Albert-László Barabási: Difference between revisions

From Wikipedia, the free encyclopedia
Browse history interactively
← Previous editNext edit →
Content deleted Content added
VisualWikitext
m task, replaced: N Engl J Med. → N Engl J Med
Citation bot (talk | contribs)
Bots
4,890,960 edits
Add: doi-access, pmc, year, doi, pmid, s2cid, bibcode, arxiv, authors 1-1. Removed proxy/dead URL that duplicated identifier. Removed parameters. Some additions/deletions were parameter name changes. | Use this bot. Report bugs. | Suggested by Headbomb | Linked from Wikipedia:WikiProject_Academic_Journals/Journals_cited_by_Wikipedia/Sandbox | #UCB_webform_linked 3/41
Line 60: Line 60:


=== Scale-Free Networks ===
=== Scale-Free Networks ===
His biggest role has been the discovery of the ''[[scale-free network]]s''. He reported the scale-free nature of the WWW in 1999<ref>{{Cite journal |last=Albert |first=Réka |last2=Jeong |first2=Hawoong |last3=Barabási |first3=Albert-László |date=September 1999 |title=Diameter of the World-Wide Web |url=http://www.nature.com/articles/43601 |journal=Nature |language=en |volume=401 |issue=6749 |pages=130–131 |doi=10.1038/43601 |issn=0028-0836}}</ref> and the same year, in a Science paper with Réka [[Réka Albert|Albert]], he proposed the [[Barabási–Albert model]], predicting that growth and [[preferential attachment]] are jointly responsible for the emergence of the scale-free property in real networks. According to the review of one of Barabási's books, preferential attachment can be described as follows:<blockquote>"Barabási has found that the websites that form the network (of the WWW) have certain mathematical properties. The conditions for these properties to occur are threefold. The first is that the network has to be expanding, growing. This precondition of growth is very important as the idea of emergence comes with it. It is constantly evolving and adapting. That condition exists markedly with the world wide web. The second is the condition of '''preferential attachment''', that is, nodes (websites) will wish to link themselves to hubs (websites) with the most connections. The third condition is what is termed competitive fitness which in network terms means its rate of attraction."<ref>[http://www.sociopranos.com/bookreviewlinked.htm Profile] {{webarchive|url=https://web.archive.org/web/20050309024313/http://www.sociopranos.com/bookreviewlinked.htm |date=March 9, 2005 }}, sociopranos.com; accessed 10 January 2016.</ref></blockquote>
His biggest role has been the discovery of the ''[[scale-free network]]s''. He reported the scale-free nature of the WWW in 1999<ref>{{Cite journal |last1=Albert |first1=Réka |last2=Jeong |first2=Hawoong |last3=Barabási |first3=Albert-László |date=September 1999 |title=Diameter of the World-Wide Web |url=http://www.nature.com/articles/43601 |journal=Nature |language=en |volume=401 |issue=6749 |pages=130–131 |doi=10.1038/43601 |arxiv=cond-mat/9907038 |bibcode=1999Natur.401..130A |s2cid=4419938 |issn=0028-0836}}</ref> and the same year, in a Science paper with Réka [[Réka Albert|Albert]], he proposed the [[Barabási–Albert model]], predicting that growth and [[preferential attachment]] are jointly responsible for the emergence of the scale-free property in real networks. According to the review of one of Barabási's books, preferential attachment can be described as follows:<blockquote>"Barabási has found that the websites that form the network (of the WWW) have certain mathematical properties. The conditions for these properties to occur are threefold. The first is that the network has to be expanding, growing. This precondition of growth is very important as the idea of emergence comes with it. It is constantly evolving and adapting. That condition exists markedly with the world wide web. The second is the condition of '''preferential attachment''', that is, nodes (websites) will wish to link themselves to hubs (websites) with the most connections. The third condition is what is termed competitive fitness which in network terms means its rate of attraction."<ref>[http://www.sociopranos.com/bookreviewlinked.htm Profile] {{webarchive|url=https://web.archive.org/web/20050309024313/http://www.sociopranos.com/bookreviewlinked.htm |date=March 9, 2005 }}, sociopranos.com; accessed 10 January 2016.</ref></blockquote>


He subsequently showed that the scale-free property emerges in biological systems, namely in [[metabolic networks]]<ref>{{Cite journal |last=Jeong |first=H. |last2=Tombor |first2=B. |last3=Albert |first3=R. |last4=Oltvai |first4=Z. N. |last5=Barabási |first5=A.-L. |date=October 2000 |title=The large-scale organization of metabolic networks |url=http://www.nature.com/articles/35036627 |journal=Nature |language=en |volume=407 |issue=6804 |pages=651–654 |doi=10.1038/35036627 |issn=0028-0836}}</ref> and [[protein–protein interaction]]<ref>{{Cite journal |last=Jeong |first=H. |last2=Mason |first2=S. P. |last3=Barabási |first3=A.-L. |last4=Oltvai |first4=Z. N. |date=May 2001 |title=Lethality and centrality in protein networks |url=http://www.nature.com/articles/35075138 |journal=Nature |language=en |volume=411 |issue=6833 |pages=41–42 |doi=10.1038/35075138 |issn=0028-0836}}</ref> networks. [[Science (journal)|''Science'']] celebrated the ten-year anniversary of Barabási’s 1999 discovery by devoting a special issue to Complex Systems and Networks in 2009.<ref>{{Cite journal |last=Barabasi |first=Albert-Laszlo |date=2009 |title=Scale-Free Networks: A Decade and Beyond |url=https://www.science.org/doi/10.1126/science.1173299 |journal=Science |volume=325 |issue=5939 |pages=412–413}}</ref><ref>{{Cite journal |last=Jasny |first=Barbara |date=2009 |title=Connections |url=https://www.science.org/doi/10.1126/science.325_405 |journal=Science |volume=325 |issue=5939 |pages=405}}</ref>
He subsequently showed that the scale-free property emerges in biological systems, namely in [[metabolic networks]]<ref>{{Cite journal |last1=Jeong |first1=H. |last2=Tombor |first2=B. |last3=Albert |first3=R. |last4=Oltvai |first4=Z. N. |last5=Barabási |first5=A.-L. |date=October 2000 |title=The large-scale organization of metabolic networks |url=http://www.nature.com/articles/35036627 |journal=Nature |language=en |volume=407 |issue=6804 |pages=651–654 |doi=10.1038/35036627 |arxiv=cond-mat/0010278 |bibcode=2000Natur.407..651J |s2cid=4426931 |issn=0028-0836}}</ref> and [[protein–protein interaction]]<ref>{{Cite journal |last1=Jeong |first1=H. |last2=Mason |first2=S. P. |last3=Barabási |first3=A.-L. |last4=Oltvai |first4=Z. N. |date=May 2001 |title=Lethality and centrality in protein networks |url=http://www.nature.com/articles/35075138 |journal=Nature |language=en |volume=411 |issue=6833 |pages=41–42 |doi=10.1038/35075138 |pmid=11333967 |arxiv=cond-mat/0105306 |bibcode=2001Natur.411...41J |s2cid=258942 |issn=0028-0836}}</ref> networks. [[Science (journal)|''Science'']] celebrated the ten-year anniversary of Barabási’s 1999 discovery by devoting a special issue to Complex Systems and Networks in 2009.<ref>{{Cite journal |last=Barabasi |first=Albert-Laszlo |date=2009 |title=Scale-Free Networks: A Decade and Beyond |url=https://www.science.org/doi/10.1126/science.1173299 |journal=Science |volume=325 |issue=5939 |pages=412–413|doi=10.1126/science.1173299 |pmid=19628854 |bibcode=2009Sci...325..412B |s2cid=43910070 }}</ref><ref>{{Cite journal |last=Jasny |first=Barbara |date=2009 |title=Connections |url=https://www.science.org/doi/10.1126/science.325_405 |journal=Science |volume=325 |issue=5939 |pages=405|doi=10.1126/science.325_405 |pmid=19628849 |bibcode=2009Sci...325..405J }}</ref>


