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<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/43323?</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42552/bioinformatics-workbook</guid>
	<pubDate>Tue, 05 Jan 2021 22:42:32 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42552/bioinformatics-workbook</link>
	<title><![CDATA[bioinformatics workbook]]></title>
	<description><![CDATA[<p><span>This books assumes that the reader has some knowledge of biology and basic understanding of the Unix command line. However, for the beginner, the appendix contains introductory material and tips/tricks for common bioinformatic problems, that is referred to for more information throughout the book.</span></p>
<p>https://bioinformaticsworkbook.org/</p><p>Address of the bookmark: <a href="https://bioinformaticsworkbook.org/" rel="nofollow">https://bioinformaticsworkbook.org/</a></p>]]></description>
	<dc:creator>biogeek</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27463/bpipe-a-tool-for-running-and-managing-bioinformatics-pipelines</guid>
	<pubDate>Sat, 21 May 2016 22:42:16 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27463/bpipe-a-tool-for-running-and-managing-bioinformatics-pipelines</link>
	<title><![CDATA[Bpipe - a tool for running and managing bioinformatics pipelines]]></title>
	<description><![CDATA[<p>Bpipe provides a platform for running big bioinformatics jobs that consist of a series of processing stages - known as 'pipelines'.</p>
<ul>
<li>January 20th, 2016 - New! Bpipe 0.9.9 released!</li>
<li>Download <a href="http://download.bpipe.org/versions/bpipe-0.9.9.tar.gz">latest</a>, <a href="http://download.bpipe.org">all</a></li>
<li><a href="http://docs.bpipe.org">Documentation</a></li>
<li><a href="https://groups.google.com/forum/#%21forum/bpipe-discuss">Mailing List</a> (Google Group)</li>
</ul>
<p>Bpipe has been published in <a href="http://bioinformatics.oxfordjournals.org/content/early/2012/04/11/bioinformatics.bts167.abstract">Bioinformatics</a>! If you use Bpipe, please cite:</p>
<p><em>Sadedin S, Pope B &amp; Oshlack A, Bpipe: A Tool for Running and Managing Bioinformatics Pipelines, Bioinformatics</em></p><p>Address of the bookmark: <a href="http://docs.bpipe.org/" rel="nofollow">http://docs.bpipe.org/</a></p>]]></description>
	<dc:creator>Radha Agarkar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/file/view/29601/statistics-using-r-with-biological-examples</guid>
	<pubDate>Thu, 03 Nov 2016 04:55:41 -0500</pubDate>
	<link>https://bioinformaticsonline.com/file/view/29601/statistics-using-r-with-biological-examples</link>
	<title><![CDATA[Statistics Using R   with Biological Examples]]></title>
	<description><![CDATA[<p>This book is a manifestation of my desire to teach researchers in biology a bit more about statistics than an ordinary introductory course covers and to introduce the utilization of R as a tool for analyzing their data. My goal is to reach those with little or no training in higher level statistics so that they can do more of their own data analysis, communicate more with statisticians, and appreciate the great potential statistics has to offer as a tool to answer biological questions. </p><p>This is necessary in light of the increasing use of higher level statistics in biomedical research. I hope it accomplishes this mission and encourage its free distribution and use as a course text or supplement.</p><p>K Seefeld, May 2007</p>]]></description>
	<dc:creator>Neel</dc:creator>
	<enclosure url="https://bioinformaticsonline.com/file/download/29601" length="4581031" type="application/pdf" />
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/2492/plos-computational-biology-translational-bioinformatics-educational-resources</guid>
	<pubDate>Fri, 16 Aug 2013 12:24:56 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/2492/plos-computational-biology-translational-bioinformatics-educational-resources</link>
	<title><![CDATA[PLOS Computational Biology: Translational Bioinformatics educational resources]]></title>
	<description><![CDATA[<p>PLOS present collection of Education articles:&nbsp; &ldquo;Translational Bioinformatics&rdquo;. This collection is presented as an online &ldquo;book&rdquo; which could serve as a reference tool for a graduate level introductory course, marking a step in an exciting new direction for the Education section of the journal.</p>
<p>Blog : http://blogs.plos.org/biologue/2012/12/28/translational-bioinformatics-plos-computational-biology-presents-an-educational-resource-for-an-emerging-field/</p>
<p>Educational Material : http://www.ploscollections.org/article/browseIssue.action?issue=info:doi/10.1371/issue.pcol.v03.i11</p><p>Address of the bookmark: <a href="http://www.ploscollections.org/article/browseIssue.action?issue=info:doi/10.1371/issue.pcol.v03.i11" rel="nofollow">http://www.ploscollections.org/article/browseIssue.action?issue=info:doi/10.1371/issue.pcol.v03.i11</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42672/introduction-to-bioinformatics-and-computational-biology</guid>
