Journal:Analyzing the field of bioinformatics with the multi-faceted topic modeling technique

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Full article title Analyzing the field of bioinformatics with the multi-faceted topic modeling technique
Journal BMC Bioinformatics
Author(s) Heo, Go Eun; Kang, Keun Young; Song, Min; Lee, Jeong-Hoon
Author affiliation(s) Yonsei University, POSTECH
Primary contact Email: min dot song at yonsei dot ac dot kr
Year published 2017
Volume and issue 18 (Suppl 7)
Page(s) 251
DOI 10.1186/s12859-017-1640-x
ISSN 1471-2105
Distribution license Creative Commons Attribution 4.0 International
Download (PDF)


Background: Bioinformatics is an interdisciplinary field at the intersection of molecular biology and computing technology. To characterize the field as a convergent domain, researchers have used bibliometrics, augmented with text-mining techniques for content analysis. In previous studies, Latent Dirichlet Allocation (LDA) was the most representative topic modeling technique for identifying topic structure of subject areas. However, as opposed to revealing the topic structure in relation to metadata such as authors, publication date, and journals, LDA only displays the simple topic structure.

Methods: In this paper, we adopt the Author-Conference-Topic (ACT) model of Tang et al. to study the field of bioinformatics from the perspective of keyphrases, authors, and journals. The ACT model is capable of incorporating the paper, author, and conference into the topic distribution simultaneously. To obtain more meaningful results, we used journals and keyphrases instead of conferences and the bag-of-words. For analysis, we used PubMed to collect forty-six bioinformatics journals from the MEDLINE database. We conducted time series topic analysis over four periods from 1996 to 2015 to further examine the interdisciplinary nature of bioinformatics.

Results: We analyzed the ACT Model results in each period. Additionally, for further integrated analysis, we conducted a time series analysis among the top-ranked keyphrases, journals, and authors according to their frequency. We also examined the patterns in the top journals by simultaneously identifying the topical probability in each period, as well as the top authors and keyphrases. The results indicate that in recent years diversified topics have become more prevalent, and convergent topics have become more clearly represented.

Conclusion: The results of our analysis imply that over time the field of bioinformatics becomes more interdisciplinary where there is a steady increase in peripheral fields such as conceptual, mathematical, and system biology. These results are confirmed by integrated analysis of topic distribution as well as top ranked keyphrases, authors, and journals.

Keywords: bioinformatics, text mining, topic modeling, ACT model, keyphrase extraction


Over the years, academic subject areas have converged to form a variety of new, interdisciplinary fields. Bioinformatics is one example. Research domains from molecular biology to machine learning are used in conjunction to better understand complex biological systems such as cells, tissues, and the human body. Due to the complexity and broadness of the field, bibliometric analysis is often adopted to assess the current knowledge structure of a subject area, specify the current research themes, and identify the core literature of that area.[1]

Bibliometrics identifies research trends using quantitative measures such as a researcher’s number of publications and citations, journal impact factors, and other indices that can measure impact or productivity of author or journal.[2][3][4][5] In addition, other factors such as the affiliation of authors, collaborations, and citation data are often incorporated into bibliometric analysis.[6][7][8][9]

Previous studies mainly rely on quantitative measures and suffer from the lack of content analysis. To incorporate content analysis into bibliometrics, text-mining techniques are applied. Topic-modeling techniques are mostly adopted to identify the topics of a subject area while analyzing that area more abundantly.[10][11][12][13] These techniques allow for enriched content analysis. As an extension of Latent Dirichlet Allocation (LDA), which is the best received topic-modeling technique, Steyvers et al.[14] proposed the author-topic modeling technique that analyzes authors and topics simultaneously. They identify the authors’ impact or productivity of researchers in a given subject area.[15][16] By adding multiple conditions to LDA, Tang et al.[17] suggested a new methodology, called the Author-Conference-Topic (ACT) model that analyzes the author, conference, and topic in one model to understand the subject area in an integrated manner.

In this paper, we apply the ACT model to examine the interdisciplinary nature of bioinformatics. Unlike studies that use extended versions of LDA for topic analysis, the ACT model enables us to analyze topic, author, and journal at one time, providing an integrated view for understanding bioinformatics. The research questions that we are to investigate in this paper are: 1) What are the topical trends of bioinformatics over time? 2) Who are the key contributors in major topics of bioinformatics?, and 3) Which journal is leading which topic?

