Citation algorithms for identifying research milestones driving biomedical innovation

📝 Abstract

Scientific activity plays a major role in innovation for biomedicine and healthcare. For instance, fundamental research on disease pathologies and mechanisms can generate potential targets for drug therapy. This co-evolution is punctuated by papers which provide new perspectives and open new domains. Despite the relationship between scientific discovery and biomedical advancement, identifying these research milestones that truly impact biomedical innovation can be difficult and is largely based solely on the opinions of subject matter experts. Here, we consider whether a new class of citation algorithms that identify seminal scientific works in a field, Reference Publication Year Spectroscopy (RPYS) and multi-RPYS, can identify the connections between innovation (e.g. therapeutic treatments) and the foundational research underlying them. Specifically, we assess whether the results of these analytic techniques converge with expert opinions on research milestones driving biomedical innovation in the treatment of Basal Cell Carcinoma. Our results show that these algorithms successfully identify the majority of milestone papers detailed by experts (Wong and Dlugosz 2014) thereby validating the power of these algorithms to converge on independent opinions of seminal scientific works derived by subject matter experts. These advances offer an opportunity to identify scientific activities enabling innovation in biomedicine.

💡 Analysis

Scientific activity plays a major role in innovation for biomedicine and healthcare. For instance, fundamental research on disease pathologies and mechanisms can generate potential targets for drug therapy. This co-evolution is punctuated by papers which provide new perspectives and open new domains. Despite the relationship between scientific discovery and biomedical advancement, identifying these research milestones that truly impact biomedical innovation can be difficult and is largely based solely on the opinions of subject matter experts. Here, we consider whether a new class of citation algorithms that identify seminal scientific works in a field, Reference Publication Year Spectroscopy (RPYS) and multi-RPYS, can identify the connections between innovation (e.g. therapeutic treatments) and the foundational research underlying them. Specifically, we assess whether the results of these analytic techniques converge with expert opinions on research milestones driving biomedical innovation in the treatment of Basal Cell Carcinoma. Our results show that these algorithms successfully identify the majority of milestone papers detailed by experts (Wong and Dlugosz 2014) thereby validating the power of these algorithms to converge on independent opinions of seminal scientific works derived by subject matter experts. These advances offer an opportunity to identify scientific activities enabling innovation in biomedicine.

📄 Content

Page  1  of  16                       Citation  algorithms  for  identifying  research  milestones  driving  biomedical  innovation     Jordan  A.  Cominsa,*  and  Loet  Leydesdorffb  

a  *corresponding  author;;  Center  for  Applied  Information  Science,  Virginia  Tech  Applied  Research   Corporation,  Arlington,  VA,  United  States;;  jcomins@gmail.com     b  Amsterdam  School  of  Communication  Research  (ASCoR),  University  of  Amsterdam,  PO  Box  15793,   1001  NG  Amsterdam,  The  Netherlands;;  loet@leydesdorff.net       Page  2  of  16 Abstract   Scientific  activity  plays  a  major  role  in  innovation  for  biomedicine  and  healthcare.  For  instance,   fundamental  research  on  disease  pathologies  and  mechanisms  can  generate  potential  targets   for  drug  therapy.  This  co-­evolution  is  punctuated  by  papers  which  provide  new  perspectives  and   open  new  domains.  Despite  the  relationship  between  scientific  discovery  and  biomedical   advancement,  identifying  these  research  milestones  that  truly  impact  biomedical  innovation  can   be  difficult  and  is  largely  based  solely  on  the  opinions  of  subject  matter  experts.  Here,  we   consider  whether  a  new  class  of  citation  algorithms  that  identify  seminal  scientific  works  in  a   field,  Reference  Publication  Year  Spectroscopy  (RPYS)  and  multi-­RPYS,  can  identify  the   connections  between  innovation  (e.g.,  therapeutic  treatments)  and  the  foundational  research   underlying  them.  Specifically,  we  assess  whether  the  results  of  these  analytic  techniques   converge  with  expert  opinions  on  research  milestones  driving  biomedical  innovation  in  the   treatment  of  Basal  Cell  Carcinoma.  Our  results  show  that  these  algorithms  successfully  identify   the  majority  of  milestone  papers  detailed  by  experts  (Wong  and  Dlugosz  2014)  –  thereby   validating  the  power  of  these  algorithms  to  converge  on  independent  opinions  of  seminal   scientific  works  derived  by  subject  matter  experts.  These  advances  offer  an  opportunity  to   identify  scientific  activities  enabling  innovation  in  biomedicine.           Keywords:  Reference  Publication  Year  Spectroscopy;;  Citation  Analysis;;  Algorithmic   Historiography;;  Bibliometrics             Page  3  of  16 Introduction   Biomedical  innovation  is  guided,  at  least  in  part,  by  historical  influences  (Agarwal  and   Searls,  2009).  In  some  cases,  biomedical  innovation  is  guided  by  key  scientific  discoveries,   referred  to  here  as  research  milestones  treatments  (Wong  and  Dlugosz,  2014).  Nelson,   Buterbaugh,  Perl,  &  Gelijns (2011) distinguish analytically among three enabling forces in medical innovation: advances of scientific understanding of diseases, learning in clinical practices, and advances in technological capabilities (which often originate outside of medicine) for the development of novel modalities of diagnosis and treatment. However,   interactions  between  supply-­side  factors  and  demand  originating  from  diseases  can  be   expected  to  shape  co-­evolutions  along  trajectories  enabled  by  technological  capabilities   (Petersen,  Rotolo,  &  Leydesdorff,  2016).  From  the  historiographic  perspective  in  innovation   studies,  however,  the  innovation  itself  is  focal  (Von  Hippel,  1988);;  but  the  scientific  knowledge   base  of  innovations  can  be  excavated  from  the  literature  (Garfield,  Sher,  &  Torpie,  1964).   Medical  innovations  are  sometimes  triggered  by  breakthroughs  and  new  developments  on  the   supply  side  (Leydesdorff  &  Rafols,  2011;;  Mina,  Ramlogan,  Tampubolon,  &  Metcalfe,  2007).   Connecting  major  advances  in  medical  treatments  with  earlier  research  milestones  is   considered  essential  for  public  appreciation  of  the  role  of  basic  research  discoveries  in  major   health  advances  (Williams  et  al.,  2015)  and  offers  an  opportunity  to  more  fully  consider  the   factors  leading  to  new  drugs  (Nelson,  Buterbaugh,  Perl,  &  Gelijns,  2011;;  Swanson,  1990).   Despite  the  value  of  the  translation  of  scientific  activities  into  medical  innovations  (Mogoutov,   Cambrosio,  Keating,  &  Mustar,  2008),  current  approaches  for  retrieving  research  milestones   require  considerable  time  and  commitment  of  subject  matter  experts.  In  this  brief   communication,  we  assess  the  utility  of  using  computational  approaches  to  identify  research   milestones,  which  may  allow  for  broader  engagement  of  scientific  and  non-­scientific   communities  with  a  field’s  intellectual  history.       Page  4  of  16   Eugene Garfield, who first created the Science Citation Index, found the  development  of   citation  algor

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