Here is the latest bag of tweets^*, which covers June 2011.

(*) These are interesting news that I found on Twitter and that I archive periodically.

• Bruce Walsh’s lecture notes on statistics of genetics http://t.co/U0Tu0as #SISG2011 (h/t Jeylen, 25 Jun)
• Discovering Disease Associations by Integrating Electronic Clinical Data and Medical Literature http://ff.im/-Gw7R8 (h/t kshameer, 25 Jun)
• Friendly Reminder: the excellent KEGG downloads will stop being free in less than a week (July 1) http://www.genome.jp/kegg/download/ (h/t biocs, 25 Jun)
• Great list of machine learning blogs http://bit.ly/l509iq (h/t drewconway, 25 Jun)
• One of my favorite vis algorithms, hierarchical edge bundling, finds its way into D3.js! http://t.co/4lBVt1a /cc @jasondavies (h/t mbostock, 24 Jun)
• #emacs discoveries: html-quote http://bit.ly/jdgvdj, bookmarks are very convenient, ediff is easier on the eyes than diff (h/t JohnDCook, 23 Jun)
• 2 new loci +replication of 20 known, n=3456/29624 RT @nkeriks: The 23andMe Parkinson’s disease GWAS paper is out today! http://bit.ly/msMzbr (h/t genetics_blog, 23 Jun)
• RT @cdsamii: Combining information from surveys that use multiple modes of contact http://bit.ly/isol2Q (h/t berndweiss, 23 Jun)
• Announcing GitHub for Mac http://t.co/XNux4NY (h/t github, 22 Jun)
• A final draft of my slides for tomorrow’s meetup are now online: http://bit.ly/mq2RcN The slides are ~10 MB because they embed animations. (h/t johnmyleswhite, 22 Jun)
• Enthought: ETS 4.0 Released http://bit.ly/j81kWz (h/t planetpython, 22 Jun)
• “Neuroeconomics: From brain process data to economic theory by Colin Camerer” http://t.co/x (h/t statalgo, 22 Jun)
• “Neuroeconomics: From brain process data to economic theory by Colin Camerer” http://t.co/x (h/t neilfws, 22 Jun)
• A study with GWAS, methylation, and gene expression in human brains is in dbGaP. http://1.usa.gov/j7uScd (h/t genetics_blog, 21 Jun)
• Using random matrix theory to analyze the genetic code of #HIV: http://on.wsj.com/l2cRs1 #mathematics #AIDS (h/t SIAMconnect, 21 Jun)
• Wordle - Positive words to use in a grant proposal http://t.co/yLxBrz3 (h/t mjpallen, 21 Jun)
• RIP: The regulatory interaction predictor - a machine learning based approach for predicting target genes of… http://ff.im/-GeXNA (h/t kshameer, 21 Jun)
• New Blog Post: ML Discuss for ICML 2011 ☞ http://t.co/fyQMjeu #icml2011 (h/t mdreid, 21 Jun)
• Studied under Leo Breiman at Berkeley? Here’s all about the #datamining software he developed: RandomForests http://ow.ly/5lYrL (h/t salfordsystems, 21 Jun)
• NEJM Archive 1812-1989 is free until June 30 via this link. Explore 150,000 articles and 85,316 images. http://nej.md/l31A5Y (h/t NEJM, 20 Jun)
• mapping #epidemiology #disease spreading with #google #maps http://bit.ly/iOdSiz (h/t MarcodelToro1, 20 Jun)
• A skip list container class in Python http://post.ly/2F7su (h/t irr, 19 Jun)
• Petit bilan des logiciels d’analyse numériques et scientifiques sur LinuxFr : http://is.gd/XQvWTn (from LinuxFr) (h/t shanneton, 19 Jun)
• Check it out, it’s @mikedewar’s Twitter ego-net http://bit.ly/kuPMbs #hackurfriends (h/t drewconway, 19 Jun)
• “#WinBUGS: a tutorial” by Lykou/Ntzoufras PDF für umme http://bit.ly/l1MzAZ #bayes (h/t berndweiss, 18 Jun)
• My blog post explaining Pregel, Google’s system for processing very large graphs http://icio.us/X0HApb (h/t michael_nielsen, 17 Jun)
• “Indirect estimations” of race, ethnicity from Census may help form nat’l framework to monitor health care disparities. http://nej.md/ikE5fu (h/t NEJM, 17 Jun)
• “A Survey of Multiple Tree Visualisation” http://t.co/2RYtMke - immensely useful paper in IVS Journal. (h/t FILWD, 17 Jun)