To address these questions, we collect PubMed articles in XML format and extract metadata and content such as the PMID, author, year, journal, title, and abstract. From the title and abstract, we extract keyphrases, which provide more meaningful interpretations than single words, as an input of the ACT model. We also divide the collected datasets into four time periods to examine the topic changes over time. The results of ACT model–based analysis show that various topics begin to appear and mixed subject topics become more apparent over time.

The rest of the paper is organized as follows. In the "Related work" section, we discuss work related to bibliometric analysis and topic modeling. We then describe the proposed method in the "Methods" section. We analyze and discuss the results of leading topics, authors, and journals in the "Results" and "Discussion" sections. Finally, we conclude the paper and suggest future lines of inquiry in the "Conclusion" section.

Related work

Bibliometric analysis

Bibliometric analysis identifies the research trends in a given subject area and core journals or documents, and it helps with contrasting analysis. Many bibliometric studies use the number of published articles or journal impact factors to measure research productivity or to identify core journals in a specific field. Soteriades and Falalgas[3] applied quantitative and qualitative measurements to analyze the fields of preventive medicine, occupational and environmental medicine, epidemiology, and public health using the number of articles and impact factor. Ugolini et al.[4] measured research productivity and evaluated the publication trends in the field of cancer molecular epidemiology. To quantify productivity, they used the number of articles and the average and sum of impact factors. To evaluate publication trends, they collected and divided the keywords from MeSH terms about the publication into six groups. Ramos et al.[18] measured the national research activity of the tuberculosis field, using impact factor and the first author’s address. Claude et al.[19] examined research productivity by using distribution of publications related to medicine and ANN, the subfield of biology. They used the number of publications, impact factor, and journal category compared with national gross domestic product (GDP). In the bioinformatics field, Patra and Mishra[20] used the number of articles, publication of each journal, publication type, and the impact factor of journals to understand the growth of bioinformatics. They also found the core journals in the bioinformatics fields. Using author affiliation, they applied Lotka’s law to assess the distribution of each author’s productivity. Chen et al.[2] identified research trends using statistical methods based on the type of publication, language, and distribution of nation or institution. They measured h-index, adding statistical materials with the number of citations. Through this, they analyzed the research productivity by topic, institution, and journal. In addition, they conducted a keyword analysis to comprehend the research trend in a macroscopic view.

Mainstream bibliometrics research focuses on identifying the knowledge structure of a certain field with quantitative measures. In addition, some studies use author information or the collaboration pattern among authors to understand the certain field. Seglen and Aksnes[9] used the size and the productivity of research groups in the microbiology field in Norway as a measurement for bibliometric analysis. Geaney et al.[7] performed bibliometric analysis and density-equalizing mapping on scientific publications related to type 2 diabetes mellitus. They collected citation data and used various citation-oriented measures such as the number of citations, the average number of citations per journal, the total number of publications, impact factor, and Eigenfactor score. To conduct content analysis and study the collaboration pattern between authors and the core sub-field of AIDS, Macías-Chapula and Mijangos-Nolasco[8] analyzed MeSH thesaurus using check tags, main headings, and subheadings of each MeSH term hierarchy. In addition, to measure the national research productivity, they used the authors’ address information. Bornmann and Mutz[6] recently identified the development of modern science by bibliometric analysis. They divide the data into three time periods to analyze the changes of fields over time.

Text mining applied to bibliometrics

Recently, there have been many attempts to apply text-mining techniques to bibliometric analysis to identify the knowledge structure of the field or measure its influence on other researchers and their fields and productivity. Song and Kim[11] collected full-text articles from PubMed Central and computed their citation relation. They infer the knowledge structure and understand the trend of the bioinformatics field. In a similar vein, Song et al.[12] measured the influence and productivity of bioinformatics by mining full-text articles retrieved from PubMed Central. To calculate the field’s productivity, they identified the most productive author, nation, institution, and topic word; to calculate its influence, they identified the most-cited paper, author, and rising researcher. Song et al.[21] analyzed topic evolution in the bioinformatics field using DBLP data in the field of computer science. To identify topic trends over time, they divided a dozen years (2000–2011) into four periods and applied Markov Random Field-based topic clustering. For automatic clustering labeling, they calculated topic similarity based on Within-Period Cluster Similarity (WPCS) and Between-Period Cluster Similarity (BPCS). Their approach created topic graphs that show interaction among topics over some period of time. Lee et al.[22] mapped the Alzheimer’s disease field into three different perspectives: indexer, author, and citer. They applied entity-metrics[23] the extended notion of bibliometrics, to analyze the field by constructing four kinds of networks that convey these three perspectives.

