479:
each protein appears only once in the interactome, whereas in reality, one protein can occur in different contexts and different cellular locations. Such signaling modules are therapeutically best targeted at several sites, which is now the new and clinically applied definition of network pharmacology. To achieve higher than current precision, patients must not be selected solely on descriptive phenotypes but also based on diagnostics that detect the module dysregulation. Moreover, such mechanism-based network pharmacology has the advantage that each of the drugs used within one module is highly synergistic, which allows for reducing the doses of each drug, which then reduces the potential of these drugs acting on other proteins outside the module and hence the chance for unwanted side effects.
35:
333:, also called the diseasome, are networks in which the nodes are diseases and the links, the strength of correlation between them. This correlation is commonly quantified based on associated cellular components that two diseases share. The first-published human disease network (HDN) looked at genes, finding that many of the disease associated genes are
536:
The development of organs and other biological systems can be modelled as network structures where the clinical (e.g., radiographic, functional) characteristics can be represented as nodes and the relationships between these characteristics are represented as the links among such nodes. Therefore, it
308:, which is a neighborhood or group of components in the interactome that, if disrupted, results in a specific pathophenotype. Disease modules can be used in a variety of ways, such as predicting disease genes that have not been discovered yet. Therefore, network medicine looks to identify the disease
409:
in these networks and understand the role of the environment on the interactome. The human symptom-disease network (HSDN), published in June 2014, showed that the symptoms of disease and disease associated cellular components were strongly correlated and that diseases of the same categories tend to
527:
Recently, some researchers tended to represent medication use in form of networks. The nodes in these networks represent medications and the edges represent some sort of relationship between these medications. Cavallo et al. (2013) described the topology of a co-prescription network to demonstrate
208:
database. The projection of the diseases, called the human disease network (HDN), is a network of diseases connected to each other if they share a common gene. Using the HDN, diseases can be classified and analyzed through the genetic relationships between them. Network medicine has proven to be a
528:
which drug classes are most co-prescribed. Bazzoni et al. (2015) concluded that the DPNs of co-prescribed medications are dense, highly clustered, modular and assortative. Askar et al. (2021) created a network of the severe drug-drug interactions (DDIs) showing that it consisted of many clusters.
478:
have also been studied in this field. The next iteration of network pharmacology used entirely different disease definitions, defined as dysfunction in signaling modules derived from protein-protein interaction modules. The latter as well as the interactome had many conceptual shortcomings, e.g.,
436:
as well as the type of drug-drug interactions, thus can help design efficient and safe therapeutic strategies. In addition, the drug-target network (DTN) can play an important role in understanding the mechanisms of action of approved and experimental drugs. The network theory view of
465:
for disease detection. There can be a variety of ways to identifying drugs using network pharmacology; a simple example of this is the "guilt by association" method. This states if two diseases are treated by the same drug, a drug that treats one disease may treat the other.
961:
Schäfer, Samuel; Smelik, Martin; Sysoev, Oleg; Zhao, Yelin; Eklund, Desiré; Lilja, Sandra; Gustafsson, Mika; Heyn, Holger; Julia, Antonio; Kovács, István A.; Loscalzo, Joseph; Marsal, Sara; Zhang, Huan; Li, Xinxiu; Gawel, Danuta (20 March 2024).
297:
Using interactome networks, one can discover and classify diseases, as well as develop treatments through knowledge of its associations and their role in the networks. One observation is that diseases can be classified not by their principle
1020:
Nogales C, Mamdouh ZM, List M, Kiel C, Casas AI, Schmidt HHHW. Network pharmacology: curing causal mechanisms instead of treating symptoms. Trends
Pharmacol Sci. 2022 Feb;43(2):136-150. doi: 10.1016/j.tips.2021.11.004. Epub 2021 Dec 9. PMID
857:
Rual, J. F., Venkatesan, K., Hao, T., Hirozane-Kishikawa, T., Dricot, A., Li, N., ... & Vidal, M. (2005). Towards a proteome-scale map of the human protein–protein interaction network. Nature, 437(7062),
1237:
Scala, A. Auconi, P., Scazzocchio, M., Caldarelli, G., McNamara, J., Franchi, L. (2014). Complex networks for data-driven medicine: the case of Class III dentoskeletal disharmony, New J. Phys. 16 115017
623:
Catalyst (The
Harvard Clinical and Translational Science Center) offers a three-day course entitled "Introduction to Network Medicine", open to clinical and science professionals with doctorate degrees.
