IEEE/ACM Transactions on Computational Biology and Bioinformatics

Top Research Topics at IEEE/ACM Transactions on Computational Biology and Bioinformatics?

The journal tackles a plethora of topics, such as Artificial intelligence, Computational biology, Data mining, Machine learning and Algorithm. The in-depth study on Artificial intelligence also explores topics in the intersecting field of Pattern recognition. In it, Genetics, Gene and Genomics are investigated in conjunction with one another to address concerns in Computational biology research.

The journal focuses on Genomics research as part of the broader topic of Genome. The journal connects the study in Data mining with the closely related area of Cluster analysis. The study on Machine learning presented in the journal intersects with subjects under the field of Data modeling.

The studies on Algorithm discussed can also contribute to research in the domains of Theoretical computer science and Set (abstract data type). IEEE/ACM Transactions on Computational Biology and Bioinformatics dives deep in exploring the relationship between the study of Feature extraction and Feature selection.

• Artificial intelligence (29.86%)
• Computational biology (17.78%)
• Data mining (16.07%)

What are the most cited papers published in the journal?

• Biclustering Algorithms for Biological Data Analysis: A Survey (1805 citations)
• A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis (343 citations)
• Efficient Detection of Network Motifs (312 citations)

Research areas of the most cited articles at IEEE/ACM Transactions on Computational Biology and Bioinformatics:

The published papers focus on Artificial intelligence, Data mining, Machine learning, Pattern recognition and Algorithm. Aside from discussions in Data mining, the published papers also deal with the subject of Cluster analysis which intersects with Identification (information) disciplines. The journal papers facilitate discussions on Algorithm that incorporate concepts from other fields like Protein structure prediction, Theoretical computer science and Mathematical optimization.

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

• Gene
• Artificial intelligence
• Statistics

The previous edition focused in particular on these issues:

The main points discussed in IEEE/ACM Transactions on Computational Biology and Bioinformatics deals with Artificial intelligence, Computational biology, Pattern recognition, Machine learning and Deep learning. The journal tackles issues in Artificial intelligence, particularly in the topics of Feature extraction, Convolutional neural network, Artificial neural network, Feature (machine learning) and Cluster analysis. While work presented in the journal provided substantial information on Feature extraction, it also covered topics in Feature selection and Support vector machine.

IEEE/ACM Transactions on Computational Biology and Bioinformatics addresses concerns in Computational biology which are intertwined with other disciplines, such as Identification (information) and Genome, Gene, DNA sequencing, Genomics. It holds forums on Pattern recognition that merges themes from other disciplines such as Image (mathematics) and Feature (computer vision). While Machine learning is the focus of it, it also provided insights into the studies of Field (computer science), Graph (abstract data type), Drug discovery and Benchmark (computing).

The most cited articles from the last journal are:

• Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images. (142 citations)
• CNN-RNN Based Intelligent Recommendation for Online Medical Pre-Diagnosis Support (51 citations)
• A Fast Linear Neighborhood Similarity-Based Network Link Inference Method to Predict MicroRNA-Disease Associations (46 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 IEEE/ACM Transactions on Computational Biology and Bioinformatics (based on the number of publications) are:

• Jianxin Wang (61 papers) published 20 papers at the last edition, 3 more than at the previous edition,
• Fang-Xiang Wu (49 papers) published 13 papers at the last edition, 3 less than at the previous edition,
• Yi Pan (47 papers) published 11 papers at the last edition, 2 less than at the previous edition,
• Min Li (36 papers) published 9 papers at the last edition, 2 less than at the previous edition,
• De-Shuang Huang (29 papers) published 3 papers at the last edition, 4 less than at the previous 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 IEEE/ACM Transactions on Computational Biology and Bioinformatics (based on the number of publications) are:

• Central South University (74 papers) published 25 papers at the last edition, 7 more than at the previous edition,
• City University of Hong Kong (55 papers) published 2 papers at the last edition, 9 less than at the previous edition,
• Tongji University (50 papers) published 5 papers at the last edition, 3 less than at the previous edition,
• Georgia State University (49 papers) published 11 papers at the last edition, 3 less than at the previous edition,
• University of Saskatchewan (47 papers) published 13 papers at the last edition, 1 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, 14.32% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 17.28% were posted by at least one author from the top 10 institutions publishing in the journal. Another 9.07% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 15.86% of all publications and 57.79% 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.

Contributing Schools and Institutions

An interesting perspective in reviewing a journal is the investigation of the primary schools and institutions contributing to the published works. Specifically, for the IEEE/ACM Transactions on Computational Biology and Bioinformatics, there are numerous institutions making significant academic contributions. The exploration of the institutions holding pivotal roles in the submissions to this journal is an essential part of understanding the academic environment and influences shaping this domain's future direction. By studying the institutions behind the research published, we can gain insights into the sectors actively pushing the boundaries in computational biology and bioinformatics. For would-be researchers and students interested in this path, knowing the universities and research institutions driving the field can provide a roadmap for potential study or research collaboration opportunities. For instance, if you are considering the career path into academia as a private school teacher in Maryland with a focus in computational biology, understanding the active institutions in this domain could inform your career choices or study options. In ensuing sections, we provide a detailed breakdown of the prominent institutions contributing to the journal, their locations, and the number of contributions they have made to the recent editions of the journal.