=== Network Robustness ===
=== Network Robustness ===
In a 2001 paper with [[Réka Albert]] and [[Hawoong Jeong]] he demonstrated the [[Achilles' heel]] property of scale-free networks, showing that such networks are robust to random failures but fragile to attacks.<ref>{{cite book |last1=Barabási |first1=Albert-László |title=Network science |date=July 21, 2016 |location=Cambridge, United Kingdom |isbn=9781107076266}}</ref> Specifically, they showed that networks can easily survive the random failure of a very large number of nodes, i.e. the failure threshold is high. In contrast, the same networks collapse if they are attacked, by removing the biggest hubs first. The threshold characterizing the breakdown of a network under random failures was subsequently linked<ref>{{Cite journal |last=Cohen, Reuven; Erez, Keren; ben-Avraham, Daniel; Havlin, Shlomo |date=2000 |title="Resilience of the Internet to Random Breakdowns". |url=https://arxiv.org/abs/cond-mat/0007048 |journal=Physical Review Letters |volume=85 |issue=21 |pages=4626–4628}}</ref> it to the second moment of the [[degree distribution]]. The threshold converges to zero for large networks, indicating that large networks can easily survive the failure of a very large fraction of their nodes. The calculations also showed that robustness to random failures is not limited to scale-free networks, but it is a general property of most real networks with a wide range of node degrees.
In a 2001 paper with [[Réka Albert]] and [[Hawoong Jeong]] he demonstrated the [[Achilles' heel]] property of scale-free networks, showing that such networks are robust to random failures but fragile to attacks.<ref>{{cite book |last1=Barabási |first1=Albert-László |title=Network science |date=July 21, 2016 |location=Cambridge, United Kingdom |isbn=9781107076266}}</ref> Specifically, they showed that networks can easily survive the random failure of a very large number of nodes, i.e. the failure threshold is high. In contrast, the same networks collapse if they are attacked, by removing the biggest hubs first. The threshold characterizing the breakdown of a network under random failures was subsequently linked<ref>{{Cite journal |last=Cohen, Reuven; Erez, Keren; ben-Avraham, Daniel; Havlin, Shlomo |date=2000 |title="Resilience of the Internet to Random Breakdowns". |url=https://arxiv.org/abs/cond-mat/0007048 |journal=Physical Review Letters |volume=85 |issue=21 |pages=4626–4628|doi=10.1103/PhysRevLett.85.4626 |pmid=11082612 |arxiv=cond-mat/0007048 |bibcode=2000PhRvL..85.4626C |s2cid=15372152 }}</ref> it to the second moment of the [[degree distribution]]. The threshold converges to zero for large networks, indicating that large networks can easily survive the failure of a very large fraction of their nodes. The calculations also showed that robustness to random failures is not limited to scale-free networks, but it is a general property of most real networks with a wide range of node degrees.


