Comput Math Methods Med - Modeling and visualizing cell type switching.

Tópicos

{ model(2220) cell(1177) simul(1124) }
{ state(1844) use(1261) util(961) }
{ structur(1116) can(940) graph(676) }
{ gene(2352) biolog(1181) express(1162) }
{ method(1969) cluster(1462) data(1082) }
{ data(1714) softwar(1251) tool(1186) }
{ model(2341) predict(2261) use(1141) }
{ analysi(2126) use(1163) compon(1037) }
{ imag(2830) propos(1344) filter(1198) }
{ network(2748) neural(1063) input(814) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ drug(1928) target(777) effect(648) }
{ framework(1458) process(801) describ(734) }
{ chang(1828) time(1643) increas(1301) }
{ risk(3053) factor(974) diseas(938) }
{ visual(1396) interact(850) tool(830) }
{ use(2086) technolog(871) perceiv(783) }
{ method(2212) result(1239) propos(1039) }
{ system(1976) rule(880) can(841) }
{ method(1219) similar(1157) match(930) }
{ algorithm(1844) comput(1787) effici(935) }
{ method(1557) propos(1049) approach(1037) }
{ control(1307) perform(991) simul(935) }
{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
{ perform(999) metric(946) measur(919) }
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{ import(1318) role(1303) understand(862) }
{ studi(1119) effect(1106) posit(819) }
{ model(3480) simul(1196) paramet(876) }
{ ehr(2073) health(1662) electron(1139) }
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{ detect(2391) sensit(1101) algorithm(908) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
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{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ can(981) present(881) function(850) }
{ health(1844) social(1437) communiti(874) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }

Resumo

Understanding cellular differentiation is critical in explaining development and for taming diseases such as cancer. Differentiation is conventionally represented using bifurcating lineage trees. However, these lineage trees cannot readily capture or quantify all the types of transitions now known to occur between cell types, including transdifferentiation or differentiation off standard paths. This work introduces a new analysis and visualization technique that is capable of representing all possible transitions between cell states compactly, quantitatively, and intuitively. This method considers the regulatory network of transcription factors that control cell type determination and then performs an analysis of network dynamics to identify stable expression profiles and the potential cell types that they represent. A visualization tool called CellDiff3D creates an intuitive three-dimensional graph that shows the overall direction and probability of transitions between all pairs of cell types within a lineage. In this study, the influence of gene expression noise and mutational changes during myeloid cell differentiation are presented as a demonstration of the CellDiff3D technique, a new approach to quantify and envision all possible cell state transitions in any lineage network.

Resumo Limpo

understand cellular differenti critic explain develop tame diseas cancer differenti convent repres use bifurc lineag tree howev lineag tree readili captur quantifi type transit now known occur cell type includ transdifferenti differenti standard path work introduc new analysi visual techniqu capabl repres possibl transit cell state compact quantit intuit method consid regulatori network transcript factor control cell type determin perform analysi network dynam identifi stabl express profil potenti cell type repres visual tool call celldiffd creat intuit threedimension graph show overal direct probabl transit pair cell type within lineag studi influenc gene express nois mutat chang myeloid cell differenti present demonstr celldiffd techniqu new approach quantifi envis possibl cell state transit lineag network

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