UML).
The research on Feature selection tackled can also make contributions to studies in the areas of Microarray analysis techniques and Support vector machine. The work on Cluster analysis addressed in the journal expands to the thematically related Identification (information). The studies on Computational biology discussed can also contribute to research in the domains of Cancer, Single-nucleotide polymorphism and Gene, DNA sequencing.
International Journal of Data Mining and Bioinformatics features studies on Gene, including topics such as Gene expression. The Pattern recognition works featured in International Journal of Data Mining and Bioinformatics incorporate elements from Feature (machine learning) and Feature (computer vision). Most of the Data mining studies addressed also intersect with Biological network.
The most cited papers facilitate discussions on Artificial intelligence, Artificial neural network, Support vector machine, Data mining and Pattern recognition. The studies tackled in the published articles, which mainly focus on Artificial intelligence, apply to Machine learning as well. The Naive Bayes classifier studies presented in the journal papers fall under the field of Pattern recognition, but they also have connections to other fields such as Pupylation.
The journal mainly tackles studies in Genome-wide association study, Artificial intelligence, Genetics, Single-nucleotide polymorphism and Feature selection. Genome-wide association study research featured in the journal incorporates concerns from various other topics such as Genome, Genetic risk, DNA sequencing and Coronavirus disease 2019 (COVID-19). Some problems in Artificial intelligence that were presented in the journal overlapped with concepts under Ranking, Machine learning, Genetic association and Pattern recognition.
The Gene duplication, genomic DNA, SNP and Biobank studies presented in International Journal of Data Mining and Bioinformatics fall under the field of Genetics, but it also has connections to other fields such as Aneuploidy. Computational biology, Overlapping gene, Haplotype and Candidate gene are some topics wherein Single-nucleotide polymorphism research discussed in it have an impact. International Journal of Data Mining and Bioinformatics holds forums on Feature selection that merges themes from other disciplines such as Artificial neural network, Classifier (linguistics) and Naive Bayes classifier, Support vector machine.
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 International Journal of Data Mining and Bioinformatics (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 International Journal of Data Mining and Bioinformatics (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, 14.29% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 66.67% were posted by at least one author from the top 10 institutions publishing in the journal. Another 16.67% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 0.00% of all publications and 16.67% 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.
The International Journal of Data Mining and Bioinformatics is continuously seeking contribution from researchers and scholars around the world. Various professionals, from computational biologists to artificial intelligence experts, have added value to our academic community through their valuable research. Our most effective contributors are those who have a sound understanding of the subjects covered in our journal and are willing to share their findings to propel human knowledge further. Many of our contributors are university lecturers and professors. They apply to contribute to our journal to share the results of their research, potentially earning academic citations and contributing to their professional reputations. For individuals contemplating a career in academia or research, including those desiring to become high school teachers, contributing to our journal could be an excellent opportunity to gain valuable experience and exposure. More information on how to build a career as a history teacher can be found here. Finally, the International Journal of Data Mining and Bioinformatics welcomes contrasting perspectives or arguments to published articles, analyses, and other contents of the journal. This means that even if you are not an active researcher, but an avid reader and have insights to share, you have the opportunity to contribute to our journal. Start your journey as a contributor to the International Journal of Data Mining and Bioinformatics today.
Leila Baradaran Sorkhabi;Farhad Soleimanian Gharehchopogh;Jafar Shahamfar
(2020)Lorenzo Madeddu;Giovanni Stilo;Paola Velardi
(2020)Xin Yan;Lei Wang;Zhu-Hong You;Li-Ping Li
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