| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 435 | 29 | 41 | 14 |
| Biology and Biochemistry | 674 | 6 | 18 | 8 |
The aim of Interdisciplinary Sciences: Computational Life Sciences is to expand the discussion of research in Computational biology, Artificial intelligence, Biochemistry, In silico and Computational Science and Engineering. The Computational biology works featured in Interdisciplinary Sciences: Computational Life Sciences incorporate elements from Gene, Disease and Bioinformatics. The Gene works, particularly on Genome are tackled in Interdisciplinary Sciences: Computational Life Sciences.
Artificial intelligence research featured in Interdisciplinary Sciences: Computational Life Sciences incorporates concerns from various other topics such as Machine learning and Pattern recognition. Interdisciplinary Sciences: Computational Life Sciences concentrates on Biochemistry topics that focus on Docking (molecular), Homology modeling, Enzyme, Amino acid and Active site. The research on Docking (molecular) featured in Interdisciplinary Sciences: Computational Life Sciences combines topics in other fields like Pharmacology and Binding site.
Most of the works presented in Interdisciplinary Sciences: Computational Life Sciences deals with Homology modeling but it intersects with the subject of Protein structure. In silico research presented falls under the umbrella topic of Genetics. Specifically, studies on Phylogenetic tree are prevalent in the Genetics works discussed.
The most cited articles mainly tackle studies in Computational biology, Docking (molecular), Biochemistry, Homology modeling and Genetics. While work presented in the journal papers provide substantial information on Computational biology, it also covers topics in Proteome, Microarray analysis techniques, Genome, Genomics and In silico. While Docking (molecular) is the focus of the most cited articles, it also provides insights into the studies of Baicalein, Pharmacology, Virology and Enzyme.
Interdisciplinary Sciences: Computational Life Sciences is mainly concerned with subjects like Artificial intelligence, Computational biology, Pattern recognition, Deep learning and Cluster analysis. Many of the studies tackled connect Artificial intelligence with a similar field of study like Machine learning. Interdisciplinary Sciences: Computational Life Sciences held discussions to help close the divide between two different fields of study: Computational biology and Computational Science and Engineering.
The Pattern recognition works featured in the journal incorporate elements from CAD and Feature (computer vision). While Deep learning is the focus of it, it also provided insights into the studies of Radiology, Medical imaging, Breast cancer and X ray image. The featured Cluster analysis studies mainly concentrate on Algorithm but also cover areas of interest in Image processing, Segmentation and Inference.
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 Interdisciplinary Sciences: Computational Life Sciences (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 Interdisciplinary Sciences: Computational Life Sciences (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, 3.53% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 19.51% were posted by at least one author from the top 10 institutions publishing in the journal. Another 4.88% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 13.41% of all publications and 62.20% 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.
Expanding your knowledge in the field of Interdisciplinary Sciences: Computational Life Sciences can lead to a varied and interesting career path. A bachelor's degree in this field can pave your way towards advanced positions like computational biologist, molecular modeler, bioinformatics scientist, or even an elementary school teacher specializing in science education, based in Mississippi. A Master's degree or PhD could open doors towards a career in academia or research.
Students pursuing studies in this field will find ample opportunities for internship positions that will help them gain practical experience in the industry. These may involve working on Computational biology projects, contributing in Artificial intelligence research, or conducting lab work in the realm of Biochemistry.
In conclusion, a career in Interdisciplinary Sciences: Computational Life Sciences is rewarding and full of opportunities to contribute to important scientific discoveries. Majority of graduates find positions in the industry, research labs, universities, hospitals, or government agencies.
Haiping Zhang;Konda Mani Saravanan;Yang Yang;Tofazzal Hossain
(2020)Jawad Rasheed;Alaa Ali Hameed;Chawki Djeddi;Akhtar Jamil
(2021)Gaurav Dhiman;V. Vinoth Kumar;Amandeep Kaur;Ashutosh Sharma
(2021)Abbas Khan;Mazhar Khan;Shoaib Saleem;Zainib Babar
(2020)Jawad Rasheed;Akhtar Jamil;Alaa Ali Hameed;Fadi Al-Turjman
(2021)Imran Ahmed;Gwanggil Jeon
(2021)Abbas Khan;Zainab Rehman;Huma Farooque Hashmi;Abdul Aziz Khan
(2020)Quan Quan;Jianxin Wang;Liangliang Liu;Liangliang Liu
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