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本帖最后由 细胞海洋 于 2010-7-20 15:26 编辑 / e/ p- F: _4 ~* X8 ]
" w! k. x. P: n6 o/ Z6 bModern biology has become an information science. Since the invention of a9 Z; H# j, J5 P7 g% b* w* Y
DNA sequencing method by Sanger in the late seventies, public repositories$ [! w0 G: u' l) @# s) a' E
of genomic sequences have been growing exponentially, doubling in size every% J0 Y% M& i7 W3 L5 p' \
16 months—a rate often compared to the growth of semiconductor transistor
4 }# t; ]% c0 odensities in CPUs known as Moore’s Law. In the nineties, the public–private
1 f1 `9 i3 P8 |6 W6 J, @0 ?race to sequence the human genome further intensified the fervor to gener-
7 R, I6 g4 d, }$ c6 H* y7 m# G, [ate high-throughput biomolecular data from highly parallel and miniaturized' F, H) ?, _$ l9 m+ l: y' B
instruments. Today, sequencing data from thousands of genomes, including0 J' P# K& a$ f. F* y
plants, mammals, and microbial genomes, are accumulating at an unprece-
( N! `5 s8 Q4 r; u( rdented rate. The advent of second-generation DNA sequencing instruments,, o7 d; c6 @. ]
high-density cDNA microarrays, tandem mass spectrometers, and high-power
5 T$ H& {; G/ Q, F: D5 RNMRs have fueled the growth of molecular biology into a wide spectrum of/ z4 w9 Y g- h! @
disciplines such as personalized genomics, functional genomics, proteomics,' a: r( ^1 n y/ ?& g3 d" K
metabolomics, and structural genomics. Few experiments in molecular biol-
7 W A6 c1 e( I& ~ogy and genetics performed today can afford to ignore the vast amount of
) V. P. y# C! q1 i0 Z$ ibiological information publicly accessible. Suddenly, molecular biology and9 M2 K0 t: L5 C; \! v9 t4 R8 J
genetics have become data rich.
/ V' ]. z% E+ i6 v) j, pBiological data mining is a data-guzzling turbo engine for postgenomic! K2 E. b* _' v1 x
biology, driving the competitive race toward unprecedented biological discov-
6 v8 \4 e! M/ }" d- }! w' Y- Cery opportunities in the twenty-first century. Classical bioinformatics emerged
, C* H9 g# T8 a# S. u$ w, C( O, Mfrom the study of macromolecules in molecular biology, biochemistry, and# }. K" Z, C7 H
biophysics. Analysis, comparison, and classification of DNA and protein se-
& [% g8 }$ a# ?- Oquences were the dominant themes of bioinformatics in the early nineties.6 D1 Q* ?' y& d2 ^" J1 [. m4 I
Machine learning mainly focused on predicting genes and proteins functions
' u- L$ u V6 v0 |- p8 I( Afrom their sequences and structures. The understanding of cellular functions* h1 z) P* h& }4 ~0 r4 o7 r' w3 b
and processes underlying complex diseases were out of reach. Bioinformatics5 I0 L4 R- n0 b4 L6 }/ r
scientists were a rare breed, and their contribution to molecular biology and8 s; U4 J4 j; }$ a8 i7 b0 g; c2 M
genetics was considered marginal, because the computational tools available
5 R& C* G5 o" U0 Z B2 w/ O: c7 V! ]then for biomolecular data analysis were far more primitive than the array
' v, r! C. L3 l8 h P$ r; N& S/ }of experimental techniques and assays that were available to life scientists. d1 D. t! `% U2 n
Today, we are now witnessing the reversal of these past trends. Diverse sets
, P5 j1 m. N- R4 yof data types that cover a broad spectrum of genotypes and phenotypes, par-, A( M1 ^) G5 _ r9 `6 B% l
ticularly those related to human health and diseases, have become available." q; J2 q& F8 |7 m+ |
Many interdisciplinary researchers, including applied computer scientists, ap-
/ e) K$ v8 ^7 C( d) L, Jplied mathematicians, biostatisticians, biomedical researchers, clinical scien-% m5 y$ a+ ?& J4 O6 R; P
tists, and biopharmaceutical professionals, have discovered in biology a goldmine of knowledge leading to many exciting possibilities: the unraveling of the
5 C9 x$ F/ M8 e2 s8 qtree of life, harnessing the power of microbial organisms for renewable energy,6 v2 p: u% m: w8 J
finding new ways to diagnose disease early, and developing new therapeutic
5 ^) t9 O5 X3 q! h+ acompounds that save lives. Much of the experimental high-throughput biology
" c, ^, v9 ?' J- U" e; W# Fdata are generated and analyzed “in haste,” therefore leaving plenty of oppor-4 y3 t4 [1 v! {" F& b
tunities for knowledge discovery even after the original data are released. Most
& c" _# k6 A8 ~1 {of the bets on the race to separate the wheat from the chaff have been placed9 k* k" P8 Y. H, I
on biological data mining techniques. After all, when easy, straightforward,
: t7 I2 `( I2 U6 wfirst-pass data analysis has not yielded novel biological insights, data mining1 a5 R/ X( ~2 k" @/ [
techniques must be able to help—or, many presumed so.
5 ] T: J# W- R/ Q! y5 B/ {. w8 b6 @: r" L5 E: t
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发表于 2010-7-20 15:17