These studies identify the knowledge structure of a certain field by constructing bibliometric networks or databases with text-mining techniques. The most prevalent approach is to apply topic modeling to content analysis as a part of bibliometrics. Starting from the probabilistic Latent Semantic Indexing (pLSI)[24] model, Latent Dirichlet Allocation (LDA)[25] is the most accepted topic modeling technique for bibliometrics. While each document consists of a set of topics in pLSI, using the LDA model results in a more precise manipulation being added to organize the topics. Yan[13] used the LDA model to measure the influence and popularity of library and information science. He also identified the most-cited area and the patterns in this field. Jeong and Song’s[10] research measured the time gap among three different resources — web, patent, and scientific publication — in two research domains by applying the LDA model. The basic input unit for LDA is a set of documents. To organize author information into topics, Rosen-Zvi et al.[16] and Steyvers et al.[14] proposed the author-topic model with different theoretical background. Li et al.[15] identified the relations between authors and topics by using the author-topic model. They analyzed the topic distribution to examine how many authors are associated with a certain topic. Also through the number of authors, they identified topics that are studied by many researchers. Tang et al.[17] proposed the ACT model which identifies paper, author, and conference simultaneously. Additionally, they developed the ArnetMiner system for mining academic research social networks. Tang et al.[26] also supplement ArnetMiner for a topic level expertise search over heterogeneous networks using the ACT model. It generates the most issued topics, author’s interestedness, paper search, academic suggestion, and experts in a specific field. Kim et al.[27] adopted the ACT model in terms of citation analysis. They collected their dataset in the field of oncology from PubMed Central, which provides the full-text articles in the biomedical field. They utilized the ACT model for analyzing citation sentences and journals instead of abstracts and conferences.

In conclusion, most previous studies identified knowledge structures by adopting not only bibliometric analysis but text-mining techniques such as the LDA model. To supplement bibliometric analysis, there are many attempts to incorporate content analysis into bibliometrics by adopting the LDA model text-mining techniques. However, the main limitation of this application of the LDA model, the representative method for trend analysis, is that it only explains topical trends by using one parameter such as the bag-of-words on documents via topical terms. It is not sufficient to conduct comprehensive analysis for understanding knowledge disciplines. Therefore, in this paper, we apply the ACT model to the bioinformatics field for integrated analysis. Applying the ACT model, we aim to explore the importance of authors and journals in relation to topics. We divided the collected datasets into four periods to trace the changes of topic, author, and journal ranking over time, combining the results with bibliometric analysis.


In this section, we describe data collection, preprocessing, and keyphrase extraction to feed input into the ACT model. Figure 1 illustrates the overflow of our approach; detailed descriptions of each component are provided in the following section.

Fig1 Heo BMCBioinformatics2017 18.gif

Figure 1. Research overflow. Research overflow of our approach consists of data collection, preprocessing, keyphrase extraction, ACT model application, and topic analysis.

Data collection

For analysis, we collected the 48 journals in the bioinformatics field as used by Song and Kim.[11] Forty-six out of the 48 journals were found via the advanced search tool provided by PubMed. Two journals, Advanced Bioinformatics and Genome Integration, were not retrieved from PubMed. We downloaded the 46 PubMed-listed journals in XML format (Table 1). The total number of papers indexed in these journals was 241,569; Biochemistry had the greatest number of papers with 62,270, accounting for 25.78% of the collected publications.

