Analysis of the association between bioinformatics and biotechnological development.
Categories and Subject Descriptors
K.6 [Management of Computing and Information Systems]: J.3 [Life and Medical Sciences]: Medical Information Systems
BioInformatics and biotechnology Growth, Information Computing
Keywords: Computational techniques, Bioinformatics growth
Research on technological applications including computational applications in biology has profound impact on the knowledge frontiers. In terms of knowledge, both ontological and epistemological dimensions have changed from the distinction between science and technology to the convergence of science and technology. From the knowledge ontological perspective, biotechnology management and organizational research have focused on three aspects of knowledge. One focuses on the codified explicit knowledge, the second emphasises the formation of tacit knowledge and the third combines the two. These perspectives directly or indirectly represent the ontogenetic aspect of the organization (Knudsen, 1995). This refers to the transformation of single organizations through learning for growth (Penrose, 1960). In contrast, the phylogenetic phenomenon refers to the firm's internal and external knowledge.
Theoretically, it seems reasonable to assume that organizations, as systems, have a repertoire of internal and external knowledge that may be capitalized on through access and decision structures (March & Olsen, 1989; Marchand, 2000). Empirically, there is little understanding of how such interactions between the internal and external repertoires of know ledge of different kinds evolve. In particular, the relationship between bioinformatics as resource tools and biotechnological knowledge as outcome has been intuitively understood and empirically explored. However, the reverse between theoretical and platform biotechnologies may drive bioinformatics. Given the rationale that bioinformatics is both the antecedent to biotechnology and a consequence of it, it is essential that we understand how the activities related to biotechnology may drive activities in bioinformatics.
In the context of this question, the purpose of this paper is to explore the biotechnological developments driving bioinformatics knowledge by means of comparison between biotechnology industries in the most active and emerging countries. The resultant findings will explain the technological trajectories and potential capabilities of the national organizations in biotechnology. It is known that biotechnological systems are being emulated across many countries (Datamonitor, 2004), but it is not known whether there is any relationship between biotechnology and subsequent attempts to enhance bioinformatics.
Since biotechnology is a different phenomenon from conventional technologies, its antecedents and consequences differ. In an innovative system framework, the concepts and assumptions need to be framed in the context of biotechnology as the antecedent and bioinformatics as the consequential dimension. Biotechnology is a knowledge-based rather than axiom-based practice. In a process of discoveries, for instance, the emergence of a new entity is compared with the similar sequences of existing knowledge. If they are similar, their functions are deemed similar. Hence, unlike the axiom-based approach to eliciting knowledge, existing knowledge is applied to a new entity in biotechnology (Baker et al., 1998). This implies that the existing knowledge base confronts the new abstract knowledge.
Sometimes, bioinformatics and computational technology are implied or inferred to be interchangeable. However, at their meta-level, they are different. In comparison, bioinformatics refers to the creation and advancement of algorithm, computational and statistical techniques and theories. They help to solve practical problems related to the management and analysis of biological data. On the other hand, computational technology refers to investigations based on hypotheses-oriented research. It uses computers to analyse experimental or simulated data, with the aim of discovering new technologies. Considered separately, bioinformatics is problem driven, while bio-computational technology is hypothesis driven. Furthermore, bio-computational technology may be seen as top-down and bioinformatics as bottom-up.
Although they seem distinctive, they nevertheless have a common feature in their use of mathematical tools to derive information from the data resulting from high-throughput biological techniques such as the genome sequence. Another common thread is the study of gene regulation using data from micro-arrays. In the current study, bio-computing constitutes a component of biotechnology for several reasons. First, the combination of the bottom-up and top-down technologies represents a complete set of knowledge and skills. Second, both use computing and mathematical tools. Third, both add to efficiencies in problem solving and discovery making. Fourth, both are usually seen in similar firms. A bioinformatics firm is more likely to be expert in bio-computing and less likely to be either alone. Hence, the knowledge dimensions outlined in this context represent the holistic view of a learning system.