	<pubDate>Mon, 25 Jan 2021 01:32:30 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42672/introduction-to-bioinformatics-and-computational-biology</link>
	<title><![CDATA[Introduction to Bioinformatics and Computational Biology]]></title>
	<description><![CDATA[<p><span>This is the course material for STAT115/215 BIO/BST282 at Harvard University.</span></p>
<p>Xiaole Shirley Liu (lead instructor)<br>Joshua Starmer<br>Martin Hemberg<br>Ting Wang<br>Feng Yue</p>
<p>Ming Tang<br>Yang Liu<br>Jack Kang<br>Scarlett Ge<br>Jiazhen Rong<br>Phillip Nicol<br>Maartin De Vries</p>
<p>We thank many colleagues in the community, who helped Dr.&nbsp;Liu in prepare the STAT115/215 BIO/BST282 course over the years.&nbsp;</p><p>Address of the bookmark: <a href="https://liulab-dfci.github.io/bioinfo-combio/" rel="nofollow">https://liulab-dfci.github.io/bioinfo-combio/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/42815/bioinformatics-in-africa-part7-tunisia</guid>
	<pubDate>Sat, 06 Feb 2021 21:25:09 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/42815/bioinformatics-in-africa-part7-tunisia</link>
	<title><![CDATA[Bioinformatics in Africa: Part7 - Tunisia]]></title>
	<description><![CDATA[<p>Institut Pasteur de Tunis (IPT):<br />The IPT is a research institution founded in 1883. IPT is under the supervision of the Ministry of &nbsp;Health and is part of the Universit&eacute; El Manar of Tunis (Ministry of high Education). The missions &nbsp;of the institute are: Public Health Laboratory activities (PHL), Research on infectious diseases, and &nbsp;R/D on vaccines. Research programs are mainly oriented towards local health problems such as &nbsp;leishmaniais, viral hepatitis, and scorpion venoms. The &nbsp; group &nbsp; of &nbsp; Bioinformatics &nbsp; and &nbsp; Modelling &nbsp; of &nbsp; the &nbsp; IPT &nbsp; is &nbsp; hosted &nbsp; by &nbsp; the &nbsp;Laboratoire &nbsp;d&rsquo;Immunopathologie Vaccinologie et G&eacute;n&eacute;tique Mol&eacute;culaire &nbsp;(LIVGM), and exists since the &nbsp;beginning of 2005. Its present research activities include: genome annotation, EST clustering and &nbsp;modelling of the host/parasite response to Leishmania infection. It consists of two senior scientists, &nbsp;two PhD students and one MSc student</p><p>Centre&nbsp;de&nbsp;Biotechnology&nbsp;de&nbsp;Sfax&nbsp;(CBS):<br />Bioinformatics&nbsp;activity&nbsp;started&nbsp;at&nbsp;CBS&nbsp;in&nbsp;2001&nbsp;with&nbsp;the&nbsp;setting&shy;up&nbsp;of&nbsp;a&nbsp;research&nbsp;and&nbsp;service&nbsp;unit&nbsp;of&nbsp; bioinformatics.&nbsp;This&nbsp;unit&nbsp;currently&nbsp;includes&nbsp;one&nbsp;senior&nbsp;researcher,&nbsp;one&nbsp;engineer&nbsp;and&nbsp;four&nbsp;Phd&nbsp; students.&nbsp;Activities&nbsp;include&nbsp;sequence&nbsp;annotation&nbsp;(service)&nbsp;and&nbsp;three&nbsp;research&nbsp;programs:&nbsp;ab&nbsp;initio&nbsp; prediction&nbsp;of&nbsp;short&nbsp;eukaryote&nbsp;genes,&nbsp;statistical&nbsp;modelling&nbsp;by&nbsp;Bayesian&nbsp;networks&nbsp;approach&nbsp;of&nbsp;signal&nbsp; transduction&nbsp;pathways&nbsp;and&nbsp;statistical&nbsp;analysis&nbsp;of&nbsp;human&nbsp;sequence&nbsp;variation&nbsp;data&nbsp;(haplotype&nbsp; reconstruction&nbsp;and&nbsp;linkage&nbsp;disequilibrium).&nbsp;Activities&nbsp;of&nbsp;the&nbsp;Bioinformatics&nbsp;unit&nbsp;could&nbsp;be&nbsp;found&nbsp;at&nbsp; the&nbsp;website:&nbsp;http://www.cbs.rnrt.tn/&nbsp;and&nbsp;the&nbsp;research&nbsp;activity&nbsp;report&nbsp;is&nbsp;available&nbsp;under&nbsp;request&nbsp;to&nbsp; Bioinformatics@cbs.rnrt.tn.&nbsp;Although&nbsp;the&nbsp;computing&nbsp;facilities&nbsp;are&nbsp;good,&nbsp;there&nbsp;is&nbsp;still&nbsp;a&nbsp;need&nbsp;for&nbsp; trained&nbsp;human&nbsp;resources&nbsp;to&nbsp;strengthen&nbsp;bioinformatics&nbsp;capacities&nbsp;at&nbsp;CBS,&nbsp;particularly&nbsp;in&nbsp;structural&nbsp; bioinformatics.</p><p>Web site and links: http://www.cbs.rnrt.tn</p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/43272/bioinformatics-head-bioinformatics-manager-iii-cancer-genomics-research-laboratory-at-frederick-national-laboratory</guid>
  <pubDate>Wed, 18 Aug 2021 00:19:48 -0500</pubDate>
  <link></link>
  <title><![CDATA[Bioinformatics Head (Bioinformatics Manager III), Cancer Genomics Research Laboratory at  Frederick National Laboratory]]></title>
  <description><![CDATA[
<p>Frederick National Laboratory seeking an enthusiastic, creative, and seasoned bioinformatics professional to join our leadership team and direct the exceptional Bioinformatics Group at the Cancer Genomics Research Laboratory (CGR).  CGR has a diverse team of bioinformatics and computational scientists that support all areas of bioinformatics and data analysis (infrastructure, data QC, pipeline development and maintenance, data curation and sharing, methodology development, statistical analyses, machine learning approaches, and scientific interpretation).</p>