Table 1. Statistics of collected publications
Ranking Journal name Number of papers Ratio (%)
1 Biochemistry 62,270 25.78
2 Journal of Molecular Biology 29,968 12.41
3 The EMBO Journal 17,296 7.16
4 Journal of Theoretical Biology 12,200 5.05
5 Bioinformatics 9,847 4.08
6 Human Molecular Genetics 9,347 3.87
7 Genomics 8,316 3.44
8 BMC Genomics 7,741 3.20
9 BMC Bioinformatics 6,780 2.81
10 Protein Science: A publication of the Protein Society 6,047 2.50
11 Journal of Proteome Research 5,575 2.31
12 Proteomics 5,545 2.30
13 Journal of Biotechnology 5,204 2.15
14 PLOS Genetics 5,139 2.13
15 PLOS Computational Biology 3,852 1.59
16 BMC Research Notes 3,743 1.55
17 Mammalian Genome 3,499 1.45
18 Genome Biology 3,411 1.41
19 PLOS Biology 3,280 1.36
20 Trends in Biochemical Sciences 3,171 1.31
21 Trends in Genetics 3,035 1.26
22 Journal of Molecular Modeling 2,852 1.18
23 Molecular & cellular proteomics: MCP 2,796 1.16
24 Trends in Biotechnology 2,353 0.97
25 Bulletin of Mathematical Biology 2,331 0.96
26 Journal of Proteomics 2,158 0.89
27 Physiological Genomics 1,794 0.74
28 Journal of Computer-Aided Molecular Design 1,706 0.71
29 BMC Systems Biology 1,397 0.58
30 Bioinformation 1,297 0.54
31 Pharmacogenetics and Genomics 1,072 0.44
32 Statistical Methods in Medical Research 976 0.40
33 Journal of Computational Neuroscience 925 0.38
34 Molecular Systems Biology 822 0.34
35 Genome Medicine 676 0.28
36 Theoretical Biology and Medical Modeling 498 0.21
37 Comparative and Functional Genomics 466 0.19
38 Neuroinformatics 385 0.16
39 Cancer Informatics 355 0.15
40 Briefings in Functional Genomics & Proteomics 290 0.12
41 Evolutionary Bioinformatics 249 0.10
42 Algorithms for Molecular Biology 245 0.10
43 Journal of Biomedical Semantics 240 0.10
44 BioData Mining 149 0.06
45 EURASIP Journal on Bioinformatics and Systems Biology 140 0.06
46 Source Code for Biology and Medicine 131 0.05
Total 241,569 100.00

Data preprocessing and keyphrase extraction

We limited the publication year back to 1996 and divided the dataset into the following four time periods to identify the trend of bioinformatics from the birth of the field to present: 1996–2000, 2001–2005, 2006–2010, and 2011–2015 (Fig. 2).

Fig2 Heo BMCBioinformatics2017 18.gif

Figure 2. Research overflow. Data distribution. Publication year of our dataset is from 1996 to 2015. To identify topical trends of bioinformatics, we divided total 20 years into four time periods. X-axis is publication year and Y-axis is the number of papers.

As shown in Fig. 2, there is a relatively consistent increase in the number of papers. There are fewer than half as many papers published in 2015 than in 2014 because we collected our dataset in June 2015. Nevertheless, we included the 2015 data to observe the latest publication trends.

Table 2 presents the breakdown of our dataset by period. As in Fig. 2, the fourth period is the most productive, containing 53,520 papers, or 31.46% of the total dataset. The most productive year is 2014, which accounts for 7.20% with 12,251 papers. The total number of papers for all 20 years is 170,099. This number is different from Table 1 (241,569) as a result of preprocessing; we excluded papers that did not have an abstract.

Table 2. Time-based statistics for 20 years
Year Number of paper Ratio (%) Ranking
1996 5,713 3.36 19
1997 5,549 3.26 20
1998 5,853 3.44 18
1999 5,877 3.46 17
2000 5,947 3.50 16
Period 1 28,939 17.01
2001 6,199 3.64 14
2002 6,456 3.80 13
2003 6,668 3.92 12
2004 7,564 4.45 11
2005 8,545 5.02 10
Period 2 35,432 20.83
2006 9,845 5.79 9
2007 10,112 5.94 8
2008 10,352 6.09 7
2009 10,868 6.39 6
2010 11,031 6.49 5
Period 3 52,208 30.69
2011 11,518 6.77 4
2012 11,986 7.05 2
2013 11,695 6.88 3
2014 12,251 7.20 1
2015 6,070 3.57 15
Period 4 53,520 31.46
Total 170,099 100.00

We extract various metadata, such as the PMID, author, publication year, journal title, title, and abstract from XML formatted records. After XML processing, we combine the title with abstract and conduct keyphrase extraction. For keyphrase extraction, we use MAUI, which has the keyphrase model trained with MeSH terms.[28] In this dataset, there are 500 documents and several keys consisting of MeSH terms about each document, manually assigned by the indexer. MAUI is a newer version of the keyphrase extraction algorithm KEA.[29] Keyphrase extraction enables researchers to select representative phrases to make topic detection more meaningful. Therefore, we use keyphrases extracted from the title or abstract as our input for the ACT model instead of individual words.