This convergence is captured by Berman et al. (2000) who state that "bioinformatics is the field of science in which, biology, computer science, and informational technology merge into a single discipline. There are three important sub-disciplines within bioinformatics: the development of new algorithms and statistics with which to assess relationships among members of large data sets; the analysis and interpretation of various types of data including nucleotide and amino acid sequences, protein domains, and protein structures; and the development and implementation of tools that enable efficient access and management of different types of information" (p.28).
Following this definition, the literature on innovation at the macro level refers to the national innovation system (Freeman & Perez, 1988; Lundvall, 1988; Nelson, 1993). The technological-oriented literature refers to innovation systems and technological innovation systems (Carlsson, 2000). Another dimension of this stream refers to the sector's innovation system (Malerba, 2004). The current focus is on the biotechnology innovation system as well as national level analysis. Therefore, it is a combination of the national and technological system. An innovation system has three core elements: (i) technology (knowledge), (ii) relevant actors' interaction, and (iii) institutional structures that shape the technology (Malerba, 2004).
(i) Knowledge and technology in biotechnology are used interchangeably because biotechnology is both science and its application. As a result, the boundaries of science have shifted downwards to application, and the industrial boundaries have shifted upwards to the laboratory. Hence, explicit knowledge such as codified and documented knowledge in repertoires in data banks is just one dimension. The other dimension is the tacit knowledge that resides in the application of knowledge and in the experience. The tacit dimension can be individual as well as collective. Since a collective dimension is embedded in interaction, the actors' interaction is an essential element of an innovation system.
(ii) Biotechnologies and bioinformatics are knowledge-based phenomena shaped by social interaction and interactive learning (Lundvall, 1988). This interaction among actors provides mechanisms for the interplay between the four dimensions of knowledge (Johnson et al., 2002; Lundvall, 1998): know-what (data/facts), know-why (principles), know-how (knowing-by-doing), and know-who (complementary knowledge owners) (Johnson et al., 2002). Some literature focuses on individual level teaching (Grant, 1996). Other literature focuses on organizational learning because individual knowledge shapes values, not because of its explicit dimension but because of its socialization (Nonaka & Takeuchi, 1995). By means of their interaction in a social system, actors affect the system and, in turn, the system affects them (Giddens, 1984). In this sense, the interaction and decision-making are shaped by access structures and the decision structure (common mechanisms) in the system (March & Olsen, 1989). This entails the co-evolution between the knowledge and interaction in an institutional structure (Nelson, 1994).
(iii) The literature on institutions in the context of an innovation system has three dimensions. It subsumes both formal and informal institutions, it includes both the rules of the game and the players in the game and it incorporates the constraining roles of the institutions and their empowering role. Hence, institutions are both antecedents as well as consequences of the interaction. The institutional structure provides a framework of incentives that shape the interaction between the knowledge sources of the four dimensions of knowledge identified in the above discussion on knowledge and technology. Therefore, institutions provide conditions for the complementary resources. Without such conditions, the transaction of knowledge and values are difficult because of the bounded limitations of the system (Simon, 1959). Consequently, such limitations prevent the perfection of knowledge. Perfection needs continuous feed backward and forward (Van Den Bosch et al., 1999). Institutions facilitate such learning for exploration and exploitation (March, 1991).