157:, similarly to social and technological systems, contain many components that are connected in complicated relationships but are organized by simple principles. Relaying on the tools and principles of
393:
exceeds a predefined threshold. This does not look at the mechanism of action of diseases, but captures disease progression and how highly connected diseases correlate to higher mortality rates.
337:, as these are the genes that do not completely disrupt the network and are able to be passed down generations. Metabolic disease networks (MDN), in which two diseases are connected by a shared
1276:
951:
Chiang, A. P., & Butte, A. J. (2009). Systematic evaluation of drug–disease relationships to identify leads for novel drug uses. Clinical
Pharmacology & Therapeutics, 86(5), 507–510.
765:
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.
924:
Roeland van Wijk et al., Non-monotonic dynamics and crosstalk in signaling pathways and their implications for pharmacology. Scientific
Reports 5:11376 (2015) doi: 10.1038/srep11376
885:
Hidalgo, C. A., Blumm, N., Barabási, A. L., & Christakis, N. A. (2009). A dynamic network approach for the study of human phenotypes. PLoS Computational
Biology, 5(4), e1000353.
867:
Ravasz, E., Somera, A. L., Mongru, D. A., Oltvai, Z. N., & Barabási, A. L. (2002). Hierarchical organization of modularity in metabolic networks. science, 297(5586), 1551–1555.
933:
Mehrad Babaei et al., Biochemical reaction network topology defines dose-dependent Drug–Drug interactions. Comput Biol Med 155:106584 (2023) doi: 10.1016/j.compbiomed.2023.106584
894:
Liu, Y. I., Wise, P. H., & Butte, A. J. (2009). The "etiome": identification and clustering of human disease etiological factors. BMC bioinformatics, 10(Suppl 2), S14.
1228:
P. Auconi, G. Caldarelli, A. Scala, G. Ierardo, A. Polimeni (2011). A network approach to orthodontic diagnosis, Orthodontics and
Craniofacial Research 14, 189-197.
964:"scDrugPrio: a framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases"
848:
Loscalzo, J., & Barabasi, A. L. (2011). Systems biology and the future of medicine. Wiley
Interdisciplinary Reviews: Systems Biology and Medicine, 3(6), 619–627.
173:, diseases, phenotypes, etc.) and links (edges) represent their relationships (physical interactions, shared metabolic pathway, shared gene, shared trait, etc.).
1039:
Christakis, N. A., & Fowler, J. H. (2007). The spread of obesity in a large social network over 32 years. New
England Journal of Medicine, 357(4), 370–379.
836:
Barabási, A. L., Gulbahce, N., & Loscalzo, J. (2011). Network medicine: a network-based approach to human disease. Nature
Reviews Genetics, 12(1), 56–68.
876:
Braun, P., Rietman, E., & Vidal, M. (2008). Networking metabolites and diseases. Proceedings of the National Academy of Sciences, 105(29), 9849–9850.
432:
that looks at the effect of drugs on both the interactome and the diseasome. The topology of a biochemical reaction network determines the shape of drug
1074:, & Vespignani, A. (2014). Assessing the international spreading risk associated with the 2014 West African Ebola outbreak. PLOS Currents Outbreaks.
753:
Chan, S. Y., & Loscalzo, J. (2012). The emerging paradigm of network medicine in the study of human disease. Circulation research, 111(3), 359–374.
1141:
Bazzoni, Gianfranco (April 2015). "The Drug Prescription Network: A System-Level View of Drug Co-Prescription in Community-Dwelling Elderly People".
53:
942:
Yıldırım, M. A., Goh, K. I., Cusick, M. E., Barabási, A. L., & Vidal, M. (2007). Drug—target network. Nature Biotechnology, 25(10), 1119–1126.
619:
offers an undergraduate course called "Network Medicine: Using Systems Biology and Signaling Networks to Create Novel Cancer Therapeutics". Also,
196:
and that representing these as complex networks will shed light on the causes and mechanisms of diseases. It is possible, for example, to infer a
169:
linked together by a particular biological or molecular relationship. For networks pertaining to medicine, nodes represent biological factors (
495:
and social networks play a role in the spread of disease. Social networks have been used to assess the role of social ties in the spread of
373:
states that if a metabolic pathway is linked to two different diseases, then the two diseases likely have a shared metabolic origin (
1030:
Pastor-Satorras, R., & Vespignani, A. (2001). Epidemic spreading in scale-free networks. Physical review letters, 86(14), 3200.