=== Network Medicine ===
=== Network Medicine ===
Barabási is one of the founders of [[network medicine]], a term he coined in a scientific article entiled "Network Medicine – From Obesity to the "Diseasome", published in The New England Journal of Medicine, in 2007.<ref>{{Cite journal |last=Barabási AL |title=Network medicine--from obesity to the "diseasome" |journal=N Engl J Med |volume=357 |issue=4 |pages=404–407}}</ref> His work introduced the concept of diseasome, or disease network,<ref>{{Cite journal |last=Goh, K. I., Cusick, M. E., Valle, D., Childs, B., Vidal, M., & Barabási, A. L. |date=2007 |title=The human disease network |journal=Proceedings of the National Academy of Sciences |volume=104 |issue=21 |pages=8685–8690}}</ref> showing how diseases link to each other through shared genes, capturing their common genetic roots. He subsequently pioneered the use of large patient data to explore disease comorbidity, linking it to molecular network data.<ref>{{cite journal |last1=Barabási |first1=Albert-László |last2=Gulbahce |first2=Natali |last3=Loscalzo |first3=Joseph |title=Network medicine: a network-based approach to human disease |journal=Nature Reviews Genetics |date=January 2011 |volume=12 |issue=1 |pages=56–68 |doi=10.1038/nrg2918|pmid=21164525 |pmc=3140052 }}</ref> He discovered that genes associated with the same disease tend to be located in the same network neighborhood,<ref>{{Cite journal |last=Menche J, Sharma A, Kitsak M, Ghiassian SD, Vidal M, Loscalzo J, Barabási AL. |date=2005 |title=Uncovering disease-disease relationships through the incomplete interactome. |journal=Science |volume=347 |issue=6224 |pages=1257601}}</ref> introducing the concept of disease module, currently used to aid [[drug discovery]], [[drug design]], and the development of [[biomarker]]s for disease detection, as he outlined in 2012 in a [[TEDMED]] talk.<ref>{{Citation |title=Do your proteins have their own social network? |url=https://www.youtube.com/watch?v=10oQMHadGos |language=en |access-date=2022-11-01}}</ref> His work has inspired the founding of the [https://www.brighamandwomens.org/research/departments/channing-division-of-network-medicine/overview Channing Division of Network Medicine at Harvard Medical School] and the [https://www.network-medicine.org/ Network Medicine Institute and Global Alliance], representing 33 leading universities and institutions around the world committed to advancing the field. His work in network medicine has led to multiple experimentally falsifiable predictions, helping identify experimentally validated novel pathways in asthma,<ref>{{Cite journal |last=Sharma |first=Amitabh |last2=Menche |first2=Jörg |last3=Huang |first3=C. Chris |last4=Ort |first4=Tatiana |last5=Zhou |first5=Xiaobo |last6=Kitsak |first6=Maksim |last7=Sahni |first7=Nidhi |last8=Thibault |first8=Derek |last9=Voung |first9=Linh |last10=Guo |first10=Feng |last11=Ghiassian |first11=Susan Dina |last12=Gulbahce |first12=Natali |last13=Baribaud |first13=Frédéric |last14=Tocker |first14=Joel |last15=Dobrin |first15=Radu |date=2015-06-01 |title=A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma |url=https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddv001 |journal=Human Molecular Genetics |language=en |volume=24 |issue=11 |pages=3005–3020 |doi=10.1093/hmg/ddv001 |issn=1460-2083 |pmc=4447811 |pmid=25586491}}</ref> predicting novel mechanism of action for rosmarinic acid,<ref>{{Cite journal |last=do Valle |first=Italo F. |last2=Roweth |first2=Harvey G. |last3=Malloy |first3=Michael W. |last4=Moco |first4=Sofia |last5=Barron |first5=Denis |last6=Battinelli |first6=Elisabeth |last7=Loscalzo |first7=Joseph |last8=Barabási |first8=Albert-László |date=March 2021 |title=Network medicine framework shows that proximity of polyphenol targets and disease proteins predicts therapeutic effects of polyphenols |url=http://www.nature.com/articles/s43016-021-00243-7 |journal=Nature Food |language=en |volume=2 |issue=3 |pages=143–155 |doi=10.1038/s43016-021-00243-7 |issn=2662-1355}}</ref> and predicting novel therapeutic functions of existing drugs, and testing them directly in patients (drug repurposing).<ref>{{Cite journal |last=Cheng |first=Feixiong |last2=Desai |first2=Rishi J. |last3=Handy |first3=Diane E. |last4=Wang |first4=Ruisheng |last5=Schneeweiss |first5=Sebastian |last6=Barabási |first6=Albert-László |last7=Loscalzo |first7=Joseph |date=December 2018 |title=Network-based approach to prediction and population-based validation of in silico drug repurposing |url=http://www.nature.com/articles/s41467-018-05116-5 |journal=Nature Communications |language=en |volume=9 |issue=1 |pages=2691 |doi=10.1038/s41467-018-05116-5 |issn=2041-1723 |pmc=6043492 |pmid=30002366}}</ref> The products of network medicine are already on the market, helping tens of thousands of rheumatoid arthritis patients decide if they respond to anti-TNF therapy.<ref>{{Cite journal |last=Cohen |first=Stanley |last2=Wells |first2=Alvin F. |last3=Curtis |first3=Jeffrey R. |last4=Dhar |first4=Rajat |last5=Mellors |first5=Theodore |last6=Zhang |first6=Lixia |last7=Withers |first7=Johanna B. |last8=Jones |first8=Alex |last9=Ghiassian |first9=Susan D. |last10=Wang |first10=Mengran |last11=Connolly-Strong |first11=Erin |last12=Rapisardo |first12=Sarah |last13=Gatalica |first13=Zoran |last14=Pappas |first14=Dimitrios A. |last15=Kremer |first15=Joel M. |date=September 2021 |title=A Molecular Signature Response Classifier to Predict Inadequate Response to Tumor Necrosis Factor-α Inhibitors: The NETWORK-004 Prospective Observational Study |url=https://link.springer.com/10.1007/s40744-021-00330-y |journal=Rheumatology and Therapy |language=en |volume=8 |issue=3 |pages=1159–1176 |doi=10.1007/s40744-021-00330-y |issn=2198-6576 |pmc=8214458 |pmid=34148193}}</ref><ref>{{Cite journal |last=Ghiassian |first=Susan D |last2=Voitalov |first2=Ivan |last3=Withers |first3=Johanna B |last4=Santolini |first4=Marc |last5=Saleh |first5=Alif |last6=Akmaev |first6=Viatcheslav R |date=August 2022 |title=Network-based response module {{sic|comprised |hide=y|of}} gene expression biomarkers predicts response to infliximab at treatment initiation in ulcerative colitis |url=https://linkinghub.elsevier.com/retrieve/pii/S1931524422000494 |journal=Translational Research |language=en |volume=246 |pages=78–86 |doi=10.1016/j.trsl.2022.03.006}}</ref> During COVID  Barabási has led a major collaboration involving researchers at [[Harvard University]], [[Boston University]] and The Broad Institute, predicting and experimentally testing the potential efficacy for COVID patients of 6,000 approved drugs.<ref>{{Cite journal |last=Morselli Gysi, D., Do Valle, Í., Zitnik, M., Ameli, A., Gan, X., Varol, O., Ghiassian, S.D., Patten, J.J., Davey, R.A., Loscalzo, J. and Barabási, A.L. |date=2021 |title=Network medicine framework for identifying drug-repurposing opportunities for COVID-19. |journal=Proceedings of the National Academy of Sciences |volume=118 |issue=19 |pages=e2025581118}}</ref><ref>{{Cite journal |last=Patten |first=J.J. |last2=Keiser |first2=Patrick T. |last3=Morselli-Gysi |first3=Deisy |last4=Menichetti |first4=Giulia |last5=Mori |first5=Hiroyuki |last6=Donahue |first6=Callie J. |last7=Gan |first7=Xiao |last8=Valle |first8=Italo do |last9=Geoghegan-Barek |first9=Kathleen |last10=Anantpadma |first10=Manu |last11=Boytz |first11=RuthMabel |last12=Berrigan |first12=Jacob L. |last13=Stubbs |first13=Sarah H. |last14=Ayazika |first14=Tess |last15=O’Leary |first15=Colin |date=September 2022 |title=Identification of potent inhibitors of SARS-CoV-2 infection by combined pharmacological evaluation and cellular network prioritization |url=https://linkinghub.elsevier.com/retrieve/pii/S258900422201197X |journal=iScience |language=en |volume=25 |issue=9 |pages=104925 |doi=10.1016/j.isci.2022.104925 |pmc=9374494 |pmid=35992305}}</ref>
