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本帖最后由 细胞海洋 于 2010-7-20 15:26 编辑
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Modern biology has become an information science. Since the invention of a
, _1 q) |! E# H. Y4 V6 U% I7 g1 LDNA sequencing method by Sanger in the late seventies, public repositories* Z' q$ l; @! J) a7 |7 Y
of genomic sequences have been growing exponentially, doubling in size every
* i3 M2 W) s d7 r; H% ^16 months—a rate often compared to the growth of semiconductor transistor
/ S1 N% A" O6 q2 T( w4 f! D/ w+ Gdensities in CPUs known as Moore’s Law. In the nineties, the public–private1 p+ |, \5 I9 g" m3 s9 }5 `
race to sequence the human genome further intensified the fervor to gener-
! a1 V+ A: p, Y6 Q* p0 Sate high-throughput biomolecular data from highly parallel and miniaturized" a2 O8 O% n/ z
instruments. Today, sequencing data from thousands of genomes, including
& a" `/ y* G: ~' O) o# L) Qplants, mammals, and microbial genomes, are accumulating at an unprece-
" V2 a; p+ ]# Odented rate. The advent of second-generation DNA sequencing instruments,
9 I+ G; s+ i" `, {( Q" ehigh-density cDNA microarrays, tandem mass spectrometers, and high-power5 ]# ~9 w: u4 r9 K' B8 |
NMRs have fueled the growth of molecular biology into a wide spectrum of: y2 x5 m/ \, D
disciplines such as personalized genomics, functional genomics, proteomics,5 |) f: X/ b. @* j, `; P1 n1 d' A
metabolomics, and structural genomics. Few experiments in molecular biol-3 \. {& O- g! G/ O% {" k. }2 \
ogy and genetics performed today can afford to ignore the vast amount of
4 T' J3 ]6 y# D7 \7 `7 e) C& wbiological information publicly accessible. Suddenly, molecular biology and4 [. ~( s6 ~& n) k) A2 n. l
genetics have become data rich., s& [9 M9 v' f) y9 A
Biological data mining is a data-guzzling turbo engine for postgenomic6 ^' I: c- j/ F M' e. y: P
biology, driving the competitive race toward unprecedented biological discov-
7 l1 a! v/ J0 ? k7 uery opportunities in the twenty-first century. Classical bioinformatics emerged
$ o! O3 W& B7 r* S8 K( J) ifrom the study of macromolecules in molecular biology, biochemistry, and+ x b$ A2 b& L% r
biophysics. Analysis, comparison, and classification of DNA and protein se-
2 u: N3 v$ b- U) j/ w& u' Jquences were the dominant themes of bioinformatics in the early nineties.
; @, E; j8 F. v% a, y( G8 G$ |5 pMachine learning mainly focused on predicting genes and proteins functions* G4 {7 |7 {8 ~ p. N, k9 c1 H
from their sequences and structures. The understanding of cellular functions/ g" O$ _$ |) W4 i2 _
and processes underlying complex diseases were out of reach. Bioinformatics" |! M) W, a7 _" e$ a5 Y. S: v3 v7 H0 [
scientists were a rare breed, and their contribution to molecular biology and: {, n) T: z1 |6 T" |1 L& {
genetics was considered marginal, because the computational tools available
8 m9 p2 \% E! ^2 Ithen for biomolecular data analysis were far more primitive than the array, E p$ K1 r+ c) Z) o7 c
of experimental techniques and assays that were available to life scientists.
9 j2 k3 ?3 E9 o1 XToday, we are now witnessing the reversal of these past trends. Diverse sets# ^7 X# h/ v+ q
of data types that cover a broad spectrum of genotypes and phenotypes, par-# {$ o0 ^" Y' a. U+ Y
ticularly those related to human health and diseases, have become available.3 @! H# |9 \/ ]+ L9 y8 ]# D9 J
Many interdisciplinary researchers, including applied computer scientists, ap-- {0 Z: ^. W* r: C3 G& S: P3 u
plied mathematicians, biostatisticians, biomedical researchers, clinical scien-
0 X- h3 X, Z+ k+ J* g8 e9 Ztists, and biopharmaceutical professionals, have discovered in biology a goldmine of knowledge leading to many exciting possibilities: the unraveling of the
+ p4 }! _' _( y/ v+ U6 ~: M5 ftree of life, harnessing the power of microbial organisms for renewable energy,. ]4 G. i' D3 Q: X$ x, F, @. |
finding new ways to diagnose disease early, and developing new therapeutic
7 m* ~: m6 r" e6 d; [. S2 f, H! bcompounds that save lives. Much of the experimental high-throughput biology
& [1 O1 N1 V2 E, I0 o* G* a3 Qdata are generated and analyzed “in haste,” therefore leaving plenty of oppor-
, }/ k ~2 C6 z5 }tunities for knowledge discovery even after the original data are released. Most
% S! L3 A5 @1 ?; ]0 p$ Mof the bets on the race to separate the wheat from the chaff have been placed
1 b p% @% X# W; D9 U3 u5 k) Gon biological data mining techniques. After all, when easy, straightforward,
1 P' i7 |7 ]7 T; g0 Mfirst-pass data analysis has not yielded novel biological insights, data mining
; y! K9 F8 q ^" Ptechniques must be able to help—or, many presumed so.
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发表于 2010-7-20 15:17