• If you want to create beautiful custom maps (who wouldn’t!) @tillnm wrote a nice tutorial on TileMill http://t.co/X5ST3QS (via @brainSteen) (h/t moritz_stefaner, 17 Jun)
• The Formation of Econometrics: A Historical Perspective: http://amzn.to/iplnib (h/t Jonfooruse, 17 Jun)
• I’ve uploaded a draft of my presentation “being a Bioinformatician 2.0” http://t.co/uY5Yjwr (h/t yokofakun, 17 Jun)
• Learn about our research to improve assessments for people with disabilities: http://bit.l (h/t ETSresearch, 16 Jun)
• Europe faces neuropsychiatric research vacuum, warns ECNP report http://sns.mx/qQeyy0 (h/t TheNeuroScience, 16 Jun)
• Discovered this gem - MapReduce for Machine Learning on Multicore - http://t.co/beu2m4U - Chu et al. + Bradski and Ng #MapReduce #ML #Stats (h/t suncoolsu, 16 Jun)
• Just discovered the Bioconductor download stats page: http://j.mp/iFj3DT. #rstats (h/t neilfws, 16 Jun)
• RT @dahern1: NEJM article on indirect estimation method for disparities http://healthpolicyandreform.nejm.org/?p=14700 (h/t Bill_Gardner, 16 Jun)
• BTW, the Google Social Graph API is a FANTASTIC way to avoid the Twitter API rate-limiting gremlins http://bit.ly/iRkYnP (h/t drewconway, 16 Jun)
• RT @irr Mathematical Background http://post.ly/2DZW6 « concise survey of many mathematical concepts from a compsci point of view (h/t benm, 15 Jun)
• #twitter might help fight #spreading of #disease http://bit.ly/ijAxKA. #socialmedia (h/t MacodelToro1, 15 Jun)
• “A new Hadoop-based framework implemented in Scala under development Yahoo!Labs” http://t.co/L1GLuwJ via @ogrisel (h/t ijuma, 15 Jun)
• Introduction To Machine Learning (Smola & Vishwanathan) http://alex.smola.org/drafts/thebook.pdf (http://bit.ly/lXWoLv) #guru (h/t newsyc20, 15 Jun)
• http://t.co/9hY42R1 – Bayesian Nonparametrics tutorial by Drs. Gershman and Blei – Great Intro #bayes #nonparametrics #MachineLearning (h/t suncoolsu, 15 Jun)
• Why Rubyists Should Care About Messaging (A High Level Intro) http://bit.ly/jFkbdW #greader (h/t neilfws, 15 Jun)
• Scala AVL tree data structure implementation: http://t.co/M1c7Kb2 #scala (h/t jboner, 14 Jun)
• Shift + Command + I when in safari (or Chrome) opens a mail with the link of the current page in it. via @smashingmag @Uleria (h/t heimatgestalten, 13 Jun)
• [biostar] Answer by Pierre Lindenbaum for Loading 1000 genomes vcf files in R: “I’m wondering if anyone… http://tinyurl.com/3vzzw8g (h/t yokofakun, 13 Jun)
• Brain structure adapts to environmental change http://sns.mx/qTeyy3 (h/t TheNeuroScience, 13 Jun)
• Long interview with Donald Knuth http://bit.ly/kfpNJE via @Jose_A_Alonso (h/t TeXtip, 13 Jun)
• Common genetic variation and human traits. http://ff.im/FDM4l (h/t kshameer, 13 Jun)
• Using simulation to demonstrate theory: Hardy-Weinberg Equilibrium http://ow.ly/5gpyw (h/t StatFact, 13 Jun)
• my book, Linguistic Structure Prediction, is now live … http://t.co/u34fbHY (h/T nlpnoah, 13 Jun)
• Scala: The static language that feels dynamic by Bruce Eckel http://ow.ly/5fXPg (h/t CompSciFact, 13 Jun)
• Best #datamining algorithm http://bit.ly/a2qDWJ (h/t zyxo, 13 Jun)
• Such few lines of code! Great! RT @mbostock: Using custom forces to cluster nodes with D3’s force-directed graph layout: http://t.co/bXYICLM (h/t JanWillemTulp, 12 Jun)
• Harvard course on pseudorandomness http://ow.ly/5ftCq (h/t ProbFact, 12 Jun)
• A very effective #visualization technique I wasn’t aware of: Bubble Sets: http://j.mp/kLc8oy #infoviz (h/t JanWillemTulp, 12 Jun)
• NoSQL Databases: What, Why, and When http://post.ly/2C2WS (h/t irr, 11 Jun)
• NinetyNineHaskellProblems - The solutions to well-known 99 Haskell problems http://bit.ly/lDpy9s (h/t github_hs, 11 Jun)
• “Machine Learning: An Algorithmic Perspective” (http://t.co/SwR5prr) might be the best overall intro ML book (and has #python code). (h/t statalgo, 11 Jun)