<p>More at https://leidosbiomed.csod.com/ats/careersite/jobdetails.aspx?site=4&amp;c=leidosbiomed&amp;id=2040</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/35534/awk-for-bioinformatician-and-computational-biologist</guid>
	<pubDate>Tue, 06 Feb 2018 14:54:35 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/35534/awk-for-bioinformatician-and-computational-biologist</link>
	<title><![CDATA[Awk for Bioinformatician and computational biologist]]></title>
	<description><![CDATA[<p>Awk is a programming language which allows easy manipulation of structured data and is mostly used for pattern scanning and processing. It searches one or more files to see if they contain lines that match with the specified patterns and then perform associated actions. The basic syntax is:</p><blockquote><p><br />awk '/pattern1/ {Actions}<br /> /pattern2/ {Actions}' file</p></blockquote><p><br />The working of Awk is as follows<br />Awk reads the input files one line at a time.<br />For each line, it matches with given pattern in the given order, if matches performs the corresponding action.<br />If no pattern matches, no action will be performed.<br />In the above syntax, either search pattern or action are optional, But not both.<br />If the search pattern is not given, then Awk performs the given actions for each line of the input.<br />If the action is not given, print all that lines that matches with the given patterns which is the default action.<br />Empty braces with out any action does nothing. It wont perform default printing operation.<br />Each statement in Actions should be delimited by semicolon.<br />Say you have data.tsv with the following contents:</p><p><br />$ cat data/test.tsv<br />contig1 ACTGTCTGTCACTGTGTTGTGATGTTGTGTGTG<br />contig2 ACTTTATATATT<br />contig3 ACTTATATATATATA<br />contig4 ACTTATATATATATA<br />contig5 ACTTTATATATT <br />By default Awk prints every line from the file.</p><p><br />$ awk '{print;}' data/test.tsv<br />contig1 ACTGTCTGTCACTGTGTTGTGATGTTGTGTGTG<br />contig2 ACTTTATATATT<br />contig3 ACTTATATATATATA<br />contig4 ACTTATATATATATA<br />contig5 ACTTTATATATT <br />We print the line which matches the pattern contig3</p><p><br />$ awk '/contig3/' data/test.tsv<br />contig3 ACTTATATATATATA<br />Awk has number of builtin variables. For each record i.e line, it splits the record delimited by whitespace character by default and stores it in the $n variables. If the line has 5 words, it will be stored in $1, $2, $3, $4 and $5. $0 represents the whole line. NF is a builtin variable which represents the total number of fields in a record.</p><p><br />$ awk '{print $1","$2;}' data/test.tsv<br />contig1,ACTGTCTGTCACTGTGTTGTGATGTTGTGTGTG<br />contig2,ACTTTATATATT<br />contig3,ACTTATATATATATA<br />contig4,ACTTATATATATATA<br />contig5,ACTTTATATATT</p><p>$ awk '{print $1","$NF;}' data/test.tsv<br />contig1,ACTGTCTGTCACTGTGTTGTGATGTTGTGTGTG<br />contig2,ACTTTATATATT<br />contig3,ACTTATATATATATA<br />contig4,ACTTATATATATATA<br />contig5,ACTTTATATATT</p><p><br />Awk has two important patterns which are specified by the keyword called BEGIN and END. The syntax is as follows:</p><blockquote><p>BEGIN { Actions before reading the file}<br />{Actions for everyline in the file} <br />END { Actions after reading the file }</p></blockquote><p><br />For example,<br />$ awk 'BEGIN{print "Header,Sequence"}{print $1","$2;}END{print "-------"}' data/test.tsv<br />Header,Sequence<br />contig1,ACTGTCTGTCACTGTGTTGTGATGTTGTGTGTG<br />contig2,ACTTTATATATT<br />contig3,ACTTATATATATATA<br />contig4,ACTTATATATATATA<br />contig5,ACTTTATATATT<br />------- <br />We can also use the concept of a conditional operator in print statement of the form print CONDITION ? PRINT_IF_TRUE_TEXT : PRINT_IF_FALSE_TEXT. For example, in the code below, we identify sequences with lengths &gt; 14:</p><p>$ awk '{print (length($2)&gt;14) ? $0"&gt;14" : $0"&lt;=14";}' data/test.tsv<br />contig1 ACTGTCTGTCACTGTGTTGTGATGTTGTGTGTG&gt;14<br />contig2 ACTTTATATATT&lt;=14<br />contig3 ACTTATATATATATA&gt;14<br />contig4 ACTTATATATATATA&gt;14<br />contig5 ACTTTATATATT&lt;=14<br />We can also use 1 after the last block {} to print everything (1 is a shorthand notation for {print $0} which becomes {print} as without any argument print will print $0 by default), and within this block, we can change $0, for example to assign the first field to $0 for third line (NR==3), we can use:</p><p>$ awk 'NR==3{$0=$1}1' data/test.tsv<br />contig1 ACTGTCTGTCACTGTGTTGTGATGTTGTGTGTG<br />contig2 ACTTTATATATT<br />contig3<br />contig4 ACTTATATATATATA<br />contig5 ACTTTATATATT<br />You can have as many blocks as you want and they will be executed on each line in the order they appear, for example, if we want to print $1 three times (here we are using printf instead of print as the former doesn't put end-of-line character),</p><p>$ awk '{printf $1"\t"}{printf $1"\t"}{print $1}' data/test.tsv<br />contig1 contig1 contig1<br />contig2 contig2 contig2<br />contig3 contig3 contig3<br />contig4 contig4 contig4<br />contig5 contig5 contig5 <br />Although, we can also skip executing later blocks for a given line by using next keyword:</p><p>$ awk '{printf $1"\t"}NR==3{print "";next}{print $1}' data/test.tsv<br />contig1 contig1<br />contig2 contig2<br />contig3 <br />contig4 contig4<br />contig5 contig5</p><p>$ awk 'NR==3{print "";next}{printf $1"\t"}{print $1}' data/test.tsv<br />contig1 contig1<br />contig2 contig2</p><p>contig4 contig4<br />contig5 contig5<br />You can also use getline to load the contents of another file in addition to the one you are reading, for example, in the statement given below, the while loop will load each line from test.tsv into k until no more lines are to be read:</p><p>$ awk 'BEGIN{while((getline k &lt;"data/test.tsv")&gt;0) print "BEGIN:"k}{print}' data/test.tsv<br />BEGIN:contig1 ACTGTCTGTCACTGTGTTGTGATGTTGTGTGTG<br />BEGIN:contig2 ACTTTATATATT<br />BEGIN:contig3 ACTTATATATATATA<br />BEGIN:contig4 ACTTATATATATATA<br />BEGIN:contig5 ACTTTATATATT<br />contig1 ACTGTCTGTCACTGTGTTGTGATGTTGTGTGTG<br />contig2 ACTTTATATATT<br />contig3 ACTTATATATATATA<br />contig4 ACTTATATATATATA<br />contig5 ACTTTATATATT <br />You can also store data in the memory with the syntax VARIABLE_NAME[KEY]=VALUE which you can later use through for (INDEX in VARIABLE_NAME) command:</p><p>$ awk '{i[$1]=1}END{for (j in i) print j"&lt;="i[j]}' data/test.tsv<br />contig1&lt;=1<br />contig2&lt;=1<br />contig3&lt;=1<br />contig4&lt;=1<br />contig5&lt;=1</p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44614/online-resources-on-must-read-papers-in-evolutionary-biology</guid>
	<pubDate>Fri, 26 Jul 2024 01:39:14 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44614/online-resources-on-must-read-papers-in-evolutionary-biology</link>
	<title><![CDATA[Online resources on must-read papers in evolutionary biology]]></title>
	<description><![CDATA[<pre>Online resources on must-read papers in evolutionary biology, for a literature club.<br /><br />Below is a summary of all answers that we received.

