Signal Transduction and Targeted Therapy

Top Research Topics at Signal Transduction and Targeted Therapy?

The primary areas of discussion in the journal are Cancer research, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Coronavirus disease 2019 (COVID-19), 2019-20 coronavirus outbreak and Virology. The studies on Cancer research discussed can also contribute to research in the domains of Cancer cell, Cancer, Metastasis, Carcinogenesis and Text mining. The work on Text mining tackled in it brings together disciplines like Computational biology and MEDLINE.

The research on MEDLINE featured in the journal combines topics in other fields like Internal medicine and Oncology. Signal Transduction and Targeted Therapy explores issues in Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which can be linked to other research areas like Immune system and Coronavirus Infections. The work on Coronavirus disease 2019 (COVID-19) presented in the journal focuses on Pandemic in particular.

Some problems in Virology that were presented in it overlapped with concepts under Antibody and Betacoronavirus.

• Cancer research (33.33%)
• Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (15.21%)
• Coronavirus disease 2019 (COVID-19) (14.36%)

What are the most cited papers published in the journal?

• NF-κB signaling in inflammation (1900 citations)
• The role of MicroRNAs in human cancer (847 citations)
• Lymphopenia predicts disease severity of COVID-19: a descriptive and predictive study (749 citations)

Research areas of the most cited articles at Signal Transduction and Targeted Therapy:

The journal articles primarily focus on research topics in Cancer research, Cancer, Immune system, Immunology and Clinical trial. While Cancer research is the focus of the published articles, it also provides insights into the studies of Cancer cell, Apoptosis and Microvesicles. The published papers address concerns in the field of Cancer by exploring it in line with topics in Epigenetics which intersect with Regulation of gene expression subjects.

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 Signal Transduction and Targeted Therapy (based on the number of publications) are:

• Min Wu (11 papers) published 3 papers at the last edition, 3 less than at the previous edition,
• Rong Xiang (10 papers) published 2 papers at the last edition, 1 more than at the previous edition,
• Wei Wang (10 papers) published 7 papers at the last edition, 5 more than at the previous edition,
• Shibo Jiang (9 papers) published 4 papers at the last edition, 1 less than at the previous edition,
• Shengyong Yang (9 papers) published 2 papers at the last edition the same number as 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 Signal Transduction and Targeted Therapy (based on the number of publications) are:

• Sichuan University (104 papers) published 43 papers at the last edition, 11 more than at the previous edition,
• Chinese Academy of Sciences (75 papers) published 40 papers at the last edition, 13 more than at the previous edition,
• Peking Union Medical College (63 papers) published 39 papers at the last edition, 21 more than at the previous edition,
• Sun Yat-sen University (59 papers) published 27 papers at the last edition, 2 more than at the previous edition,
• Fudan University (58 papers) published 30 papers at the last edition, 8 more 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, 3.26% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 56.46% were posted by at least one author from the top 10 institutions publishing in the journal. Another 7.87% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 11.24% of all publications and 24.44% 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.

Career Prospects in Medical Billing and Coding

While this article focuses on the wide-ranging and fascinating field of signal transduction and targeted therapy, our readers may also be interested in exploring alternative career paths in healthcare such as medical billing and coding. This role is often pivotal in the smooth operation of medical healthcare operations and ensures that healthcare providers are appropriately compensated for their services.

To become a successful medical biller and coder, one needs specific training and certification. Programs typically involve a comprehensive study of healthcare administration, anatomy, and healthcare coding processes. Most programs can be completed within two years, with numerous online and offline options available to accommodate diverse learning preferences and commitments.

Being a medical biller and coder offers diverse job opportunities, ranging from hospitals, doctors’ offices, nursing care facilities, and government agencies. The role is critical and in-demand across the healthcare spectrum.

For those interested in learning more about this career, we have a comprehensive guide on how to become a medical biller and coder in Ohio, which is home to many top-tier healthcare institutions with potential employment opportunities.

If you're curious about the earning potential in this field, the average salary for a medical biller in Ohio is a valuable resource. It’s important to keep in mind that salaries can vary based on experience, location, and the specific organization you’re employed with.

Whether you’re passionate about assisting healthcare providers or interested in contributing to the healthcare sector in a crucial administrative capacity, a career in medical billing and coding could be a rewarding choice.