Table 3 shows the results of keyphrase extraction and other metadata such as the title and publication year from the PubMed record PMID 26030820.

Table 3. Example of results of keyphrase extraction and other metadata from PMID of 26030820
Information Content
Title encoding cell amplitude frequency modulation
Author Micali Gabriele, Aquino Gerardo, Richards David M, Endres Robert G
Year 2015
Journal PLOS computational biology
Keyphrases Down-Regulation - Ion Channels - Ions - L Cells (Cell Line) - Ligands - Social Control, Formal - Social Control, Informal - Up-Regulation

ACT model application

The ACT model, proposed by Tang et al.[17] as an extension of the LDA model[25], is a unified topic model for modeling various metadata simultaneously. This model starts with the assumption that the order of the topic created by the paper, author, and conference is the same. It also estimates the statistical distribution associated with all topics for the purpose of discovering latent topic distribution related with paper, author, and conference. In this paper, two metadata types are changed. First, conference is replaced with journal. Also, a bag-of-keyphrases are used instead of a bag-of-words to represent documents in a more precise manner.

Figure 3 illustrates the ACT model, and Table 4 provides a description of the parameters used. Model estimation is conducted by setting parameters, and for estimation of the model parameter, the Gibbs sampling method is employed. Gibbs sampling takes samples from a probability distribution by using the Markov Chain Monte Carlo sampling method. Three parameters for estimating the model are as follows: 1) θ is the topic probability for a given author (author*topic matrix), 2) φ is the journal probability for a given topic (topic*journal matrix), and 3) ψ is the word probability for a given topic (topic*word matrix). According to the independence assumption, joint distribution of topic, author, journal, and word stand on the basis Ad, meaning the total number of authors in paper d. In our experiments, we set the hyper-parameters α, β, and γ, which are parameters of a prior with α = 50/T, β = 0.01, and γ = 0.01, respectively. In addition, we fix the number of topics K to 20, the number of top keyphrases to 30, and the number of iterations to 1,000. With these settings, we selected 15 out of 20 topics for analysis.

Fig3 Heo BMCBioinformatics2017 18.gif

Figure 3. ACT Model. Author-Conference-Topic (ACT) Model is proposed by Tang et al. which is a probabilistic topic model to extract topics, authors, and conference simultaneously.

Table 4. Notation and description of the ACT model
Notation Description
d Paper
x Author
w Word
j Journal
D Total number of papers
A Total number of authors
K Selected number of topics
Nd Total number of words in paper d
Ad Total number of authors in paper d
z Topic
θ Author-topic distribution
φ Topic-journal distribution
ψ Topic-word distribution
α,β,γ Hyper-parameters of Dirichlet distribution


To examine the consistency of our results, we repeated each run 10 times with a topic number of 20. After that, we calculated the similarity between topics. For statistical analysis, we computed Pearson correlation coefficients between any two topics and averaged them out. Table 5 shows the average of correlation coefficients per execution. In all runs, Pearson correlation coefficients between topics were weakly, positively correlated. Also, the range of correlation was not wide (0.13 to 0.18). It implies that there was no difference in similarity between topics regardless of different runs. This result can verify consistency and reliability of our topic clusters.

Table 5. Average of Pearson correlation coefficients result
Number of runs Pearson correlation coefficients
1 0.155
2 0.140
3 0.152
4 0.177
5 0.180
6 0.146
7 0.136
8 0.160
9 0.158
10 0.178

In addition, to evaluate the topic model results, we used perplexity which is a well-known measurement in information theory for testing goodness of a model. In our case, we make a test set by collecting bioinformatics journals published in 2016. The sample size is 1,000 papers. In the training set, we divided 20 years into four periods and calculated the perplexity by setting the number of topics as 10, 20, 30, and 50 respectively. The results are presented in Table 6 and Fig. 4. As shown in Table 6 and also confirmed in Fig. 4, there is not much difference in performance in regards to the number of topics by perplexity. However, there is a clear difference among periods by perplexity. In particular, the third period has the highest perplexity value, which implies that it is the most difficult period as to predicting the topic trend in 2016 in the bioinformatics field.