361:
states that if a gene is linked to two different disease phenotypes, then the two diseases likely have a common genetic origin (
1048:
Keeling, M. J., & Eames, K. T. (2005). Networks and epidemic models. Journal of the Royal Society Interface, 2(4), 295–307.
1084:
616:
397:
Some disease networks connect diseases to associated factors outside the human cell. Networks of environmental and genetic
915:
Hopkins, A. L. (2008). Network pharmacology: the next paradigm in drug discovery. Nature Chemical Biology, 4(11), 682–690.
903:
Zhou, X., Menche, J., Barabási, A. L., & Sharma, A. (2014). Human symptoms–disease network. Nature Communications, 5.
516:
453:(API) aimed at one target may not affect the entire disease module. The concept of disease modules can be used to aid in
149:
1057:
Pastor-Satorras, R., & Vespignani, A. (2002). Immunization of complex networks. Physical Review E, 65(3), 036104.
71:
719:
512:
450:
263:
17:
546:
127:
are used to model the spreading of disease across populations. Network medicine is a medically focused area of
140:
185:
115:, which map relationships between diseases and biological factors, also play an important role in the field.
1247:
232:, can be used for disease identification and prevention. These networks have been technically classified as
639:
251:
193:
104:
1261:
605:
433:
309:
184:, and the social network. The network medicine is based on the idea that understanding complexity of
1277:"Network Medicine: Using Systems Biology and Signaling Networks to Create Novel Cancer Therapeutics"
176:
Barabasi suggested that understanding human disease requires us to focus on three key networks, the
654:
1314:
573:
287:
1176:
Askar, Mohsen (June 2021). "An introduction to network analysis for studies of medication use".
402:
245:
669:
330:
325:
181:
166:
112:
714:
500:
429:
419:
406:
49:
8:
446:
398:
258:
and their interactions between each other as links. These maps utilize databases such as
255:
241:
1294:
998:
963:
1211:
1185:
1123:
811:
776:
634:
374:
346:
233:
189:
100:
441:
is based on the effect of the drug in the interactome, especially the region that the
385:
uses phenotypic disease networks (PDN), where two diseases are linked if the observed
1215:
1203:
1158:
1115:
1003:
985:
816:
798:
679:
601:
342:
271:
267:
177:
154:
108:
1127:
91:
towards identifying, preventing, and treating diseases. This field focuses on using
1195:
1150:
1107:
993:
975:
806:
788:
699:
684:
664:
471:
467:
362:
313:
96:
92:
1199:
775:
Sonawane, Abhijeet R.; Weiss, Scott T.; Glass, Kimberly; Sharma, Amitabh (2019).
709:
689:
649:
554:
550:
504:
401:
linked with shared diseases, called the "etiome", can be also used to assess the
197:
162:
128:
88:
507:, have been adapted to be used in network analysis. These models can be used in
980:
694:
644:
597:
488:
454:
438:
345:, have also been extensively studied and is especially relevant in the case of
334:
283:
158:
120:
1098:
Cavallo, Pierpaolo (February 2013). "Network analysis of drug prescriptions".
449:
for a complex disease (polypharmacology) is suggested in this field since one
1308:
1071:
989:
802:
793:
537:
is possible to use networks to model how organ systems dynamically interact.
508:
237:
228:
The whole set of molecular interactions in the human cell, also known as the
1207:
1162:
1119:
1007:
820:
704:
659:
569:
492:
425:
124:
116:
1154:
1067:
674:
487:
Network epidemics has been built by applying network science to existing
475:
458:
442:
386:
278:
if they are in the same pathway. Researchers have used databases such as
229:
223:
170:
338:
275:
161:, the organizing principles can be analyzed by representing systems as
1111:
609:
549:
was created in 2012 to study, reclassify, and develop treatments for
462:
390:
299:
143:
in an the article "Network Medicine – From Obesity to the 'Diseasome
1190:
739:
Caldarelli G. (2007). Scale-Free Networks. Oxford University Press.
565:
410:
form highly connected communities, with respect to their symptoms.
620:
496:
259:
587:
200:
representing the connections of diseases to their associated
1262:"Yang-Yu Liu – Harvard Catalyst Profiles – Harvard Catalyst"
1085:"Disease modelers project a rapidly rising toll from Ebola"
583:
279:
205:
201:
99:
towards identifying diseases and developing medical drugs.
