[主题活动] 【CASK EFFECT】0910F阅读全方位锻炼--越障【SCI】 1-1 [复制链接]

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发表于 2009-7-10 17:02:12 |只看该作者 |倒序浏览

【CASK EFFECT】0910G阅读全方位锻炼--难度【LSAT】汇总贴
【CASK EFFECT】0910G阅读全方位锻炼--速度【CET】汇总贴
【CASK EFFECT】0910F阅读全方位锻炼--越障【SCI】汇总贴
【CASK EFFECT】0910G阅读全方位锻炼--真题【GRE】（后期推出）
【CASK EFFECT】0910G阅读全方位锻炼--深度【FICTION】（后期推出）
【CASK EFFECT】0910F阅读全方位锻炼--RAM 汇总贴（后期推出）

规则：

我每天贴出1000字左右的一篇文字（从我平时看的书或者paper里摘的）

没有别的要求，只要大家坚持读完就可以

如果你能坚持一个月，你会发现自己的阅读进化了~

[注]
1、直接在电脑屏幕面前做，虽然GRE阅读是在纸上考，但是这个过程会遏制你做笔记，同时给你的阅读造成视觉障碍，也就是把难度训练和抗干扰训练同步结合，增加效率（初期会很累，但是既然大家想要成为高手，那么就别对自己太温柔）
2、不用苛求速度，看完即可

In the early 1990s when one of us was teaching his first bioinformatics class,
he was not sure that there would be enough students to teach. Although
the Smith-Waterman and BLAST algorithms had already been developed
they had not become the household names among biologists that they are
today. Even the term “bioinformatics” had not yet been coined. DNA arrays
were viewed by most as intellectual toys with dubious practical application,
except for a handful of enthusiasts who saw a vast potential in the technology.
A few bioinformaticians were developing new algorithmic ideas for
nonexistent data sets: David Sankoff laid the foundations of genome rearrangement
studies at a time when there was practically no gene order data,
Michael Waterman and Gary Stormo were developing motif finding algorithms
when there were very few promoter samples available, Gene Myers
was developing sophisticated fragment assembly tools when no bacterial
genome has been assembled yet, andWebbMiller was dreaming about comparing
billion-nucleotide-long DNA sequences when the 172, 282-nucleotide
Epstein-Barr virus was the longest GenBank entry. GenBank itself just recently
made a transition from a series of bound (paper!) volumes to an electronic
database on magnetic tape that could be sent to scientists worldwide.
One has to go back to the mid-1980s and early 1990s to fully appreciate the
revolution in biology that has taken place in the last decade. However, bioinformatics
has affected more than just biology—it has also had a profound
impact on the computational sciences. Biology has rapidly become a large
source of new algorithmic and statistical problems, and has arguably been
the target for more algorithms than any of the other fundamental sciences.
This link between computer science and biology has important educational
implications that change the way we teach computational ideas to biologists,
as well as how applied algorithmics is taught to computer scientists.
For many years computer science was taught to only computer scientists,
and only rarely to students fromother disciplines. A biology student in an algorithms
classwould be a surprising and unlikely (though entirelywelcome)
guest in the early 1990s. But these things change; many biology students
now take some sort of Algorithms 101. At the same time, curious computer
science students often take Genetics 101 and Bioinformatics 101. Although
these students are still relatively rare, keep in mind that the number of bioinformatics
classes in the early 1990swas so small as to be considered nonexistent.
But that number is not so small now. We envision that undergraduate
bioinformatics classes will become a permanent component at every major
university. This is a feature, not a bug.
This is an introductory textbook on bioinformatics algorithms and the computational
ideas that have driven them through the last twenty years. There
are many important probabilistic and statistical techniques that we do not
cover, nor do we cover many important research questions that bioinformaticians
are currently trying to answer. We deliberately do not cover all
areas of computational biology; for example, important topics like protein
folding are not even discussed. The very first bioinformatics textbooks were
Waterman, 1995 (108), which contains excellent coverage of DNA statistics
and Gusfield, 1997 (44) which includes an encyclopedia of string algorithms.
Durbin et al., 1998 (31) and Baldi and Brunak, 1997 (7) emphasize Hidden
Markov Models and machine learning techniques; Baxevanis and Ouellette,
1998 (10) is an excellent practical guide to bioinformatics; Mount, 2001 (76)
excels in showing the connections between biological problems and bioinformatics
techniques; and Bourne and Weissig, 2002 (15) focuses on protein
bioinformatics. There are also excellent web-based lecture notes for many
bioinformatics courses and we learned a lot about the pedagogy of bioinformatics
from materials on the World Wide Web by Serafim Batzoglou, Dick
Karp, Ron Shamir, Martin Tompa, and others.