Barabási is one of the founders of [[network medicine]], a term he coined in a scientific article entiled "Network Medicine – From Obesity to the "Diseasome", published in The New England Journal of Medicine, in 2007.<ref>{{Cite journal |last=Barabási AL |title=Network medicine--from obesity to the "diseasome" |journal=N Engl J Med |year=2007 |volume=357 |issue=4 |pages=404–407|doi=10.1056/NEJMe078114 |pmid=17652657 }}</ref> His work introduced the concept of diseasome, or disease network,<ref>{{Cite journal |last=Goh, K. I., Cusick, M. E., Valle, D., Childs, B., Vidal, M., & Barabási, A. L. |date=2007 |title=The human disease network |journal=Proceedings of the National Academy of Sciences |volume=104 |issue=21 |pages=8685–8690|doi=10.1073/pnas.0701361104 |pmid=17502601 |pmc=1885563 |bibcode=2007PNAS..104.8685G |doi-access=free }}</ref> showing how diseases link to each other through shared genes, capturing their common genetic roots. He subsequently pioneered the use of large patient data to explore disease comorbidity, linking it to molecular network data.<ref>{{cite journal |last1=Barabási |first1=Albert-László |last2=Gulbahce |first2=Natali |last3=Loscalzo |first3=Joseph |title=Network medicine: a network-based approach to human disease |journal=Nature Reviews Genetics |date=January 2011 |volume=12 |issue=1 |pages=56–68 |doi=10.1038/nrg2918|pmid=21164525 |pmc=3140052 }}</ref> He discovered that genes associated with the same disease tend to be located in the same network neighborhood,<ref>{{Cite journal |last=Menche J, Sharma A, Kitsak M, Ghiassian SD, Vidal M, Loscalzo J, Barabási AL. |date=2005 |title=Uncovering disease-disease relationships through the incomplete interactome. |journal=Science |volume=347 |issue=6224 |pages=1257601|doi=10.1126/science.1257601 |pmid=25700523 |pmc=4435741 }}</ref> introducing the concept of disease module, currently used to aid [[drug discovery]], [[drug design]], and the development of [[biomarker]]s for disease detection, as he outlined in 2012 in a [[TEDMED]] talk.<ref>{{Citation |title=Do your proteins have their own social network? |url=https://www.youtube.com/watch?v=10oQMHadGos |language=en |access-date=2022-11-01}}</ref> His work has inspired the founding of the [https://www.brighamandwomens.org/research/departments/channing-division-of-network-medicine/overview Channing Division of Network Medicine at Harvard Medical School] and the [https://www.network-medicine.org/ Network Medicine Institute and Global Alliance], representing 33 leading universities and institutions around the world committed to advancing the field. His work in network medicine has led to multiple experimentally falsifiable predictions, helping identify experimentally validated novel pathways in asthma,<ref>{{Cite journal |last1=Sharma |first1=Amitabh |last2=Menche |first2=Jörg |last3=Huang |first3=C. Chris |last4=Ort |first4=Tatiana |last5=Zhou |first5=Xiaobo |last6=Kitsak |first6=Maksim |last7=Sahni |first7=Nidhi |last8=Thibault |first8=Derek |last9=Voung |first9=Linh |last10=Guo |first10=Feng |last11=Ghiassian |first11=Susan Dina |last12=Gulbahce |first12=Natali |last13=Baribaud |first13=Frédéric |last14=Tocker |first14=Joel |last15=Dobrin |first15=Radu |date=2015-06-01 |title=A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma |journal=Human Molecular Genetics |language=en |volume=24 |issue=11 |pages=3005–3020 |doi=10.1093/hmg/ddv001 |issn=1460-2083 |pmc=4447811 |pmid=25586491}}</ref> predicting novel mechanism of action for rosmarinic acid,<ref>{{Cite journal |last1=do Valle |first1=Italo F. |last2=Roweth |first2=Harvey G. |last3=Malloy |first3=Michael W. |last4=Moco |first4=Sofia |last5=Barron |first5=Denis |last6=Battinelli |first6=Elisabeth |last7=Loscalzo |first7=Joseph |last8=Barabási |first8=Albert-László |date=March 2021 |title=Network medicine framework shows that proximity of polyphenol targets and disease proteins predicts therapeutic effects of polyphenols |url=http://www.nature.com/articles/s43016-021-00243-7 |journal=Nature Food |language=en |volume=2 |issue=3 |pages=143–155 |doi=10.1038/s43016-021-00243-7 |s2cid=232317723 |issn=2662-1355}}</ref> and predicting novel therapeutic functions of existing drugs, and testing them directly in patients (drug repurposing).<ref>{{Cite journal |last1=Cheng |first1=Feixiong |last2=Desai |first2=Rishi J. |last3=Handy |first3=Diane E. |last4=Wang |first4=Ruisheng |last5=Schneeweiss |first5=Sebastian |last6=Barabási |first6=Albert-László |last7=Loscalzo |first7=Joseph |date=December 2018 |title=Network-based approach to prediction and population-based validation of in silico drug repurposing |journal=Nature Communications |language=en |volume=9 |issue=1 |pages=2691 |doi=10.1038/s41467-018-05116-5 |issn=2041-1723 |pmc=6043492 |pmid=30002366|bibcode=2018NatCo...9.2691C }}</ref> The products of network medicine are already on the market, helping tens of thousands of rheumatoid arthritis patients decide if they respond to anti-TNF therapy.<ref>{{Cite journal |last1=Cohen |first1=Stanley |last2=Wells |first2=Alvin F. |last3=Curtis |first3=Jeffrey R. |last4=Dhar |first4=Rajat |last5=Mellors |first5=Theodore |last6=Zhang |first6=Lixia |last7=Withers |first7=Johanna B. |last8=Jones |first8=Alex |last9=Ghiassian |first9=Susan D. |last10=Wang |first10=Mengran |last11=Connolly-Strong |first11=Erin |last12=Rapisardo |first12=Sarah |last13=Gatalica |first13=Zoran |last14=Pappas |first14=Dimitrios A. |last15=Kremer |first15=Joel M. |date=September 2021 |title=A Molecular Signature Response Classifier to Predict Inadequate Response to Tumor Necrosis Factor-α Inhibitors: The NETWORK-004 Prospective Observational Study |journal=Rheumatology and Therapy |language=en |volume=8 |issue=3 |pages=1159–1176 |doi=10.1007/s40744-021-00330-y |issn=2198-6576 |pmc=8214458 |pmid=34148193}}</ref><ref>{{Cite journal |last1=Ghiassian |first1=Susan D |last2=Voitalov |first2=Ivan |last3=Withers |first3=Johanna B |last4=Santolini |first4=Marc |last5=Saleh |first5=Alif |last6=Akmaev |first6=Viatcheslav R |date=August 2022 |title=Network-based response module {{sic|comprised |hide=y|of}} gene expression biomarkers predicts response to infliximab at treatment initiation in ulcerative colitis |url=https://linkinghub.elsevier.com/retrieve/pii/S1931524422000494 |journal=Translational Research |language=en |volume=246 |pages=78–86 |doi=10.1016/j.trsl.2022.03.006|pmid=35306220 |s2cid=247514416 }}</ref> During COVID  Barabási has led a major collaboration involving researchers at [[Harvard University]], [[Boston University]] and The Broad Institute, predicting and experimentally testing the potential efficacy for COVID patients of 6,000 approved drugs.<ref>{{Cite journal |last=Morselli Gysi, D., Do Valle, Í., Zitnik, M., Ameli, A., Gan, X., Varol, O., Ghiassian, S.D., Patten, J.J., Davey, R.A., Loscalzo, J. and Barabási, A.L. |date=2021 |title=Network medicine framework for identifying drug-repurposing opportunities for COVID-19. |journal=Proceedings of the National Academy of Sciences |volume=118 |issue=19 |pages=e2025581118|doi=10.1073/pnas.2025581118 |pmid=33906951 |pmc=8126852 |arxiv=2004.07229 |bibcode=2021PNAS..11825581M }}</ref><ref>{{Cite journal |last1=Patten |first1=J.J. |last2=Keiser |first2=Patrick T. |last3=Morselli-Gysi |first3=Deisy |last4=Menichetti |first4=Giulia |last5=Mori |first5=Hiroyuki |last6=Donahue |first6=Callie J. |last7=Gan |first7=Xiao |last8=Valle |first8=Italo do |last9=Geoghegan-Barek |first9=Kathleen |last10=Anantpadma |first10=Manu |last11=Boytz |first11=RuthMabel |last12=Berrigan |first12=Jacob L. |last13=Stubbs |first13=Sarah H. |last14=Ayazika |first14=Tess |last15=O’Leary |first15=Colin |date=September 2022 |title=Identification of potent inhibitors of SARS-CoV-2 infection by combined pharmacological evaluation and cellular network prioritization |journal=iScience |language=en |volume=25 |issue=9 |pages=104925 |doi=10.1016/j.isci.2022.104925 |pmc=9374494 |pmid=35992305|bibcode=2022iSci...25j4925P }}</ref>