540:
291:
774:
960:
582:
focuses on complex respiratory diseases, specifically
572:
in large, long-term epidemiology studies, such as the
515:
and has been recently used to model the spread of the
499:
in populations. Epidemic models and concepts, such as
119:
is extensively studied using network science as well;
1295:"Introduction to Network Medicine – Harvard Catalyst"
777:"Network Medicine in the Age of Biomedical Big Data"
522:
1066:Gomes, M. F., Piontti, A. P., Rossi, L., Chao, D.,
511:policies, in order to implement strategies such as
44:
may be too technical for most readers to understand
1306:
596:uses multidisciplinary approaches, including as
209:valuable tool in analyzing big biomedical data.
282:to map these networks. Others networks include
1178:Research in Social and Administrative Pharmacy
911:
909:
844:
842:
832:
830:
139:The term "network medicine" was introduced by
733:
545:The Channing Division of Network Medicine at
352:Three representations of the diseasome are:
1274:
906:
839:
827:
608:, to understand complex diseases and guide
1222:
749:
747:
745:
1231:
1189:
997:
979:
810:
792:
761:
759:
270:encompasses the biochemical reactions in
72:Learn how and when to remove this message
56:, without removing the technical details.
1248:"Channing Division of Network Medicine"
1140:
1097:
742:
541:Educational and clinical implementation
14:
1307:
756:
1175:
617:Massachusetts Institute of Technology
54:make it understandable to non-experts
1100:Pharmacoepidemiology and Drug Safety
482:
312:for a specific pathophenotype using
254:have been mapped, using proteins as
111:, are utilized by network medicine.
28:
517:Ebola virus epidemic in West Africa
150:The New England Journal of Medicine
24:
370:Shared metabolic pathway formalism
25:
1326:
531:
523:Drug prescription networks (DPNs)
519:across countries and continents.
212:
720:Targeted immunization strategies
590:, in smaller population studies.
451:active pharmaceutical ingredient
264:Human Protein Reference Database
153:, in 2007. Barabási states that
33:
1287:
1268:
1254:
1240:
1169:
1134:
1091:
1077:
1060:
1051:
1042:
1033:
1024:
1014:
954:
945:
936:
927:
918:
897:
888:
879:
580:Systems Genetics & Genomics
428:is a developing field based in
413:
870:
861:
851:
768:
557:. It focuses on three areas:
302:(pathophenotype) but by their
217:
13:
1:
1200:10.1016/j.sapharm.2021.06.021
726:
382:Disease comorbidity formalism
134:
640:Biological network inference
562:Chronic Disease Epidemiology
547:Brigham and Women's Hospital
319:
252:Protein-protein interactions
194:protein-protein interactions
105:protein-protein interactions
7:
627:
165:, which are collections of
10:
1331:
981:10.1186/s13073-024-01314-7
606:combinatorial optimization
553:using network science and
417:
323:
221:
461:, and the development of
794:10.3389/fgene.2019.00294
655:Glossary of graph theory
288:gene regulatory networks
493:transportation networks
125:transportation networks
472:drug-drug interactions
331:Human disease networks
246:betweenness centrality
141:Albert-László Barabási
87:is the application of
1155:10.1089/rej.2014.1628
1143:Rejuvenation Research
781:Frontiers in Genetics
670:Human disease network
513:targeted immunization
407:environmental factors
358:Shared gene formalism
326:Human disease network
314:clustering algorithms
715:Systems pharmacology
574:Nurses' Health Study
430:systems pharmacology
420:Systems pharmacology
242:small-world networks
447:Combination therapy
434:dose-response curve
399:etiological factors
375:metabolic disorders
347:metabolic disorders
335:non-essential genes
190:metabolic reactions
101:Biological networks
1281:MIT OpenCourseWare
635:Biological network
272:metabolic pathways
155:biological systems
109:metabolic pathways
1275:Dr. Michael Lee.
1184:(12): 2054–2061.
1087:. 31 August 2014.