Assuming that national level institutional systems foster their compatible technological systems (Casper, 2000), they are forward linked to the emerging bioinformatics. This entails the co-evolution and interdependence of the two technologies (Nelson et al., 2004). Putting them together, we have a co-evolutionary innovation system (Nelson, 2003) The evolution of the systems in this paper reveals the dynamics of the relationships between the antecedent to the technological systems, such as therapeutic biotechnology, and the consequences of the technological systems such as bioinformatics organizations. The antecedent institutions are the firms that develop the main biotechnology. In prior studies, such organizations have intuitively been assumed to be the result of bioinformatics. In the current setting, the consequential organizations are bioinformatics that are developed because of the existence of the biotechnology firms. The antecedent technologies may be physical and social technologies (Nelson & Sampat, 2001). The consequential institutional systems refer to the emergent development in knowledge-related activities. The possibilities of molecular and atomic manipulation and prefabrication in biosciences are being acknowledged and used. It is generally accepted by both writers and scientists that medicine and biosciences are at the forefront, as two of the greatest beneficiaries of bioinformatics, and it is also widely acknowledged that the knowledge in repertoires such as informatics and bio-computational technologies are the beneficiaries of the main biotechnological innovations. Since the existence of one depends on the other, the legitimacy of one is the result of the other.
Thus, antecedents, such as therapeutics biotechnologies, are defined as "the art and science of building complex[es], practical devices with atomic precision" (Crandall, 1996), their consequence being bioinformatics. Given the relationship between biotechnology as the proposed antecedent and bioinformatics as the outcome, firms within national borders represent a set of relationships that are integrated upwards to a national system of innovation.
This, in turn, reflects the institutional interaction for knowledge generation, technological capabilities and innovative outcome. However, the focus is on the structure of the system; in other words, how biotechnological firms interact with bioinformatics technology firms.
Bioinformatics technology and Biomedia are considered the "technical contextualization of biological components and processes" (Thacker, 2003), which are obviously enabled by the information technology fields such as bioinformatics, system biology, bio-computing and nanomedicines (Milburn, 2005). However, it is not technology alone that shapes demand; users and demand shape the development of technology. Hence, the development of bioinformatics is a response of firms to biotechnology-related demands. Thus, with biotechnology as the antecedent and bioinformatics as the consequential systems, the exploratory questions are: (a) what is the relationship between biotechnology firms and bioinformatics firms? and (b) how do biotechnology firms influence the development of bioinformatics firms? This is likely to explain biotechnological innovation systems affecting bioinformatics activities.
Units of Analysis: Innovation systems are classified into the hierarchy of national systems, technological systems and organizational systems (Carlsson, 2000). These emphasize a specific relevant perspective. In this research, the emphasis is on technological systems, and the domain of the focus is alliances. Therefore, the logical process is to use the relational constructs, which can best be exploited on the basis of qualitative announcements on inter-firm deals. In the current study, the unit of analysis is each announcement of a firm.
In coding the qualitative data in the public domain (announcements on alliances) this study attempts to include all dedicated biotechnology firms (DBFs) and all the alliances systematically captured in a single database. This database is large in terms of the number of observations and in terms of time span (1994-2004). The data source is the large database of a specialist firm managing public announcements. Some empirical studies on biotechnology and pharmaceutical relationships have used this database. The qualitative information is generally accepted in organizational and management research. For instance, Kelm et al. (1995) studied qualitative announcements to gauge capital markets' signals. In biopharmaceuticals research, Rothaermel (2001) used BioScan's database to code alliances between biotechnology firms and incumbent pharmaceutical corporations. In biotechnology firms, Powell et al. (1996) used a similar process to examine the alliance between a university and DBFs.
In this study BioSpace's database was used. BioSpace Inc. is an organization that systematically documents qualitative announcements on DBFs. This database documents each press release related to biotechnology activity. On average, releases vary from 5 to 7 lines. Accordingly, following the framework in this context, each announcement was analysed for the period of 12 years.
Data Collection: The data were coded according to the concepts and variables. Following biotechnology as the explanatory variable, multiple concepts supporting it were upward integrated. On the response variable side, biocomputing, bioinformatics and enabling (automatedsystems) were used to integrate and construct relationships. The population of firms included were almost all relevant and active actors in the industry. Together, they were traced for the links in different press releases and public announcements. Hence, each piece of information was part of the anlaysis and coding for the dependent and independent variables.
