|
- 积分
- 50
- 威望
- 50
- 包包
- 541
|
本帖最后由 细胞海洋 于 2010-7-20 15:26 编辑 7 b( _* ~6 l" n, i
. h7 P& g, j: u8 b7 o
Modern biology has become an information science. Since the invention of a
6 w. ?3 Q* T3 gDNA sequencing method by Sanger in the late seventies, public repositories
$ V1 l" ]4 T7 O) C+ U1 s4 \3 n2 tof genomic sequences have been growing exponentially, doubling in size every) ?9 B3 x. E' B- C; N
16 months—a rate often compared to the growth of semiconductor transistor
0 \( o! n/ o9 g, Ddensities in CPUs known as Moore’s Law. In the nineties, the public–private
% l* k, i. i+ [) h/ S- \% arace to sequence the human genome further intensified the fervor to gener-+ a: i7 d# V( C/ }
ate high-throughput biomolecular data from highly parallel and miniaturized
) ?' ?9 ?/ v3 n2 x$ f# H2 c7 B" uinstruments. Today, sequencing data from thousands of genomes, including. r; v* h' [6 r
plants, mammals, and microbial genomes, are accumulating at an unprece-
8 F$ n6 b/ t/ X3 P" edented rate. The advent of second-generation DNA sequencing instruments,
, F* m3 q" \! i; T2 t7 ]high-density cDNA microarrays, tandem mass spectrometers, and high-power. U. [4 i# j" r2 a
NMRs have fueled the growth of molecular biology into a wide spectrum of N* ~8 P' M. O& W( G9 t- f% d: n
disciplines such as personalized genomics, functional genomics, proteomics,
, q- \& e6 |, ^! |0 B8 hmetabolomics, and structural genomics. Few experiments in molecular biol-
7 q& f# ^ P* X! J$ g# P( J* uogy and genetics performed today can afford to ignore the vast amount of3 O8 k3 l+ C% [/ p. d5 ~. p5 s
biological information publicly accessible. Suddenly, molecular biology and
* f0 O6 ?- ~! ?) [9 f: |; H: S5 Y) Tgenetics have become data rich.0 [/ i8 f- b# n& T
Biological data mining is a data-guzzling turbo engine for postgenomic
1 E: y, p& O0 J ?biology, driving the competitive race toward unprecedented biological discov-2 v/ ]7 X% m5 o* G1 ~; w3 S
ery opportunities in the twenty-first century. Classical bioinformatics emerged
" ~8 N: V9 \- G" Ufrom the study of macromolecules in molecular biology, biochemistry, and
7 D; L( Y" `, G* Lbiophysics. Analysis, comparison, and classification of DNA and protein se-2 W. x# [. ~% v' ^
quences were the dominant themes of bioinformatics in the early nineties.
3 g) U2 l5 r; V' uMachine learning mainly focused on predicting genes and proteins functions4 u {( x( }6 L( `% o8 M- S
from their sequences and structures. The understanding of cellular functions- G. f9 ~; D' `. |$ i
and processes underlying complex diseases were out of reach. Bioinformatics
6 k2 ]4 Y% X& P& O3 hscientists were a rare breed, and their contribution to molecular biology and
2 s/ E" H$ c' E7 v% _. F* jgenetics was considered marginal, because the computational tools available
2 y6 H, {# ^* Z8 ^then for biomolecular data analysis were far more primitive than the array, X I# J: K3 S7 w5 D/ X8 L
of experimental techniques and assays that were available to life scientists.4 A9 o" P% X/ j, D# p& r
Today, we are now witnessing the reversal of these past trends. Diverse sets
' {' X( H6 Q! y' Y' E1 V( |of data types that cover a broad spectrum of genotypes and phenotypes, par-
! v* \) @: f+ y$ Jticularly those related to human health and diseases, have become available.
' @6 p( z1 u; w$ y& I) mMany interdisciplinary researchers, including applied computer scientists, ap-
3 \6 ^% a* S$ Q/ x( n' f4 ^plied mathematicians, biostatisticians, biomedical researchers, clinical scien-1 }) e6 J6 Y( D; \+ s
tists, and biopharmaceutical professionals, have discovered in biology a goldmine of knowledge leading to many exciting possibilities: the unraveling of the) S7 @. n1 s4 M* i% x) u5 z
tree of life, harnessing the power of microbial organisms for renewable energy,
7 l" g1 S* X* ]# L- pfinding new ways to diagnose disease early, and developing new therapeutic
b" E. ]0 `8 I# c3 u; V" |compounds that save lives. Much of the experimental high-throughput biology6 p: ]' Y; e7 B- C7 O
data are generated and analyzed “in haste,” therefore leaving plenty of oppor-
* d2 V* {: q' \tunities for knowledge discovery even after the original data are released. Most9 s0 C7 K* P! V/ k8 y' F" v
of the bets on the race to separate the wheat from the chaff have been placed
* q3 n J' L* oon biological data mining techniques. After all, when easy, straightforward,
+ I& A# H5 B* o( x0 U; Zfirst-pass data analysis has not yielded novel biological insights, data mining
* ?# H5 S% y' Z, y& ktechniques must be able to help—or, many presumed so.
8 ?3 W" ~ w n3 W' M
8 a$ N1 `9 F$ Z[hide][/hide] |
附件: 你需要登录才可以下载或查看附件。没有帐号？注册
-
总评分: 威望 + 5
包包 + 10
查看全部评分
|
发表于 2010-7-20 15:17