=== Human Dynamics ===
=== Human Dynamics ===
Line 74: Line 74:


=== Network Control ===
=== Network Control ===
His work on [[network controllability]] and [[observability]] brought the tools of [[control theory]] to network science. Barabási asked how to identify the nodes from which one can control a complex network, just like a car is controlled through three control points, the steering wheel, gas pedal and the brake. He developed the analytical formalism of controlling complex networks, by mapping the control problem, widely studied in physics and engineering since [[James Clerk Maxwell|Maxwell]], into [[graph matching]], a well-studied graph theoretic problem, merging statistical mechanics and control theory.<ref>{{Cite journal |last=Liu, Y. Y., Slotine, J. J., & Barabási, A. L. |date=2011 |title=Controllability of complex networks |journal=Nature |volume=473 |issue=7346 |pages=167–173}}</ref> The exact mapping between the dynamical control problem and [[matching (graph theory)|matching]] theory allowed him to develop tools to identify the system's control nodes. He used network control to predict the function of  individual neurons in the [[Caenorhabditis elegans]] [[connectome]], leading not only to the discovery of new neurons involved in the control of locomotion, but also offering falsifiable experimental confirmation of control principles.<ref>{{Cite journal |last=Yan, G., Vértes, P. E., Towlson, E. K., Chew, Y. L., Walker, D. S., Schafer, W. R., & Barabási, A. L. |date=2017 |title=Network control principles predict neuron function in the Caenorhabditis elegans connectome. |journal=Nature |volume=550 |issue=7677 |pages=519–523}}</ref>
His work on [[network controllability]] and [[observability]] brought the tools of [[control theory]] to network science. Barabási asked how to identify the nodes from which one can control a complex network, just like a car is controlled through three control points, the steering wheel, gas pedal and the brake. He developed the analytical formalism of controlling complex networks, by mapping the control problem, widely studied in physics and engineering since [[James Clerk Maxwell|Maxwell]], into [[graph matching]], a well-studied graph theoretic problem, merging statistical mechanics and control theory.<ref>{{Cite journal |last=Liu, Y. Y., Slotine, J. J., & Barabási, A. L. |date=2011 |title=Controllability of complex networks |journal=Nature |volume=473 |issue=7346 |pages=167–173|doi=10.1038/nature10011 |pmid=21562557 |bibcode=2011Natur.473..167L |s2cid=4334171 }}</ref> The exact mapping between the dynamical control problem and [[matching (graph theory)|matching]] theory allowed him to develop tools to identify the system's control nodes. He used network control to predict the function of  individual neurons in the [[Caenorhabditis elegans]] [[connectome]], leading not only to the discovery of new neurons involved in the control of locomotion, but also offering falsifiable experimental confirmation of control principles.<ref>{{Cite journal |last=Yan, G., Vértes, P. E., Towlson, E. K., Chew, Y. L., Walker, D. S., Schafer, W. R., & Barabási, A. L. |date=2017 |title=Network control principles predict neuron function in the Caenorhabditis elegans connectome. |journal=Nature |volume=550 |issue=7677 |pages=519–523|doi=10.1038/nature24056 |pmid=29045391 |pmc=5710776 |bibcode=2017Natur.550..519Y }}</ref>


==Awards==
==Awards==

Revision as of 11:26, 8 November 2022

The native form of this personal name is Barabási Albert-László. This article uses Western name order when mentioning individuals.
Albert-László Barabási
Barabási at the World Economic Forum Annual Meeting of the New Champions in 2012
Born
Barabási Albert László

(1967-03-30) March 30, 1967 (age 57)
Cârța, Harghita County, Romania
CitizenshipRomanian
Hungarian
American
Alma materUniversity of Bucharest (BS)
Eötvös Loránd University (MS)
Boston University (PhD)
Known forResearch of network science
the concept of scale-free networks
Proposal of Barabási–Albert model
Founder of Network Medicine
Introducing Network controllability
AwardsLilienfeld Prize, APS, 2023.

EPS Statistical and Nonlinear Physics Prize, 2021.
Bolyai Prize, Hungarian Academy of Science, 2018 Lagrange Prize, 2011

C&C Prize, NEC, Japan, 2008.
Scientific career
FieldsPhysics, Network Science, Network Medicine
ThesisGrowth and roughening of non-equilibrium interfaces (1994)
Doctoral advisorH. Eugene Stanley
Doctoral studentsGinestra Bianconi Reka Albert
Websitebarabasilab.com

Albert-László Barabási (born March 30, 1967) is a Romanian-born Hungarian-American physicist, best known for his discoveries in network science and network medicine.

He is Distinguished University Professor and Robert Gray Professor of Network Science at Northeastern University, and holds appointments at the Department of Medicine, Harvard Medical School and the Department of Network and Data Science[1] at Central European University. He is the former Emil T. Hofmann Professor of Physics at the University of Notre Dame and former associate member of the Center of Cancer Systems Biology (CCSB) at the Dana–Farber Cancer Institute, Harvard University.

He discovered in 1999 the concept of scale-free networks and proposed the Barabási–Albert model to explain their widespread emergence in natural, technological and social systems, from the cellular telephone to the World Wide Web or online communities. He is the Founding President of the Network Science Society,[2] which sponsors the flagship NetSci conference held yearly since 2006.

Birth and education

Barabási was born to an ethnic Hungarian family in Cârța, Harghita County, Romania. His father, László Barabási, was a historian, museum director and writer, while his mother, Katalin Keresztes, taught literature, and later became director of a children's theater.[3] He attended a high school specializing in science and mathematics; in the tenth grade, he won a local physics olympiad. Between 1986 and 1989, he studied physics and engineering at the University of Bucharest; during that time, he began doing research on chaos theory, publishing three papers.[3]

In 1989, Barabási emigrated to Hungary, together with his father. In 1991, he received a master's degree at Eötvös Loránd University in Budapest, under Tamás Vicsek, before enrolling in the Physics program at Boston University, where he earned a PhD in 1994. His thesis, written under the direction of H. Eugene Stanley,[4] was published by Cambridge University Press under the title Fractal Concepts in Surface Growth.[5][6]

Academic career

After a one-year postdoc at the IBM Thomas J. Watson Research Center, Barabási joined the faculty at the University of Notre Dame in 1995. In 2000, at the age of 32, he was named the Emil T. Hofman Professor of Physics, becoming the youngest endowed professor. In 2004 he founded the Center for Complex Network Research.