Independent Variables: The independent variable was coded under biotechnology and integrated using several concepts. If the concept was observed in a piece of information, it was coded 1. If the concept was absent, it was coded 0. This implies that the independent variables were dummies, comprising various success and failure scenarios. Table 1 captures the description and the predicted relationship between the variables. Because the independent variables are binary variables, it may be useful to identity the controlled variables.
Controlled Variables: Countries, years and technologies were coded based on their presence or absence. Each country represented one variable, so there were 12 variables because there were 12 years (1994 to 2005). Because there is sometimes an overlap, and a time lag between an alliance and subsequent innovations, it can be useful to capture the longitudinal aspect through the fix-affect method. In other words, controlling the year as a dummy represents the fix-affect technique, hence fulfilling the fix-affect assumption in the longitudinal analysis.
The formal logistic regression was carried out to predict the likelihood of an event occurring in response to another event. In other words, the predictor is the independent variable, and the predicted is the dependent variable. The logit model captures the probability of variances due to the change in the independent variable. In the current study, the predictors are the interactions among actors for biotechnologies, and the response variable is the development in bioinformatics. Formally, the logit model is captured as:
Odds =[pi]/(1-[pi]) = [e.sup.[alpha] + [beta] [??] Xi + ... + [beta] [kappa] X k]/1 + [e.sup.[alpha] + [beta] [??] Xi + ... + [beta] [kappa] X k]
The first element of the model defines the probability of an activity ([pi]) occurring: P (Yi = 1); and the probability of it not occurring: P (Yi = 0). The logits are log functions of the predictor variables (Pisano, 1989).
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
Running logistic regression provides two types of outcome. The first refers to the odd ratios. The odds ratios and the probability estimation ([??]) imply that the success of an innovation activity is the function of the odds: [??] = Odds/ (Odds+1). The second type refers to the logistic regression coefficient. The coefficient is the slope rate at which the predicted log odds ratio increases (or if decreases) with each successive unit of X.
This section consists of two tables. Table 1A and 1B represent the descriptive statistics. In these tables showing correlation coefficients, the countries are independent variables, and bioinformatics are dependent variables. The independent variables represent the biotechnology firms active in therapeutics and platform technologies while the dependent variables represent the activities related to the bioinformatics technologies. The variables representing years are not included in the table for logistic constraints. Table 2 shows odd ratios of the logistic regression and the coefficients of the logit model. The results outcome shows that the multicolinearity assumption was not violated. Such assumptions are violated if the correlation coefficient is > 0.5. In the current results, the correlation coefficients are at the < 0.5 level.
Table 2 shows the hypotheses based results. The exploratory study proposed that all active countries in biotechnology are likely to be predictors of bioinformatics. The results are mixed. Table 3 further refines the findings at the national level. Three dimensions of results show that (a) some countries' biotechnology positively influences bioinformatics, (b) other countries' biotechnology negatively influences bioinformatics, and (c) others are indifferent. These results are discussed in the next section.
On the basis of the results of the national level activities in bioinformatics shown in table 3, it may be concluded that the national bioinformatics system is divided into three groups: those showing positive significance logistic regression coefficients, those showing negative significance logistic regression coefficients and those showing insignificant logistic regression coefficients. The first group shows that biotechnology, such as therapeutics and platform technologies, is driving the development of bioinformatics.
There are six countries in this group, including Taiwan, the US, the UK, and Germany. According to one explanation, these countries are developing one technology in coordination with the other one. Put another way, there is some kind of co-evolution between the basic biotechnologies and bioinformatics (Nelson et al., 2004). That is to say, the activities related to knowledge generation and exploitation stay within national borders. For instance, Japanese companies tend to conduct biotechnology research not only within Japan's borders but also within large pharmaceutical firms (Kneller, 2003).