Table 6. Perplexity result of topic model
Number of topics 1996–2000 2001–2005 2006–2010 2011–2015 Average
10 2,712 2,060 875,088 501,176 345,259
20 2,978 3,161 726,329 513,176 311,411
30 2,872 2,176 742,307 481,875 307,308
50 2,480 2,149 635,960 466,676 276,816
Average 2,760 2,387 744,921 490,726

Fig4 Heo BMCBioinformatics2017 18.gif

Figure 4. Perplexity result. For evaluation of topic modeling results, we used perplexity. We calculated perplexity per each period with the number of topics as 10, 20, 30, and 50. X-axis is period and Y-axis means a perplexity value.

Together with this result, we analyzed the results of the ACT model.


We analyzed leading authors and journals in relation to topics over time. In the following section, we provide the detailed explanations of the trend per period.

Topic analysis per period

The results of our time series topic analysis show that topics seem to be more distinct and subdivided closer to present. In addition, new topics have emerged in recent years, and they do not make a new cluster, which means the exclusive topics become apparent. The results also show that research fields such as molecular biology, genomics, genetics, and proteomics play a supplementary role in biology, but also become diversified into a unique field.

First period analysis

In the first period (1996–2000), five dominant topic clusters were identified (Additional file 1: Appendix 1). Those five topics were mainly associated with proteins and peptides. Phrases such as "molecular biology" and "chemical compound" were widespread, and thermodynamics- and kinematics-related topics appeared. These topics were composed of jargon in their specific fields. The mathematical biology field was represented by topical phrases such as "database," "cluster analysis," "model," "theoretical," and "software."

Topics 0, 2, and 3 were about molecular biology, derived from biochemistry and composed of hydrogen bonding–related chemical compounds such as enzymes or lipids. Topics 4, 5, 6, and 7 were related to proteins, peptides, and protein structure. Topics 9 and 14 included words such as "probability" and "statistics," which are related to mathematical biology. Topics 13, 17, 18, and 19 covered mutagenesis, disease, and syndromes, which are all related to genetic diseases; mutagenesis consists of gene mutation, and syndromes are caused by genetic disorder. Topic 19 included the word "genetic," a parent category of previously mentioned words. Topics 15 and 16 consisted of kinetics.

Protein-related topics were dominant, and authors involved in peptide and protein structure were prevalent in the first period. Authors who were in topic 5, such as A. R. Fersht, J. M. Thornton, C. M. Dobson, L. Serrano, and M. Karplus, had a high probabilistic distribution value, which means they were leading researchers in this area. Their research interest was mainly in protein structure, and they published in the Journal of Molecular Biology. This journal appeared in almost all of the topics related to protein and dealt with structure and function of macromolecules, complexes, and protein folding.

Second period analysis

There were four topic clusters and one exclusive topic in the second period (Additional file 1: Appendix 2). In the second period (2001–2005), studies about genetics and genomics were actively conducted, and protein-related topics were diversified into subfields such as proteomics. In addition, mathematical biology and computational biology–related topics were maintained in this period.

Topics 1, 2, 5, 7, and 11 included DNA mechanisms, molecular structure, genetics, genomics, and diseases caused by DNA or a genome such as Down syndrome, DNA transposable elements, and ribonucleases. Topics 0, 3, 14, and 16 were mainly about proteomics, specifically focusing on protein structure. Topics 12, 18, and 19 addressed biotechnology, molecular modeling, and structure. Topics 8 and 9 focused on mathematical biology and computational biology. Topic 4 exclusively contained enzymology-related phrases such as "enzyme activators" and "oxygen." Enzymology-related topics were less common compared with the first period.

The second period mainly focused on gene-related topics. Topic 5 had the highest probabilistic distribution among top-ranked authors such as Gregory A. Petsko, L. Aravind, Eugene V. Koonin, Mark Gerstein, and Laurence D. Hurst, who were interested in genomics and biomedical engineering. Those authors published papers in Genome Biology, which covered subject matters related to genomics and post-genomics. Similar to the first period, protein-related research was a major topic in the second period. Top-ranked authors in this topic included Ruedi Aebersold, Peter Roepstorff, Pier Giorgio Righetti, Jean-Charles Sanchez, and Peter R. Jungblut. These authors were pioneers of proteomics, and their papers were published in the Journal of Molecular Biology and Proteomics.


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This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added. Grammar and word used were updated to make the text easier to read.