In 2005–06 he was a Visiting Professor at Harvard University. In Fall, 2007, Barabási left Notre Dame to become the Distinguished Professor and Director of the Center for Network Science at Northeastern University and to take up an appointment in the Department of Medicine at Harvard Medical School.

As of 2008, Barabási holds Hungarian, Romanian and U.S. citizenship.[7][8][9]

Research and achievements

Barabási has been a major contributor to the development of network science and the statistical physics of complex systems.

Scale-Free Networks

His biggest role has been the discovery of the scale-free networks. He reported the scale-free nature of the WWW in 1999[10] and the same year, in a Science paper with Réka Albert, he proposed the Barabási–Albert model, predicting that growth and preferential attachment are jointly responsible for the emergence of the scale-free property in real networks. According to the review of one of Barabási's books, preferential attachment can be described as follows:

"Barabási has found that the websites that form the network (of the WWW) have certain mathematical properties. The conditions for these properties to occur are threefold. The first is that the network has to be expanding, growing. This precondition of growth is very important as the idea of emergence comes with it. It is constantly evolving and adapting. That condition exists markedly with the world wide web. The second is the condition of preferential attachment, that is, nodes (websites) will wish to link themselves to hubs (websites) with the most connections. The third condition is what is termed competitive fitness which in network terms means its rate of attraction."[11]

He subsequently showed that the scale-free property emerges in biological systems, namely in metabolic networks[12] and protein–protein interaction[13] networks. Science celebrated the ten-year anniversary of Barabási’s 1999 discovery by devoting a special issue to Complex Systems and Networks in 2009.[14][15]

Network Robustness

In a 2001 paper with Réka Albert and Hawoong Jeong he demonstrated the Achilles' heel property of scale-free networks, showing that such networks are robust to random failures but fragile to attacks.[16] Specifically, they showed that networks can easily survive the random failure of a very large number of nodes, i.e. the failure threshold is high. In contrast, the same networks collapse if they are attacked, by removing the biggest hubs first. The threshold characterizing the breakdown of a network under random failures was subsequently linked[17] it to the second moment of the degree distribution. The threshold converges to zero for large networks, indicating that large networks can easily survive the failure of a very large fraction of their nodes. The calculations also showed that robustness to random failures is not limited to scale-free networks, but it is a general property of most real networks with a wide range of node degrees.

Network Medicine

Barabási is one of the founders of network medicine, a term he coined in a scientific article entiled "Network Medicine – From Obesity to the "Diseasome", published in The New England Journal of Medicine, in 2007.[18] His work introduced the concept of diseasome, or disease network,[19] showing how diseases link to each other through shared genes, capturing their common genetic roots. He subsequently pioneered the use of large patient data to explore disease comorbidity, linking it to molecular network data.[20] He discovered that genes associated with the same disease tend to be located in the same network neighborhood,[21] introducing the concept of disease module, currently used to aid drug discovery, drug design, and the development of biomarkers for disease detection, as he outlined in 2012 in a TEDMED talk.[22] His work has inspired the founding of the Channing Division of Network Medicine at Harvard Medical School and the Network Medicine Institute and Global Alliance, representing 33 leading universities and institutions around the world committed to advancing the field. His work in network medicine has led to multiple experimentally falsifiable predictions, helping identify experimentally validated novel pathways in asthma,[23] predicting novel mechanism of action for rosmarinic acid,[24] and predicting novel therapeutic functions of existing drugs, and testing them directly in patients (drug repurposing).[25] The products of network medicine are already on the market, helping tens of thousands of rheumatoid arthritis patients decide if they respond to anti-TNF therapy.[26][27] During COVID  Barabási has led a major collaboration involving researchers at Harvard University, Boston University and The Broad Institute, predicting and experimentally testing the potential efficacy for COVID patients of 6,000 approved drugs.[28][29]

Human Dynamics

His work on human dynamics resulted in the discovery of the fat tailed nature of the inter event times in human activity patterns. The pattern showed that human activity is bursty - short periods of intensive activity is followed by long periods that lack detectable activity. He also proposed the Barabási model[30] that showed that a queuing model was capable of explaining the bursty nature of human activity. This topic is covered by his book Bursts.[31]

Network Control

His work on network controllability and observability brought the tools of control theory to network science. Barabási asked how to identify the nodes from which one can control a complex network, just like a car is controlled through three control points, the steering wheel, gas pedal and the brake. He developed the analytical formalism of controlling complex networks, by mapping the control problem, widely studied in physics and engineering since Maxwell, into graph matching, a well-studied graph theoretic problem, merging statistical mechanics and control theory.[32] The exact mapping between the dynamical control problem and matching theory allowed him to develop tools to identify the system's control nodes. He used network control to predict the function of  individual neurons in the Caenorhabditis elegans connectome, leading not only to the discovery of new neurons involved in the control of locomotion, but also offering falsifiable experimental confirmation of control principles.[33]

Awards

He was elected a Fellow of the American Physical Society in 2003.[34] In 2005, he was awarded the FEBS Anniversary Prize for Systems Biology and in 2006 he was awarded the John von Neumann Medal by the John von Neumann Computer Society from Hungary, for outstanding achievements in computer-related science and technology.[35]

In 2004, he was elected as an external member of the Hungarian Academy of Sciences. In 2007, he was inducted into the Academia Europaea.[36]

In 2008 he received the 2008 C&C Prize, Japan "for stimulating innovative research on networks and discovering that the scale-free property is a common feature of various real-world complex networks"[37] and the Cozzarelli Prize, National Academies of Sciences (USA)[38]

The Lagrange Prize-Crt Foundation was awarded to Barabási in June 2011, and in November 2011 he was awarded Honorary degree Doctor Honoris Causa by Technical University of Madrid.[39] In 2017 he received the Senior scientific award of the Complex Systems Society for "setting the basis of what is now modern Network Science".[40]

In 2018 Barabási has received an honorary doctorate from Utrecht University at the occasion of her 382th Dies Natalis[41] and he was elected member of the Romanian Academy of Sciences.[42]

The Bolyai Prize was awarded to Mr. Barabási in May 2019 by the Hungarian Academy of Sciences, handed over by the President of Hungary, János Áder.

In 2021 Barabási was ranked 2nd in the world in the field of Engineering and Technology.[43]

In 2021 Barabási received the EPS Statistical and Nonlinear Physics Prize, for "his pioneering contributions to the development of complex network science, in particular for his seminal work on scale-free networks, the preferential attachment model, error and attack tolerance in complex networks, controllability of complex networks, the physics of social ties, communities, and human mobility patterns, genetic, metabolic, and biochemical networks, as well as applications in network biology and network medicine."

