| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Microbiology | 162 | 39 | 36 | 13 |
| Computer Science | 179 | 241 | 325 | 28 |
| Biology and Biochemistry | 208 | 242 | 248 | 27 |
The journal investigates areas of study like DNA microarray, Computational biology, Genetics, Data mining and Artificial intelligence. While work presented in the journal provided substantial information on DNA microarray, it also covered topics in Genome, DNA sequencing and Gene expression profiling. Some problems in Gene expression profiling that were presented in the journal overlapped with concepts under Microarray and Microarray analysis techniques.
Computational biology research featured in it incorporates concerns from various other topics such as Context (language use), Bioinformatics, Genomics, Protein structure and Gene regulatory network. Research on Genetics presented in the journal focuses, in particular, on Sequence alignment, Sequence analysis, Human genome, Regulation of gene expression and Single-nucleotide polymorphism. It explores issues in Data mining which can be linked to other research areas like Software, Set (abstract data type) and Cluster analysis.
Topics in Artificial intelligence were tackled in line with various other fields like Natural language processing, Machine learning and Pattern recognition.
The most cited articles focus on DNA microarray, Computational biology, Genetics, Data mining and Artificial intelligence. The journal papers tackle topics on DNA microarray, which can potentially contribute to the wider field of Gene. The study of Computational biology in the most cited articles encompasses disciplines such as Annotation, as well as fields such as Information retrieval, all of which overlap with one another.
The journal mainly deals with areas of study such as Computational biology, DNA microarray, Artificial intelligence, Machine learning and Genome. The research on Computational biology tackled can also make contributions to studies in the areas of Context (language use), Identification (information), Annotation, Transcriptome and Metagenomics. While DNA microarray is the focus of BMC Bioinformatics, it also provided insights into the studies of Cancer, RNA-Seq and DNA sequencing.
The work on Artificial intelligence addressed in the journal expands to the thematically related Pattern recognition. Specifically, studies on Reference genome are prevalent in the Genome works discussed. The Cluster analysis study tackled is a key component of adjacent topics in the area of Data mining.
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 BMC 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 BMC 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, 3.65% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 12.21% were posted by at least one author from the top 10 institutions publishing in the journal. Another 5.26% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 9.26% of all publications and 73.26% 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.
David R. Stirling;Madison J. Swain-Bowden;Alice M. Lucas;Anne E. Carpenter
(2021)Sebastian M. Siegner;Mehmet E. Karasu;Markus S. Schröder;Zacharias Kontarakis
(2021)Changhee Han;Leonardo Rundo;Kohei Murao;Tomoyuki Noguchi
(2021)Hasindu Gamaarachchi;Hasindu Gamaarachchi;Chun Wai Lam;Gihan Jayatilaka;Hiruna Samarakoon
(2020)Joshua J. Levy;Alexander J. Titus;Curtis L. Petersen;Curtis L. Petersen;Youdinghuan Chen
(2020)Xihui Lin;Paul C. Boutros;Paul C. Boutros
(2020)Carlos A. Ruiz-Perez;Roth E. Conrad;Konstantinos T. Konstantinidis
(2021)Pursuing a degree in Computer Science offers diverse career paths, many of which can be explored through online education. For those interested in practical applications of technology, an online engineering degree provides a solid foundation in innovation and problem-solving across various industries.
The gaming industry is booming, and an game design online degree can equip students with the skills to create immersive experiences. This pathway combines creativity with technical expertise, opening doors to roles in development and production.
As cybersecurity threats rise, a cyber security degree is increasingly valuable. This focus prepares graduates to protect vital information systems and manage risks in an interconnected world.
For those intrigued by data-driven decision making, pursuing a data master online program offers advanced expertise in managing and analyzing large datasets, which is critical for businesses and organizations across sectors.