The second group of countries show negative significance. This group consists of eleven countries, as seen in table 3. One possible explanation is that these countries are more active in biotechnology as therapeutics and platform technologies on the one hand and bioinformatics on the other hand. This implies that as one technology level tends to increase, the other receives less attention. This shows that these countries' technologies are not co-evolving. At the national level of analysis, this may imply that these countries outsource or acquire one set of technologies or the other. In most of India's biopharmaceuticals, for instance, the focus is on downstream marketing and sales of generics rather than upstream spending on discoveries (Ernst & Young, 2007).
In the third group, there are twelve countries that are more or less involved in both types of technologies, but these are developing in unrelated ways. The relationship between the two is insignificant and therefore it is not likely to predict that one therapeutics or platform will drive the activities related to other types of technologies.
Put together, it seems that some countries are developing both technologies in a parallel pattern, and other countries are developing one at the cost of the other. Yet, other countries are developing technology at different paces. This leads to several concluding comments.
This empirical investigation started with the exploratory question: how do biotechnologies shape bioinformatics within national boundaries? The data were gathered on biotechnology firms in several countries that are active in the biotechnology sector. Then the data were integrated upwards into the national level of analysis. In logistic regressions, the biotechnological activities were regressed on bioinformatics activities. Since these data were drawn on the links and not on the nodes in the interaction, they reflect the influence and confluence of one sector with the other. Accordingly, the results show three patterns: convergent groups, divergent groups and indifferent groups.
The first group confirms the literature that technologies are inter-dependent (Niosi & Bellon, 1996). One technology needs the other complementary technologies (knowledge). Therapeutics needs the knowledge base, and the knowledge base requires repertoires. This kind of knowledge resides in information systems and other enabling technologies that constitute various dimensions of bioinformatics (Baker et al., 1998). The second group the innovation system, show conflicting findings by suggesting that one technology may develop even if the other is compromised. This, perhaps, is due to the comparative advantages of the institutional environment (Casper, 2000). The third group does not show any significance. This group's influence is indifferent in the current framework and propositions. However, the introduction of a new context and variables may result in different outcomes from the current ones.
Received 14 March 2007; Revised and accepted 21 July 2007