All the best,

Jana and Xiaoyan

1.       *Nick Barton:*

- The textbook "Evolution" by Nick Barton, with resources for
  exploring the literature: Barton, N. H., Briggs, D. E. G., Eisen, J.
  A., Goldstein, D. B., &amp; Patel, N. H. (2007). Evolution. Cold Spring
  Harbor Laboratory Press.

- Papers from a course named "Classics in Evolutionary Biology":

Evolutionary Synthesis
1. Haldane, J. B. S. 1932. The causes of evolution. Longmans. New York.
   (esp. Ch. IV).
2. Fisher, R. A. 1930. The genetical theory of natural selection. Oxford
   University Press, Oxford. Selected Sections - Fundamental Theorem.

Genetic Variation
1a. Lewontin, R. C., and J. L. Hubby. 1966. A molecular approach to
the study of genic heterozygosity in natural populations. II. Amount
of variation and degree of heterozygosity in natural populations of
Drosophila pseudoobscura. Genetics. 54:595-609.

1b. Sachidandam et al. 2001. A map of human genome sequence variation
containing 1.42 million single nucleotide polymorphisms. 409: 928-33.

2. Wright S., Dobzhansky T., Hovanitz W. 1942 Genetics of natural
populations VII The allelism of lethals in the third chromosome of
Drosophila pseudoobscura. Genetics 27: 363-394.

Recombination and evolution
1. Hill, W. G., and A. Robertson. 1966. The effect of linkage on limits
to artificial selection. Genet. Res. 8:269-294.

2. Maynard Smith and Haigh. 1974. The hitch-hiking effect of a favourable
gene. Genet. Res. 23: 23-35.

Understanding sequence variation
1. Begun D. J., Aquadro C. F., 1992 Levels of naturally occurring DNA
polymorphism correlate with recombination rate in Drosophila melanogaster.
Nature 356: 519-520.

2. Green R. E., Reich D., P&auml;&auml;bo S., 2010 A draft sequence of the
Neandertal genome. Science 328: 710-722.

Quantitative Genetics:  variation in complex traits
1. Galton F., 1877 Typical laws of heredity. Nature 15: 492-495-
512-514- 532-533.

2. Turelli M., 1984 Heritable genetic variation via
mutation-selection balance: Lerch's Zeta meets the abdominal
bristle. Theor. Popul. Biol. 25: 138-193.

Quantitative Genetics:  finding the genes
1. Shrimpton A. E., Robertson A., 1988 The Isolation of polygenic factors
controlling bristle score in Drosophila melanogaster II Distribution of
third chromosome bristle effects within chromosome sections. Genetics
118: 445-459.

2. Boyle E. A., Li Y. I., Pritchard J. K., 2017 An expanded view of
complex traits: from polygenic to omnigenic. Cell 169: 1177-1186.

Neutral Evolution
1. Kimura, M. 1968. Evolutionary rate at the molecular level. Science.
217:624-626.

2a. Kern A. D., Hahn M. W., 2018 The Neutral Theory in Light of Natural
Selection. Molecular Biology and Evolution 110: 21077-6.

2b. Jensen J. D., Payseur B. A., Stephan W., Aquadro C. F., Lynch M.,
Charlesworth D., Charlesworth B., 2018 The importance of the Neutral Theory
in 1968 and 50 years on: a response to Kern and Hahn 2018. Evolution 112:
2109-4.

2c. Ellegren &amp; Galtier. 2016. Determinants of genetic diversity. Nature
Reviews Genetics.