Barabási was the recipient of the 2023 Julius Edgar Lilienfeld Prize  by the American Physical Society,[44]  "For pioneering work on the statistical physics of networks that transformed the study of complex systems, and for lasting contributions in communicating the significance of this rapidly developing field to a broad range of audiences."

Selected publications

  • Barabási, Albert-László, The Formula: The Universal Laws of Success, November 6, 2018; ISBN 0-316-50549-8 (hardcover)
  • Barabási, Albert-László (2018). Network science. Cambridge University Press. ISBN 978-1107076266.
  • Barabási, Albert-László, Bursts: The Hidden Pattern Behind Everything We Do, April 29, 2010; ISBN 0-525-95160-1 (hardcover)
  • Barabási, Albert-László, Linked: The New Science of Networks, 2002. ISBN 0-452-28439-2 (pbk)
  • Barabási, Albert-László and Réka Albert, "Emergence of scaling in random networks", Science, 286:509–512, October 15, 1999
  • Barabási, Albert-László and Zoltán Oltvai, "Network Biology", Nature Reviews Genetics 5, 101–113 (2004)
  • Barabási, Albert-László, Mark Newman and Duncan J. Watts, The Structure and Dynamics of Networks, 2006; ISBN 0-691-11357-2
  • Barabási, Albert-László, Natali Gulbahce, and Joseph Loscalzo, "Network Medicine", Nature Reviews Genetics 12, 56–68 (2011)
  • Réka Albert, Hawoong Jeong & Barabási, Albert-László (1999). "The Diameter of the WWW". Nature. 401 (6749): 130–31. arXiv:cond-mat/9907038. Bibcode:1999Natur.401..130A. doi:10.1038/43601. S2CID 4419938.
  • Y.-Y. Liu, J.-J. Slotine, A.-L. Barabási, "Controllability of complex networks", Nature 473, 167–173 (2011)
  • Y.-Y. Liu, J.-J. Slotine, A.-L. Barabási, "Observability of complex systems", Proceedings of the National Academy of Sciences 110, 1–6 (2013)
  • Baruch Barzel and A.-L. Barabási, "Universality in Network Dynamics", Nature Physics 9, 673–681 (2013)
  • Baruch Barzel and A.-L. Barabási, "Network link prediction by global silencing of indirect correlations", Nature Biotechnology 31, 720–725 (2013)
  • B. Barzel Y.-Y. Liu and A.-L. Barabási, "Constructing minimal models for complex system dynamics", Nature Communications 6, 7186 (2015)

