| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 728 | 13 | 14 | 7 |
The journal mostly deals with topics like Health informatics, Artificial intelligence, Computational biology, Data mining and Machine learning. While the journal focused on Health informatics, it was also able to explore topics like Social media, Data science and Social network. Artificial intelligence research discussed connects with the study of Pattern recognition.
In addition to Computational biology research, the journal aims to explore topics under In silico, Gene, Docking (molecular) and Disease. It features In silico research that overlaps with concepts in ADME. The featured Gene study falls within the wider topic of Genetics.
Research on Disease addressed in the journal frequently intersections with the field of Cancer.
The main points discussed in the journal articles deal with Artificial intelligence, Machine learning, Data mining, Health informatics and Bioinformatics. The most cited publications facilitate discussions on Artificial intelligence that incorporate concepts from other fields like Complement (set theory), Set (psychology) and Pattern recognition. The works on Machine learning tackled in the journal papers bring together disciplines like Social network analysis, Cognitive neuroscience of visual object recognition, Classifier (UML), Field (computer science) and Link (knot theory).
The journal facilitates discussions on Artificial intelligence, Health informatics, In silico, Algorithm and Biochemistry. Network Modeling Analysis in Health Informatics and BioInformatics focuses on Artificial intelligence but the discussions also offer insight into other areas such as Sensitivity (control systems), Machine learning and Pattern recognition. Network Modeling Analysis in Health Informatics and BioInformatics features works in Machine learning, more specifically Particle swarm optimization, Regression analysis and Quantitative structure–activity relationship, and explores their relation to disciplines like Human health and Rapid response.
Green computing and World Wide Web are some topics wherein Health informatics research discussed in it have an impact. The In silico works featured in it incorporate elements from Virtual screening, ADME, Drug and Pharmacophore, Stereochemistry. The studies in Algorithm featured incorporate elements of E-commerce, Service (systems architecture), Modeling and simulation and Glucagon, Insulin.
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 Network Modeling Analysis in Health Informatics and 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 Network Modeling Analysis in Health Informatics and 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, 12.28% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 12.00% were posted by at least one author from the top 10 institutions publishing in the journal. Another 4.00% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 8.00% of all publications and 76.00% 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.
The broad and expansive spectrum of topics that are explored in the Network Modeling Analysis in Health Informatics and BioInformatics journal also carries significant relevance within the field of education. In particular, the integration of artificial intelligence, machine learning, and health informatics is crucial in the learning environment, particularly for educators pursuing specializations in these emerging fields. For instance, an aspiring private school teacher in Maryland, dedicated towards Computer Science or Biology, would find enriching insights in our journal, broadening the scope and depth of her knowledge, hence, enhancing her classroom instruction and facilitating advanced learning. Understanding key topics such as genetics, artificial intelligence, and computational biology would not just bolster her academic competencies, but equip her with the necessary skills to bring about innovation in educational techniques. To delve into the intricacies of becoming a private school teacher in Maryland, particularly in these science-related disciplines, refer to our blog that outlines the requirements, qualifications, and pathways for pursuing this career in these specialized fields. Please visit private school teacher requirements maryland for more detailed information. By aligning the academic demands of the field of education with our research topics and academic discussions, our journal aspires to contribute to the cultivation of a more informed and knowledgeable educational workforce. We believe in disseminating scientific knowledge beyond academia, reaching professions such as teaching that form the backbone of our society.
Rabia Javed;Rabia Javed;Mohd Shafry Mohd Rahim;Tanzila Saba;Amjad Rehman
(2020)Mohsen Yoosefi Nejad;Maryam Sadat Delghandi;Ahmed Omar Bali;Mehdi Hosseinzadeh
(2020)Aaron N. Richter;Taghi M. Khoshgoftaar
(2020)Pallabi Patowary;Rosy Sarmah;Dhruba K. Bhattacharyya
(2020)Marianna Milano;Wayne B. Hayes;Pierangelo Veltri;Mario Cannataro
(2020)Marianna Milano;Chiara Zucco;Mario Cannataro
(2021)Shabnam Shadroo;Mohsen Yoosefi Nejad;Ahmed Omar Bali;Mehdi Hosseinzadeh
(2020)Magdalyn E. Elkin;Xingquan Zhu
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