國立高雄大學圖資館 |

Language: English

Back

Branching process models of cancer

Durrett, Richard.

Branching process models of cancer

Record Type:	Electronic resources : Monograph/item
Title/Author:	Branching process models of cancerby Richard Durrett.
Author:	Durrett, Richard.
Published:	Cham :Springer International Publishing :2015.
Description:	vii, 63 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	CancerMathematical models.
Online resource:	http://dx.doi.org/10.1007/978-3-319-16065-8
ISBN:	9783319160658 (electronic bk.)

Branching process models of cancer
Durrett, Richard.

Branching process models of cancer[electronic resource] /by Richard Durrett. - Cham :Springer International Publishing :2015. - vii, 63 p. :ill., digital ;24 cm. - Mathematical biosciences institute lecture series ;v.1. - Mathematical biosciences institute lecture series ;v.1..

This volume develops results on continuous time branching processes and applies them to study rate of tumor growth, extending classic work on the Luria-Delbruck distribution. As a consequence, the authors calculate the probability that mutations that confer resistance to treatment are present at detection and quantify the extent of tumor heterogeneity. As applications, the authors evaluate ovarian cancer screening strategies and give rigorous proofs for results of Heano and Michor concerning tumor metastasis. These notes should be accessible to students who are familiar with Poisson processes and continuous time. Richard Durrett is mathematics professor at Duke University, USA. He is the author of 8 books, over 200 journal articles, and has supervised more than 40 Ph.D. students. Most of his current research concerns the applications of probability to biology: ecology, genetics, and most recently cancer.

ISBN: 9783319160658 (electronic bk.)

Standard No.: 10.1007/978-3-319-16065-8doiSubjects--Topical Terms:

211589
Cancer
--Mathematical models.

LC Class. No.: RC267

Dewey Class. No.: 616.9940015118

Branching process models of cancer
LDR:01919nmm a2200325 a 4500 001 471556
003 DE-He213
005 20160122101554.0
006 m d
007 cr nn 008maaau
008 160223s2015 gw s 0 eng d
020 $a 9783319160658 (electronic bk.)
020 $a 9783319160641 (paper)
024 7 $a 10.1007/978-3-319-16065-8 $2 doi
035 $a 978-3-319-16065-8
040 $a GP $c GP
041 0 $a eng
050 4 $a RC267
072 7 $a PBT $2 bicssc
072 7 $a PBWL $2 bicssc
072 7 $a MAT029000 $2 bisacsh
082 0 4 $a 616.9940015118 $2 23
090 $a RC267 $b .D965 2015
100 1 $a Durrett, Richard. $3 309743
245 1 0 $a Branching process models of cancer $h [electronic resource] / $c by Richard Durrett.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2015.
300 $a vii, 63 p. : $b ill., digital ; $c 24 cm.
490 1 $a Mathematical biosciences institute lecture series ; $v v.1
520 $a This volume develops results on continuous time branching processes and applies them to study rate of tumor growth, extending classic work on the Luria-Delbruck distribution. As a consequence, the authors calculate the probability that mutations that confer resistance to treatment are present at detection and quantify the extent of tumor heterogeneity. As applications, the authors evaluate ovarian cancer screening strategies and give rigorous proofs for results of Heano and Michor concerning tumor metastasis. These notes should be accessible to students who are familiar with Poisson processes and continuous time. Richard Durrett is mathematics professor at Duke University, USA. He is the author of 8 books, over 200 journal articles, and has supervised more than 40 Ph.D. students. Most of his current research concerns the applications of probability to biology: ecology, genetics, and most recently cancer.
650 0 $a Cancer $x Mathematical models. $3 211589
650 0 $a Branching processes. $3 185802
650 1 4 $a Mathematics. $3 184409
650 2 4 $a Probability Theory and Stochastic Processes. $3 274061
650 2 4 $a Mathematical and Computational Biology. $3 514442
650 2 4 $a Cancer Research. $3 273660
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Mathematical biosciences institute lecture series ; $v v.1. $3 726882
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-16065-8
950 $a Mathematics and Statistics (Springer-11649)