Mutation and Genetic Variability
1. Luria, S. E., and M. Delbr&uuml;ck. 1943. Mutations of Bacteria from Virus
Sensitivity to Virus Resistance. Genetics. 28(6):491-511.

2. Hill, W G. 1982. "Rates of Change in Quantitative Traits From Fixation
of New Mutations." Proceedings of the National Academy of Sciences (U.S.A.)
79: 142-45.

Testing for selection
1. McDonald &amp; Kreitman. 1991. Adaptive protein evolution at the Adh locus
in Drosophila. Nature.

2. Begun, et al. Mol. Biol. Evol. 16, 1816-1819 (1999).

3. Siddiq et al. 2016. Experimental test and refutation of a classic case
of molecular adaptation in Drosophila melanogaster.  Nature Ecology &amp;
Evolution.

The shifting balance
1. Wright, S. 1932. The roles of mutation, inbreeding, crossbreeding and
selection in evolution. Proceedings of the VI International Congress of
Genetics: 1. pp 356-366.

2. Coyne, J.A., N.H. Barton, and M. Turelli. 1997. A critique of Wright's
shifting balance theory of evolution.  Evolution 51: 643-671.

3. Barton. 2016. Sewall Wright on Evolution in Mendelian Populations and
the "Shifting Balance". Genetics.

Evolution of Sex
1.  Muller, H.J. 1964. The relation of recombination to mutational advance.
Mutation Res. 1(1):2-9

2. McDonald et al. 2016. Sex speeds adaptation by altering the dynamics of
molecular evolution. Nature.

Kin Selection, Cooperation, and Conflict
1. Hamilton, W. D. 1964. The genetical evolution of social behaviour I.
Journal of Theoretical Biology. 7:1-52.

2. Trivers, R. L. 1974 Parent-offspring conflict. American Zoologist.
14(1):249-264.

Sexual Selection
1. Zahavi, A. 1975. Mate selection - a selection of a handicap. J. Theor.
Biol. 53:205-214.

2. Kirkpatrick, M., and Ryan, M.J. 1991. The evolution of mating
preferences and the paradox of the lek. Nature. 350:33-38.

Fitness Landscapes
1. Dean, A. 1995. A Molecular Investigation of Genotype by Environment
Interactions. Genetics. 139:19-33.

2. Costanzo et al. 2010. The Genetic Landscape of a Cell. Science.

Speciation
1. Coyne, J. A., and H. A. Orr. 1989. Patterns of speciation in Drosophila.
Evolution. 43:362-381.

2. Corbett-Detig et al. 2013. Genetic incompatibilities are widespread
within species. Nature.

2.       *Marcos Antezana:*

Valen, L. v. 1975. Energy and Evolution. University of Chicago, Department
of Biology.

3.       *Remco Folkertsma:*

1. The work by Hopi Hoekstra on local adaptation and oldfield mice

2. Poelstra, J. W., Vijay, N., Bossu, C. M., Lantz, H., Ryll, B., M&uuml;ller,
I., ... &amp; Wolf, J. B. (2014). The genomic landscape underlying phenotypic
integrity in the face of gene flow in crows. Science, 344(6190), 1410-1414.

4.       *Joshka Kaufmann and Leslie Turner*

They offer us a link to 'papers every evolutionary biologist should read',
the papers are collected by Leslie Turner.
https://static1.squarespace.com/static/53e8cb7ce4b02c4bc3aeeee4/t/5ab8fcb670a6ad55c67fcdf4/1522072758665/EvoBioClassicsRefList.pdf

5.       *Sarah Stockwell*

Matt Ridley collected classic papers in evolutionary biology and printed
part of these papers in his book Evolution (see Matt Ridley. Evolution
(Univ. of Oxford Press, 2nd edition, 2004))
</pre>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/28200/machine-learning</guid>
	<pubDate>Fri, 01 Jul 2016 12:57:12 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/28200/machine-learning</link>
	<title><![CDATA[Machine Learning !!!]]></title>
	<description><![CDATA[<p>In machine learning, computers apply&nbsp;<strong>statistical learning</strong>&nbsp;techniques to automatically identify patterns in data. These techniques can be used to make highly accurate predictions.</p>
<p><em>Keep scrolling.</em>&nbsp;Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.</p><p>Address of the bookmark: <a href="http://www.r2d3.us/visual-intro-to-machine-learning-part-1/" rel="nofollow">http://www.r2d3.us/visual-intro-to-machine-learning-part-1/</a></p>]]></description>
	<dc:creator>Gudiya Pal</dc:creator>
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