| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Biology and Biochemistry | 754 | 9 | 21 | 6 |
| Computer Science | 789 | 13 | 20 | 6 |
The concepts of Data mining, Computational biology, Artificial intelligence, Data science and Gene are tackled in Biodata Mining. Some problems in Data mining that were presented in Biodata Mining overlapped with concepts under Support vector machine and Cluster analysis. The concepts on Computational biology presented in the journal can also apply to other research fields, including Genome-wide association study, Bioinformatics, Epistasis, Genome and Disease.
Research on Genome-wide association study addressed in it frequently intersections with the field of Genetic association. While Artificial intelligence is the focus of it, it also provided insights into the studies of Machine learning and Pattern recognition. The research on Data science discussed in it draws on the closely related field of Big data.
Research in Gene discussed is concerned with the study of Genetics as a whole. Biodata Mining links adjacent topics like Genetics with SNP.
The journal papers are organized to reinforce research efforts on Data mining, Data science, Artificial intelligence, Machine learning and Visualization. In addition to Data mining research, the most cited publications aim to explore topics under Genome-wide association study, Epistasis, Support vector machine, Cluster analysis and Systems biology. Aside from investigating topics in Feature selection under Machine learning, the published papers also explore concepts in Lack of knowledge, Information repository and Resource (project management).
The journal focuses largely on the fields of Artificial intelligence, Random forest, Machine learning, Computational biology and Feature selection. Issues in Artificial intelligence were discussed, taking into consideration concepts from other disciplines like Unstructured data and Pattern recognition. Machine learning research featured in the journal incorporates concerns from various other topics such as Quality (business) and Centrality.
The journal explores issues in Computational biology which can be linked to other research areas like Cell, Precision medicine, Gene expression, Disease and DrugBank. Biodata Mining connects the study in Representation (mathematics) with the closely related area of Data mining. Attendees of it participated in discussions that delve into both Data mining and Limit (mathematics).
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 Biodata Mining (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 Biodata Mining (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, 4.35% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 22.73% were posted by at least one author from the top 10 institutions publishing in the journal. Another 6.82% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 15.91% of all publications and 54.55% 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.
Davide Chicco;Niklas Tötsch;Giuseppe Jurman
(2021)Unknown
(2023)Alena Orlenko;Jason H Moore
(2021)Brianna Sierra Chrisman;Kelley M. Paskov;Nate Tyler Stockham;Kevin Tabatabaei
(2021)Jason H. Moore;Ian Barnett;Mary Regina Boland;Yong Chen
(2020)Theodore G. Drivas;Theodore G. Drivas;Anastasia Lucas;Marylyn D. Ritchie
(2021)Davide Chicco;Luca Oneto
(2021)Kelley M. Paskov;Jae-Yoon Jung;Brianna Sierra Chrisman;Nate Tyler Stockham
(2021)Pursuing a Computer Science degree online offers great flexibility and access to quality education. Many students look for programs that not only fit their budgets but also provide solid career prospects. Exploring the best degrees to make money can help identify career paths in tech that offer lucrative salaries and growth potential.
Affordability is a major consideration. Fortunately, there are plenty of affordable online bachelor degree programs that maintain high academic standards while minimizing costs.
Choosing the right institution also matters. Accredited programs ensure recognized credentials and quality education. Students can find numerous accredited online universities that streamline admission processes without application fees, making it easier to start the journey.
For those eager to enter the workforce sooner, fast track degree programs offer compressed timelines without compromising course rigor. These options help students quickly gain the skills needed to launch successful careers in computer science and related fields.