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Abdullah Abdulaziz Al-Tameem
College of Computer and Information Sciences
Al-Imam Muhammad Ibn Saud Islamic University
P. O. Box 50996. Riyadh 11533
Table 1A Correlation Coefficient 1 2 3 4 USA 1 UK -.292 ** 1 GR -.200 ** -.034 ** 1 FR -.135 ** -.020 * .016 * 1 DN -.096 ** .015 .012 -.007 CN -.148 ** -.030 ** -.032 ** -.014 JP -.074 ** -.006 -.026 ** -.017 * BE -.064 ** -.004 .020 * .003 SW -.125 ** -.024 ** .024 ** -.016 SD -.095 ** .020 * -.014 -.013 AU -.089 ** -.007 -.020 * .004 N.Z -.013 .010 -.009 -.004 KR -.017 * -.014 -.019 * -.004 CH -.024 ** -.027 ** -.015 -.002 TW -.011 -.015 -.011 .005 IR -.056 ** .017 * -.028 ** -.014 IT -.026 ** .005 .013 -.006 SP -.030 ** .007 .014 .021 ** IN -.028 ** -.035 ** -.030 ** -.012 SA -.020 * .012 .007 -.003 SG -.017 * -.009 -.019 * -.008 ML -.027 ** .006 -.007 -.005 BZ -.006 -.005 -.001 .004 AG -.015 -.011 -.006 .031 ** MX -.028 ** -.009 -.013 .019 * RU .011 -.015 .005 .010 FN -.044 ** .003 -.006 -.005 ND -.090 ** -.016 * .005 -.001 NR -.055 ** .028 ** -.002 .000 IS -.067 ** -.018 * -.011 -.002 BioInfo .070 ** .008 .037 ** -.014 5 6 7 8 USA UK GR FR DN 1 CN -.018 * 1 JP -.023 ** -.024 ** 1 BE -.006 -.013 -.017 * 1 SW -.010 -.012 -.016 * -.011 SD .062 ** -.016 * -.017 * -.009 AU .000 -.001 .007 -.010 N.Z -.009 .000 .029 ** -.007 KR -.013 -.009 .196 ** -.005 CH -.002 -.012 .121 ** -.012 TW -.012 -.010 .172 ** .004 IR -.005 .000 -.024 ** .000 IT -.014 .010 -.008 -.006 SP .006 .000 -.012 .011 IN -.011 -.020 * -.009 -.013 SA .004 -.008 -.005 -.005 SG -.001 .007 .036 ** -.007 ML -.003 .011 .016 * -.003 BZ -.004 .008 .002 .016 * AG -.006 .002 .003 -.004 MX -.009 .011 .007 -.007 RU -.009 -.006 .003 .027 ** FN .104 ** -.012 -.001 .010 ND .011 -.014 -.020 * .066 ** NR .076 ** -.006 -.017 * .010 IS .009 -.003 -.017 * -.008 BioInfo -.034 ** -.030 ** -.012 -.006 9 10 11 12 USA UK GR FR DN CN JP BE SW 1 SD -.001 1 AU -.019 * -.002 1 N.Z -.005 -.001 .289 ** 1 KR -.003 -.013 .021 ** .041 ** CH -.010 -.014 .033 ** .057 ** TW -.004 -.011 .022 ** .049 ** IR -.019 * -.009 -.016 * -.002 IT .006 .011 -.004 -.011 SP .014 .007 -.002 -.004 IN -.001 -.015 -.008 .003 SA -.003 .016 * .067 ** .122 ** SG .015 .000 .021 ** .032 ** ML .007 -.003 .055 ** .138 ** BZ -.007 -.004 .012 -.002 AG -.001 -.005 .006 -.002 MX -.006 -.009 .005 .013 RU .003 .006 .033 ** .030 ** FN .003 .102 ** -.009 -.004 ND .016 * -.013 -.018 * -.010 NR .017 * .087 ** -.009 -.004 IS .012 .007 -.007 .013 BioInfo -.009 -.001 -.045 ** -.009 13 14 15 16 USA UK GR FR DN CN JP BE SW SD AU N.Z KR 1 CH .293 ** 1 TW .488 ** .348 ** 1 IR -.009 -.011 -.001 1 IT -.005 -.011 -.001 .021 ** SP -.005 -.006 -.005 .012 IN .024 ** .022 ** .012 -.017 * SA -.004 .041 ** -.003 .014 SG .091 ** .102 ** .106 ** -.002 ML -.002 .082 ** -.002 -.003 BZ .023 ** .020 * .027 ** -.004 AG .015 .013 .019 * -.006 MX .040 ** .034 ** .008 -.003 RU .017 * .024 ** .021 ** .004 FN -.006 -.006 -.005 .004 ND -.005 -.016 * -.007 -.013 NR -.006 -.006 -.005 .004 IS .004 .001 -.008 .016 * BioInfo -.015 -.021 ** .000 -.051 ** Table 1B Correlation