PDF电子书：Biological.Data.Mining

viogriffin

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  p- I! [  M/ UModern biology has become an information science. Since the invention of a
, V! z! m5 a# {" Q9 HDNA sequencing method by Sanger in the late seventies, public repositories  {1 Y& O  Y) g1 h! Q! D" M
of genomic sequences have been growing exponentially, doubling in size every( g( q/ F+ F- _- y
16 months—a rate often compared to the growth of semiconductor transistor
2 {0 l* A1 c, G; fdensities in CPUs known as Moore’s Law. In the nineties, the public–private
! c* G3 z- Y7 }3 qrace to sequence the human genome further intensified the fervor to gener-6 Y! a: T- f) f! B: ^& ~
ate high-throughput biomolecular data from highly parallel and miniaturized' p7 h' a& c. ?" Y/ j" z
instruments. Today, sequencing data from thousands of genomes, including
- q; e! a5 A0 H9 J3 p7 mplants, mammals, and microbial genomes, are accumulating at an unprece-2 r  ?, C! h! a% j4 @
dented rate. The advent of second-generation DNA sequencing instruments,3 F. }% z1 g' Q4 {/ n( x$ o9 v
high-density cDNA microarrays, tandem mass spectrometers, and high-power
) i9 Q6 r8 O# NNMRs have fueled the growth of molecular biology into a wide spectrum of
6 a3 P4 H. p& ~% \. G+ cdisciplines such as personalized genomics, functional genomics, proteomics,' t! v' w' L! Q" X7 f/ p
metabolomics, and structural genomics. Few experiments in molecular biol-
6 t3 ^6 F% |% v0 T6 l9 E$ O7 b+ nogy and genetics performed today can afford to ignore the vast amount of/ D, B* J; C2 r9 r
biological information publicly accessible. Suddenly, molecular biology and- P8 |! _3 _+ v
genetics have become data rich.
' f8 H$ E" p$ e! n. N, X; PBiological data mining is a data-guzzling turbo engine for postgenomic$ h# t1 [/ ^* `9 R
biology, driving the competitive race toward unprecedented biological discov-
3 z- a7 C7 H' g& M( j3 }ery opportunities in the twenty-first century. Classical bioinformatics emerged
4 z4 |6 ?" O* S  [from the study of macromolecules in molecular biology, biochemistry, and; V& z  ]. i/ o6 ?+ Y+ R
biophysics. Analysis, comparison, and classification of DNA and protein se-3 B  V3 c/ F8 G
quences were the dominant themes of bioinformatics in the early nineties., D/ L8 a8 w: C' b
Machine learning mainly focused on predicting genes and proteins functions4 l' b' C4 Y) e- I2 N) o) X5 S
from their sequences and structures. The understanding of cellular functions8 }$ f# t1 _7 z+ K' A
and processes underlying complex diseases were out of reach. Bioinformatics
  a" _4 D& i7 x1 N( i1 _* wscientists were a rare breed, and their contribution to molecular biology and
3 |8 s# c$ G8 T6 y7 y6 h9 ^genetics was considered marginal, because the computational tools available
) {8 T3 B6 m$ ?9 ?5 M7 b6 _- sthen for biomolecular data analysis were far more primitive than the array9 t- {6 u1 A) T& ]3 F4 C$ ~& \
of experimental techniques and assays that were available to life scientists./ v, A6 U8 |9 k( w4 p
Today, we are now witnessing the reversal of these past trends. Diverse sets
2 F- @2 e# Z- y1 Dof data types that cover a broad spectrum of genotypes and phenotypes, par-
3 C- a. x% C9 t% E% \- {1 {, cticularly those related to human health and diseases, have become available.
/ V% i9 l4 [2 @' o* B' u; ^7 K5 BMany interdisciplinary researchers, including applied computer scientists, ap-
7 d$ V7 z' i, B# i3 J9 N7 E3 iplied mathematicians, biostatisticians, biomedical researchers, clinical scien-2 |' {; V0 a0 i$ \3 F3 H$ Q
tists, and biopharmaceutical professionals, have discovered in biology a goldmine of knowledge leading to many exciting possibilities: the unraveling of the
/ h9 m7 Y6 O* [2 o8 ^tree of life, harnessing the power of microbial organisms for renewable energy,
7 @: V9 T+ O1 F9 G) G* X% p2 Efinding new ways to diagnose disease early, and developing new therapeutic7 k+ ~" i7 h% j3 d
compounds that save lives. Much of the experimental high-throughput biology" w9 n, O; B8 {" O1 K$ v
data are generated and analyzed “in haste,” therefore leaving plenty of oppor-: Q! \3 i& r: T- R' ]% Z
tunities for knowledge discovery even after the original data are released. Most8 |: v- U) Z2 P5 i# S
of the bets on the race to separate the wheat from the chaff have been placed) q5 i8 t! p3 w% x3 ?7 A
on biological data mining techniques. After all, when easy, straightforward,- ]+ s/ b3 \+ A; o# m: I
first-pass data analysis has not yielded novel biological insights, data mining$ }, M) C% l8 i1 j6 \7 H& S8 q
techniques must be able to help—or, many presumed so.
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