Website
We have created an extensive website to accompany this book at
http://www.bioalgorithms.info
This website contains a number of features that complement the book. For
example, though this book does not contain a glossary, we provide this service,
a searchable index, and a set of community message boards, at the
above web address. Technically savvy students can also download practical bioinformatics exercises, sample implementations of the algorithms in this
book, and sample data to test them with. Instructors and students may find
the prepackaged lecture notes on the website to be especially helpful. It is
our hope that this website be used as a repository of information that will
help introduce students to the diverse world of bioinformatics.
Acknowledgements
We are indebted to those who kindly agreed to be featured in the biographical
sketches scattered throughout the book. Their insightful and heartfelt
responses definitely made these the most interesting part of this book. Their
life stories and views of the challenges that lay ahead will undoubtedly inspire
students in the exploration of the unknown. There are many more scientists
whose bioboxes we would like to have in this book and it is only
the page limit (which turned out to be 200 pages too small) that prevented
us from commissioning more of them. Special thanks go to Ethan Bier who
inspired us to include biographical sketches in this book.
This book would not have been possible without the diligent teaching assistants
in bioinformatics courses taught during the winter and fall of 2003
and 2004: Derren Barken, Bryant Forsgren, Eugene Ke, ColemanMosley, and
Degui Zhi all helped find technical errors, refine practical exercises, and design
problems in the book. Helen Wu and John Allison spent many hours
making technical figures, which is a thankless task like no other. We are also
grateful to Vagisha Sharma who was kind enough to read the book from
cover to cover and provide insightful comments and, unfortunately, bugs in
the pseudocode. Steve Wasserman provided us with invaluable comments
from a biologist’s point of view that eventually led to new sections in the
book. Alkes Price and Haixu Tang pointed out ambiguities and helped clarify
the chapters on graphs and clustering. Ben Raphael and Patricia Jones
provided feedback on the early chapters and helped avoid some potential
misunderstandings. Dan Gilbert, of Dan Gilbert Art Group, Inc. kindly provided
uswith Triazzles to illustrate the problems of DNAsequence assembly.
Our special thanks go to Randall Christopher, the artist behind the website
www.kleemanandmike.com. Randall illustrated the book and designed
many unique graphical representations of some bioinformatics algorithms.
It has been a pleasure to work with Robert Prior of The MIT Press. With
sufficient patience and prodding, he managed to keep us on track. We also
appreciate the meticulous copyediting of G. W. Helfrich. Finally, we thank the many students in different undergraduate and graduate
bioinformatics classes at UCSD who provided comments on earlier versions
of this book.
PAP would like to thank several people who taught him different aspects
of computational molecular biology. Andrey Mironov taught him that common
sense is perhaps the most important ingredient of any applied research.
Mike Waterman was a terrific teacher at the time PAP moved from Moscow
to Los Angeles, both in science and in life. PAP also thanks Alexander Karzanov,
who taught him combinatorial optimization, which, surprisingly, remains
the most useful set of skills in his computational biology research. He
especially thanks Mark Borodovsky who convinced him to switch into the
field of bioinformatics in 1985, when it was an obscure discipline with an
uncertain future.
PAP also thanks his former students, postdocs, and lab members who
taught him most of what he knows: Vineet Bafna, Guillaume Bourque, Sridhar
Hannenhalli, Steffen Heber, Earl Hubbell, Uri Keich, Zufar Mulyukov,
Alkes Price, Ben Raphael, Sing-Hoi Sze, Haixu Tang, and Glenn Tesler.
NCJwould like to thank hismentors during undergraduate school—Toshihiko
Takeuchi, Harry Gray, John Baldeschwieler, and Schubert Soares—for
patiently but firmly teaching him that persistence is one of the more important
ingredients in research. Also, he thanks the admissions committee at the
University of California, San Diego who gambled on a chemist-turned-programmer,
hopefully for the best.
Neil Jones and Pavel Pevzner
La Jolla, California, 2004