Coefficient 17 18 19 20 21 IT 1 SP .055 ** 1 IN -.006 -.010 1 SA .001 -.003 .021 ** 1 SG -.005 -.004 .044 ** .023 ** ML -.004 -.001 .014 .133 ** BZ .034 ** .037 ** .024 ** -.001 AG .003 -.002 .004 .039 ** MX .000 -.004 .009 .070 ** RU .018 * -.004 .009 .070 ** FN .006 .030 ** -.010 -.003 ND .002 .004 -.011 .013 NR -.011 .030 ** -.010 -.003 IS -.013 -.006 -.003 .024 ** Bioinfo -.010 -.024 ** -.004 -.008 17 22 23 24 25 IT SP IN SA SG 1 ML .192 ** 1 BZ -.002 -.001 1 AG -.002 -.001 .184 ** 1 MX .014 .089 ** .035 ** .052 ** RU -.004 -.001 -.002 -.002 FN .014 -.001 -.002 -.002 ND -.009 -.004 -.004 .027 ** NR .014 -.001 -.002 -.002 IS -.006 .024 ** -.003 .012 Bioinfo -.010 -.009 -.004 -.015 17 26 27 28 IT SP IN SA SG ML BZ AG MX 1 RU .013 1 FN -.004 .013 1 ND .010 .003 .023 ** NR -.004 -.004 .243 ** IS .003 -.007 .003 Bioinfo -.019 * -.010 .022 ** 17 29 30 31 IT SP IN SA SG ML BZ AG MX RU FN ND 1 NR .017 * 1 IS .007 -.007 1 Bioinfo .007 -.019 * .004 1 ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). Table 2 Odd Ratios & Logist Coefficient Bioinformatics Odd Ratios Std Err Sig Constant Korea 0.56 0.21 0.13 China 0.47 0.16 0.03 Taiwan 2.08 0.74 0.04 Ireland 0.14 0.05 0.00 Italy 0.88 0.13 0.36 USA 1.70 0.11 0.00 British 1.32 0.09 0.00 German 1.65 0.13 0.00 French 0.94 0.11 0.60 Denmark 0.44 0.10 0.00 Canada 0.68 0.11 0.02 Japanese 0.95 0.10 0.61 Belgium 0.92 0.20 0.70 Switzerland 0.98 0.11 0.87 Swedish 1.22 0.21 0.25 Australia 0.22 0.07 0.00 Russia 0.62 0.29 0.31 Finland 0.16 0.16 0.07 Netherlands 1.30 0.19 0.07 Norway 0.32 0.23 0.11 Israel 1.36 0.30 0.16 New Zealand 1.58 0.80 0.36 India 0.99 0.15 0.93 South Africa 0.70 0.53 0.64 Singapore 0.73 0.38 0.55 Brazil 1.05 1.13 0.96 Mexico 0.27 0.20 0.07 N 15610 Bioinformatics Coeff. Std Err Sig Constant -0.50 17.13 0.98 Korea -0.58 0.38 0.13 China -0.76 0.35 0.03 Taiwan 0.73 0.36 0.04 Ireland -1.98 0.38 0.00 Italy -0.13 0.14 0.36 USA 0.53 0.06 0.00 British 0.28 0.07 0.00 German 0.50 0.08 0.00 French -0.06 0.12 0.60 Denmark -0.83 0.24 0.00 Canada -0.38 0.17 0.02 Japanese -0.05 0.10 0.61 Belgium -0.08 0.21 0.70 Switzerland -0.02 0.11 0.87 Swedish 0.20 0.17 0.25 Australia -1.52 0.34 0.00 Russia -0.48 0.47 0.31 Finland -1.85 1.01 0.07 Netherlands 0.27 0.15 0.07 Norway -1.15 0.72 0.11 Israel 0.31 0.22 0.16 New Zealand 0.46 0.50 0.36 India -0.01 0.15 0.93 South Africa -0.36 0.75 0.64 Singapore -0.32 0.53 0.55 Brazil 0.05 1.08 0.96 Mexico -1.31 0.72 0.07 N -2 Log Liklihood -6031 Chi-Square 281 *** Table 3 National System Level Patterns Bioinformatics Positive Negative Insignificance Constant Korea 0 China - Taiwan + Ireland - Italy 0 USA + British + German + French - Denmark - Canada - Japanese - Belgium - Switzerland - Swedish 0 Australia - Russia - Finland - Netherlands 0 Norway 0 Israel 0 New Zealand 0 India 0 South Africa 0 Singapore 0 Brazil 0 Mexico 0
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|Author:||Tameem, Abdullah Abdulaziz Al-|
|Publication:||Journal of Digital Information Management|
|Date:||Feb 1, 2008|
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