Computational and Structural Biotechnology Journal

Top Research Topics at Computational and structural biotechnology journal?

The journal was organized to reinforce research efforts on Computational biology, Gene, Genome, Artificial intelligence and Cell biology. In addition to Computational biology research, it aims to explore topics under Transcriptome, Function (biology) and DNA sequencing. Gene research discussed in it aim to provide more information in the subject of Genetics.

The journal features Artificial intelligence research that overlaps with concepts in Machine learning.

• Computational biology (26.60%)
• Gene (10.56%)
• Genome (8.13%)

What are the most cited papers published in the journal?

• Machine learning applications in cancer prognosis and prediction. (1125 citations)
• Machine Learning and Data Mining Methods in Diabetes Research. (391 citations)
• Machine Learning Methods for Histopathological Image Analysis. (346 citations)

Research areas of the most cited articles at Computational and structural biotechnology journal:

The journal papers investigate studies in Computational biology, Data science, Bioinformatics, Cell biology and Data mining. The works on Computational biology tackled in the most cited publications bring together disciplines like Genome, Gene, Microbiology, Protein engineering and Drug discovery. The most cited articles explore research in Bioinformatics alongside concepts in Disease and other areas of study in Cancer.

What topics the last edition of the journal is best known for?

• Gene
• Enzyme
• DNA

The previous edition focused in particular on these issues:

Computational and structural biotechnology journal primarily tackles Computational biology, Gene, Artificial intelligence, Genome and Biophysics. Computational and structural biotechnology journal connects research in Computational biology with the related topic of microRNA. The Gene study featured falls within the wider field of Genetics.

It explores research in Artificial intelligence and the adjacent study of Machine learning. The journal dives deep in exploring the relationship between the study of Biophysics and Molecular dynamics.

The most cited articles from the last journal are:

• Chemokines and chemokine receptors during COVID-19 infection (19 citations)
• Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis. (16 citations)
• Host transcriptomic profiling of COVID-19 patients with mild, moderate, and severe clinical outcomes (13 citations)

Papers citation over time

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.

Top authors and change over time

The top authors publishing in Computational and structural biotechnology journal (based on the number of publications) are:

• Sarah J Routledge (12 papers) absent at the last edition,
• Marina Kirkitadze (6 papers) published 2 papers at the last edition,
• Xiaosheng Wang (6 papers) published 3 papers at the last edition, 1 more than at the previous edition,
• Jiri Damborsky (6 papers) published 1 paper at the last edition, 3 less than at the previous edition,
• Gianni Panagiotou (6 papers) absent at the last edition.

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.

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing in Computational and structural biotechnology journal (based on the number of publications) are:

• Chinese Academy of Sciences (35 papers) published 17 papers at the last edition, 4 more than at the previous edition,
• Spanish National Research Council (21 papers) published 14 papers at the last edition, 10 more than at the previous edition,
• Zhejiang University (16 papers) published 10 papers at the last edition, 5 more than at the previous edition,
• Fudan University (13 papers) published 5 papers at the last edition the same number as at the previous edition,
• University of Minnesota (13 papers) published 2 papers at the last edition, 4 less than at the previous edition.

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.

Publication chance based on affiliation

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, 7.23% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 16.23% were posted by at least one author from the top 10 institutions publishing in the journal. Another 7.79% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 11.04% of all publications and 64.94% were from other institutions.

Returning Authors Index

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.

Returning Institution Index

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.

The experience to innovation index

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:

• Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
• Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
• Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
• Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
• Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.