References

  1. ^ People at Center for Network Science, Central European University website; accessed January 10, 2016.
  2. ^ "NetSci – the Network Science Society".
  3. ^ a b Dale Keiger, "Looking for the next big thing", Notre Dame Magazine, vol. 36 (Spring 2007), no. 1, 49–53 Archived May 9, 2008, at the Wayback Machine
  4. ^ "H. Eugene Stanley: Ph.D. Theses Supervised". Polymer.bu.edu. Retrieved January 11, 2016.
  5. ^ Albert-László Barabási at the Mathematics Genealogy Project
  6. ^ Albert-Laszlo Barabasi, Eugene H Stanley (1995). Fractal Concepts in Surface Growth. Cambridge University Press. ISBN 9780511599798.
  7. ^ "Albert-László Barabási CV" (PDF). Archived from the original (PDF) on March 3, 2016. Retrieved January 10, 2016.
  8. ^ "ETSI de Telecomunicación: ALBERT LASZLÓ BARABÁSI". www.etsit.upm.es. Retrieved November 15, 2020.
  9. ^ "Albert-László Barabási – Khoury College of Computer Sciences". Retrieved November 15, 2020.
  10. ^ Albert, Réka; Jeong, Hawoong; Barabási, Albert-László (September 1999). "Diameter of the World-Wide Web". Nature. 401 (6749): 130–131. arXiv:cond-mat/9907038. Bibcode:1999Natur.401..130A. doi:10.1038/43601. ISSN 0028-0836. S2CID 4419938.
  11. ^ Profile Archived March 9, 2005, at the Wayback Machine, sociopranos.com; accessed 10 January 2016.
  12. ^ Jeong, H.; Tombor, B.; Albert, R.; Oltvai, Z. N.; Barabási, A.-L. (October 2000). "The large-scale organization of metabolic networks". Nature. 407 (6804): 651–654. arXiv:cond-mat/0010278. Bibcode:2000Natur.407..651J. doi:10.1038/35036627. ISSN 0028-0836. S2CID 4426931.
  13. ^ Jeong, H.; Mason, S. P.; Barabási, A.-L.; Oltvai, Z. N. (May 2001). "Lethality and centrality in protein networks". Nature. 411 (6833): 41–42. arXiv:cond-mat/0105306. Bibcode:2001Natur.411...41J. doi:10.1038/35075138. ISSN 0028-0836. PMID 11333967. S2CID 258942.
  14. ^ Barabasi, Albert-Laszlo (2009). "Scale-Free Networks: A Decade and Beyond". Science. 325 (5939): 412–413. Bibcode:2009Sci...325..412B. doi:10.1126/science.1173299. PMID 19628854. S2CID 43910070.
  15. ^ Jasny, Barbara (2009). "Connections". Science. 325 (5939): 405. Bibcode:2009Sci...325..405J. doi:10.1126/science.325_405. PMID 19628849.
  16. ^ Barabási, Albert-László (July 21, 2016). Network science. Cambridge, United Kingdom. ISBN 9781107076266.{{cite book}}: CS1 maint: location missing publisher (link)
  17. ^ Cohen, Reuven; Erez, Keren; ben-Avraham, Daniel; Havlin, Shlomo (2000). ""Resilience of the Internet to Random Breakdowns"". Physical Review Letters. 85 (21): 4626–4628. arXiv:cond-mat/0007048. Bibcode:2000PhRvL..85.4626C. doi:10.1103/PhysRevLett.85.4626. PMID 11082612. S2CID 15372152.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  18. ^ Barabási AL (2007). "Network medicine--from obesity to the "diseasome"". N Engl J Med. 357 (4): 404–407. doi:10.1056/NEJMe078114. PMID 17652657.
  19. ^ Goh, K. I., Cusick, M. E., Valle, D., Childs, B., Vidal, M., & Barabási, A. L. (2007). "The human disease network". Proceedings of the National Academy of Sciences. 104 (21): 8685–8690. Bibcode:2007PNAS..104.8685G. doi:10.1073/pnas.0701361104. PMC 1885563. PMID 17502601.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  20. ^ Barabási, Albert-László; Gulbahce, Natali; Loscalzo, Joseph (January 2011). "Network medicine: a network-based approach to human disease". Nature Reviews Genetics. 12 (1): 56–68. doi:10.1038/nrg2918. PMC 3140052. PMID 21164525.
  21. ^ Menche J, Sharma A, Kitsak M, Ghiassian SD, Vidal M, Loscalzo J, Barabási AL. (2005). "Uncovering disease-disease relationships through the incomplete interactome". Science. 347 (6224): 1257601. doi:10.1126/science.1257601. PMC 4435741. PMID 25700523.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  22. ^ Do your proteins have their own social network?, retrieved November 1, 2022
  23. ^ Sharma, Amitabh; Menche, Jörg; Huang, C. Chris; Ort, Tatiana; Zhou, Xiaobo; Kitsak, Maksim; Sahni, Nidhi; Thibault, Derek; Voung, Linh; Guo, Feng; Ghiassian, Susan Dina; Gulbahce, Natali; Baribaud, Frédéric; Tocker, Joel; Dobrin, Radu (June 1, 2015). "A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma". Human Molecular Genetics. 24 (11): 3005–3020. doi:10.1093/hmg/ddv001. ISSN 1460-2083. PMC 4447811. PMID 25586491.
  24. ^ do Valle, Italo F.; Roweth, Harvey G.; Malloy, Michael W.; Moco, Sofia; Barron, Denis; Battinelli, Elisabeth; Loscalzo, Joseph; Barabási, Albert-László (March 2021). "Network medicine framework shows that proximity of polyphenol targets and disease proteins predicts therapeutic effects of polyphenols". Nature Food. 2 (3): 143–155. doi:10.1038/s43016-021-00243-7. ISSN 2662-1355. S2CID 232317723.
  25. ^ Cheng, Feixiong; Desai, Rishi J.; Handy, Diane E.; Wang, Ruisheng; Schneeweiss, Sebastian; Barabási, Albert-László; Loscalzo, Joseph (December 2018). "Network-based approach to prediction and population-based validation of in silico drug repurposing". Nature Communications. 9 (1): 2691. Bibcode:2018NatCo...9.2691C. doi:10.1038/s41467-018-05116-5. ISSN 2041-1723. PMC 6043492. PMID 30002366.
  26. ^ Cohen, Stanley; Wells, Alvin F.; Curtis, Jeffrey R.; Dhar, Rajat; Mellors, Theodore; Zhang, Lixia; Withers, Johanna B.; Jones, Alex; Ghiassian, Susan D.; Wang, Mengran; Connolly-Strong, Erin; Rapisardo, Sarah; Gatalica, Zoran; Pappas, Dimitrios A.; Kremer, Joel M. (September 2021). "A Molecular Signature Response Classifier to Predict Inadequate Response to Tumor Necrosis Factor-α Inhibitors: The NETWORK-004 Prospective Observational Study". Rheumatology and Therapy. 8 (3): 1159–1176. doi:10.1007/s40744-021-00330-y. ISSN 2198-6576. PMC 8214458. PMID 34148193.
  27. ^ Ghiassian, Susan D; Voitalov, Ivan; Withers, Johanna B; Santolini, Marc; Saleh, Alif; Akmaev, Viatcheslav R (August 2022). "Network-based response module comprised of gene expression biomarkers predicts response to infliximab at treatment initiation in ulcerative colitis". Translational Research. 246: 78–86. doi:10.1016/j.trsl.2022.03.006. PMID 35306220. S2CID 247514416.
  28. ^ Morselli Gysi, D., Do Valle, Í., Zitnik, M., Ameli, A., Gan, X., Varol, O., Ghiassian, S.D., Patten, J.J., Davey, R.A., Loscalzo, J. and Barabási, A.L. (2021). "Network medicine framework for identifying drug-repurposing opportunities for COVID-19". Proceedings of the National Academy of Sciences. 118 (19): e2025581118. arXiv:2004.07229. Bibcode:2021PNAS..11825581M. doi:10.1073/pnas.2025581118. PMC 8126852. PMID 33906951.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  29. ^ Patten, J.J.; Keiser, Patrick T.; Morselli-Gysi, Deisy; Menichetti, Giulia; Mori, Hiroyuki; Donahue, Callie J.; Gan, Xiao; Valle, Italo do; Geoghegan-Barek, Kathleen; Anantpadma, Manu; Boytz, RuthMabel; Berrigan, Jacob L.; Stubbs, Sarah H.; Ayazika, Tess; O’Leary, Colin (September 2022). "Identification of potent inhibitors of SARS-CoV-2 infection by combined pharmacological evaluation and cellular network prioritization". iScience. 25 (9): 104925. Bibcode:2022iSci...25j4925P. doi:10.1016/j.isci.2022.104925. PMC 9374494. PMID 35992305.
  30. ^ A.-L. Barabási (2005). "The origin of bursts and heavy tails in human dynamics". Nature. 435 (7039): 207–11. arXiv:cond-mat/0505371. Bibcode:2005Natur.435..207B. doi:10.1038/nature03459. PMID 15889093. S2CID 4419475.
  31. ^ Barabási, Albert-László. (2010). Bursts : the hidden pattern behind everything we do. New York, N.Y.: Dutton. ISBN 978-0-525-95160-5. OCLC 426800811.
  32. ^ Liu, Y. Y., Slotine, J. J., & Barabási, A. L. (2011). "Controllability of complex networks". Nature. 473 (7346): 167–173. Bibcode:2011Natur.473..167L. doi:10.1038/nature10011. PMID 21562557. S2CID 4334171.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  33. ^ Yan, G., Vértes, P. E., Towlson, E. K., Chew, Y. L., Walker, D. S., Schafer, W. R., & Barabási, A. L. (2017). "Network control principles predict neuron function in the Caenorhabditis elegans connectome". Nature. 550 (7677): 519–523. Bibcode:2017Natur.550..519Y. doi:10.1038/nature24056. PMC 5710776. PMID 29045391.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  34. ^ "APS Fellow Archive". APS. Retrieved September 15, 2020.
  35. ^ "Barabasi co edits new book and is awarded computing medal". Nd.edu. Archived from the original on March 3, 2016. Retrieved January 11, 2016.
  36. ^ "Northeastern Physicist Albert-László Barabási Receives Prestigious Honor for Exemplary Contributions to Interdisciplinary Science" Archived August 26, 2016, at the Wayback Machine, Northeastern University website; accessed January 10, 2016.
  37. ^ "NEC C&C Foundation". Candc.or.jp. Retrieved January 11, 2016.
  38. ^ "PNAS announces 2008 Cozzarelli Prize recipients". EurekAlert!. February 23, 2009. Retrieved January 11, 2016.
  39. ^ [1] Archived January 8, 2012, at the Wayback Machine
  40. ^ "CSS Awards". cssociety.org. Retrieved April 17, 2019.
  41. ^ "Honorary doctorate for Prof. Albert-László Barabási". Utrecht University. March 27, 2018. Retrieved April 17, 2019.
  42. ^ "Comunicat de presă - Academia Română – AGERPRES".
  43. ^ "Research.com - Leading Academic Research Portal". Research.com. Retrieved March 30, 2022.
  44. ^ APS. "2023 Julius Edgar Lilienfeld Prize Recipient".

External links

Retrieved from "https://en.wikipedia.org/w/index.php?title=Albert-László_Barabási&oldid=1120707819"