680:Metabolic network
602:dynamical systems
594:Systems Pathology
483:Network epidemics
363:genetic disorders
343:metabolic pathway
274:, connecting two
268:metabolic network
178:metabolic network
82:
81:
74:
16:(Redirected from
1322:
1299:
1298:
1291:
1285:
1284:
1272:
1266:
1265:
1258:
1252:
1251:
1244:
1238:
1235:
1229:
1226:
1220:
1219:
1193:
1173:
1167:
1166:
1138:
1132:
1131:
1112:10.1002/pds.3384
1095:
1089:
1088:
1081:
1075:
1064:
1058:
1055:
1049:
1046:
1040:
1037:
1031:
1028:
1022:
1018:
1012:
1011:
1001:
983:
958:
952:
949:
943:
940:
934:
931:
925:
922:
916:
913:
904:
901:
895:
892:
886:
883:
877:
874:
868:
865:
859:
855:
849:
846:
837:
834:
825:
824:
814:
796:
772:
766:
763:
754:
751:
740:
737:
700:Network topology
685:Network dynamics
665:Graphical models
551:complex diseases
468:Drug repurposing
244:, having a high
163:complex networks
147:", published in
146:
113:Disease networks
97:network dynamics
93:network topology
85:Network medicine
77:
70:
66:
63:
57:
37:
36:
29:
21:
18:Network Medicine
1330:
1329:
1325:
1324:
1323:
1321:
1320:
1319:
1305:
1304:
1303:
1302:
1293:
1292:
1288:
1273:
1269:
1260:
1259:
1255:
1246:
1245:
1241:
1236:
1232:
1227:
1223:
1174:
1170:
1139:
1135:
1096:
1092:
1083:
1082:
1078:
1072:Halloran, M. E.
1065:
1061:
1056:
1052:
1047:
1043:
1038:
1034:
1029:
1025:
1019:
1015:
968:Genome Medicine
959:
955:
950:
946:
941:
937:
932:
928:
923:
919:
914:
907:
902:
898:
893:
889:
884:
880:
875:
871:
866:
862:
856:
852:
847:
840:
835:
828:
773:
769:
764:
757:
752:
743:
738:
734:
729:
724:
710:Systems biology
690:Network science
650:Complex network
630:
555:systems biology
543:
534:
525:
505:contact tracing
489:epidemic models
485:
439:pharmaceuticals
422:
416:
328:
322:
226:
220:
215:
198:bipartite graph
186:gene regulation
182:disease network
144:
137:
129:systems biology
121:social networks
89:network science
78:
67:
61:
58:
50:help improve it
47:
38:
34:
23:
22:
15:
12:
11:
5:
1328:
1318:
1317:
1315:Network theory
1301:
1300:
1286:
1267:
1253:
1239:
1230:
1221:
1168:
1149:(2): 153–161.
1133:
1106:(2): 130–137.
1090:
1076:
1059:
1050:
1041:
1032:
1023:
1013:
953:
944:
935:
926:
917:
905:
896:
887:
878:
869:
860:
850:
838:
826:
767:
755:
741:
731:
730:
728:
725:
723:
722:
717:
712:
707:
702:
697:
695:Network theory
692:
687:
682:
677:
672:
667:
662:
657:
652:
647:
645:Bioinformatics
642:
637:
631:
629:
626:
614:
613:
598:control theory
591:
577:
542:
539:
533:
532:Other networks
530:
524:
521:
484:
481:
455:drug discovery
418:Main article:
415:
412:
395:
394:
389:between their
378:
366:
324:Main article:
321:
318:
305:disease module
284:cell signaling
238:disassortative
222:Main article:
219:
216:
214:
213:Research areas
211:
159:network theory
136:
133:
80:
79:
41:
39:
32:
9:
6:
4:
3:
2:
1327:
1316:
1313:
1312:
1310:
1296:
1290:
1282:
1278:
1271:
1263:
1257:
1249:
1243:
1234:
1225:
1217:
1213:
1209:
1205:
1201:
1197:
1192:
1187:
1183:
1179:
1172:
1164:
1160:
1156:
1152:
1148:
1144:
1137:
1129:
1125:
1121:
1117:
1113:
1109:
1105:
1101:
1094:
1086:
1080:
1073:
1069:
1063:
1054:
1045:
1036:
1027:
1017:
1009:
1005:
1000:
995:
991:
987:
982:
977:
973:
969:
965:
957:
948:
939:
930:
921:
912:
910:
900:
891:
882:
873:
864:
854:
845:
843:
833:
831:
822:
818:
813:
808:
804:
800:
795:
790:
786:
782:
778:
771:
762:
760:
750:
748:
746:
736:
732:
721:
718:
716:
713:
711:
708:
706:
703:
701:
698:
696:
693:
691:
688:
686:
683:
681:
678:
676:
673:
671:
668:
666:
663:
661:
658:
656:
653:
651:
648:
646:
643:
641:
638:
636:
633:
632:
625:
622:
618:
611:
607:
603:
599:
595:
592:
589:
585:
581:
578:
575:
571:
567:
563:
560:
559:
558:
556:
552:
548:
538:
529:
520:
518:
514:
510:
509:public health
506:
502:
498:
494:
490:
480:
477:
473:
469:
464:
460:
456:
452:
448:
444:
440:
435:
431:
427:
421:
411:
408:
404:
400:
392:
388:
384:
383:
379:
376:
372:
371:
367:
364:
360:
359:
355:
354:
353:
350:
348:
344:
340:
336:
332:
327:
317:
315:
311:
307:
306:
301:
295:
293:
289:
285:
281:
277:
273:
269:
265:
261:
257:
253:
249:
247:
243:
239:
235:
231:
225:
210:
207:
203:
199:
195:
191:
187:
183:
179:
174:
172:
168:
164:
160:
156:
152:
151:
142:
132:
130:
126:
122:
118:
114:
110:
106:
102:
98:
94:
90:
86:
76:
73:
65:
62:November 2014
55:
51:
45:
42:This article
40:
31:
30:
27:
19:
1289:
1280:
1270:
1256:
1242:
1233:
1224:
1181:
1177:
1171:
1146:
1142:
1136:
1103:
1099:
1093:
1079:
1062:
1053:
1044:
1035:
1026:
1016:
971:
967:
956:
947:
938:
929:
920:
899:
890:
881:
872:
863:
853:
784:
780:
770:
735:
705:Pharmacology
660:Graph theory
615:
593:
579:
570:metabolomics
561:
544:
535:
526:
486:
476:side-effects
426:pharmacology
423:
414:Pharmacology
396:
381:
380:
369:
368:
357:
356:
351:
329:
304:
303:
296:
250:
227:
175:
171:biomolecules
148:
138:
117:Epidemiology
84:
83:
68:
59:
43:
26:
1068:Longini, I.
675:Interactome
459:drug design
443:drug target
387:comorbidity
276:metabolites
230:interactome
224:Interactome
218:Interactome
1191:2106.00413
858:1173–1178.
727:References
491:, as many
463:biomarkers
445:occupies.
403:clustering
391:phenotypes
339:metabolite
300:phenotypes
294:networks.
286:networks,
234:scale-free
204:using the
135:Background
103:, such as
1216:235266038
1021:34895945.
990:1756-994X
974:(1): 42.
803:1664-8021
610:biomarker
501:spreading
474:and drug
320:Diseasome
1309:Category
1208:34226152
1163:25531938
1128:42462968
1120:23180729
1008:38509600
999:10956347
821:31031797
628:See also
566:genomics
424:Network
262:and the
812:6470635
787:: 294.
621:Harvard
612:design.
497:obesity
260:BioGRID
48:Please
1214:
1206:
1161:
1126:
1118:
1006:
996:
988:
819:
809:
801:
604:, and
588:asthma
310:module
290:, and
266:. The
192:, and
180:, the
1212:S2CID
1186:arXiv
1124:S2CID
564:uses
256:nodes
202:genes
167:nodes
1204:PMID
1159:PMID
1116:PMID
1004:PMID
986:ISSN
817:PMID
799:ISSN
586:and
584:COPD
568:and
503:and
280:KEGG
206:OMIM
123:and
107:and
95:and
1196:doi
1151:doi
1108:doi
994:PMC
976:doi
807:PMC
789:doi
405:of
341:or
292:RNA
52:to
1311::
1279:.
1210:.
1202:.
1194:.
1182:17
1180:.
1157:.
1147:18
1145:.
1122:.
1114:.
1104:22
1102:.
1070:,
1002:.
992:.
984:.
972:16
970:.
966:.
908:^
841:^
829:^
815:.
805:.
797:.
785:10
783:.
779:.
758:^
744:^
600:,
470:,
457:,
377:).
365:).
349:.
316:.
248:.
240:,
236:,
188:,
131:.
1297:.
1283:.
1264:.
1250:.
1218:.
1198::
1188::
1165:.
1153::
1130:.
1110::
1010:.
978::
823:.
791::
576:.
145:'
75:)
69:(
64:)
60:(
46:.
20:)
Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.