• #python equivalent of *nix wc -l command: sum(1 for x in open(filename)) (h/t raymondh, 10 Jun)
• Liking @sigfpe’s C-x RET C-\ TeX for Emacs TeX input mode. Can type ∑1/α² and paste straight into Wolfram Alpha: http://tinyurl.com/3oftawd (h/t benm, 10 Jun)
• Challenging Code: A Sociological Reading of the KDE Free Software Project http://t.co/ (h/t benjamingeer, 10 Jun)
• SiM: On fitting generalized linear mixed-effects models for binary responses using different statistical packages http://bit.ly/itGwbJ (h/t berndweiss, 10 Jun)
• “My colleagues, they study artificial intelligence; me, I study natural stupidity.” -Amos Tversky, founding father of behavioral economics (h/t Market_Quotes, 10 Jun)
• Tim Golden: Python Testing Cookbook http://bit.ly/mk5KFd (h/t planetpython, 10 Jun)
• MT @HealthDataGov Some interesting data/reports on healthcare quality measures available @ http://www.ahrq.gov/ & http://www.healthdata.g (h/t Biff_Bruise, 10 Jun)
• RT @nealrichter: Yahoo released its hadoop code for LDA http://j.mp/leyvTh
• Crab is now part of the #Recsys Wiki Pages :D #Python Framework for building recommendation engines! http://recsyswiki.com/wiki/Crab (h/t marcelcaraciolo, 9 Jun)
• Advanced computer science courses online with good lecture notes http://ow.ly/5dL1F (h/t CompSciFact, 9 Jun)
• A list of advanced computer science courses .. http://the-paper-trail.org/blog/?page_id=152 (h/t debasishg, 9 Jun)
• Foundations of Computational Intelligence: Volume 6: Data Mining … http://bit.ly/lqFITg (h/t DataMiningTips, 9 Jun)
• Integrative clustering of multiple genomic data types using a joint latent variable model with applic… http://bit.ly/kCG2HV #citeulike (h/t neilfws, 9 Jun)
• Who writes Wikipedia? An information-theoretic analysis of anonymity and vandalism in user-generated content http://j.mp/igMty4 (h/t drewconway, 8 Jun)
• alias ‘serve’=‘python -m SimpleHTTPServer’ (h/t ananelson, 8 Jun)
• More Command-Line Text Munging Utilities http://goo.gl/fb/JUkU6 (h/t genetics_blog, 8 Jun)
• The impoverished #Social Scientist’s Guide to Free #Statistical Software. Nice title and resource. http://bit.ly/jTKgMa (h/t NPHard, 7 Jun)
• New post: Stand-alone scientific code http://bit.ly/iMoVXH (h/t JohnDCook, 7 Jun)
• Exponential Smoothing: The State of the Art: http://www.bauer.uh.edu/gardner/docs/pdf/Exponential-Smoothing.pdf #statistics #forecasting (h/t gappy3000, 7 Jun)
• one of the best tumblrs i have seen in a while » a tumblr dedicated to scientific illustration http://j.mp/kOE8XZ (h/t asayf, 7 Jun)
• Manifold learning in scikit-learn http://ow.ly/5bQCl (h/t SciPyTip, 7 Jun)
• Free ebook PDF: On Being a Scientist: A Guide to Responsible Conduct in Research: Third Edition http://bit.ly/jThqI6 (h/t adolfont, 7 Jun)
• [delicious] Top 10 Git Tutorials for Beginners #tweet: http://tinyurl.com/3lkxxu4 (h/t yokofakun, 6 Jun)
• Machine learning expert Prof Chris Bishop | In-depth | The Engineer http://t.co/Tdnk7vw (h/t nmaillot, 6 Jun)
• Sample Chapter for “The Python Standard Library By Example” http://bit.ly/isEzev (h/t doughellmann, 6 Jun)
• Kudos to @bartdag for this Py4J project. Allow python programs access Java objects running inside a JVM! http://py4j.sourceforge.net (h/t alexdong, 6 Jun)
• Conway’s Game of Life in R with ggplot2 and animation http://bit.ly/iFWpeZ #greader (h/t neilfws, 6 Jun)
• Google paper comparing performance of C++, Java, Scala, and Go http://post.ly/295o6 (h/t irr, 3 Jun)
• Distributed Systems Papers :: myNoSQL http://j.mp/limjOn A huge collection of papers (h/t al3xandru, 3 Jun)
• Data Partitioning with OpenCL - from Matthew Scarpino, author of OpenCL in Action - http://is.gd/UhqGT7 (h/t jrecursive, 3 Jun)
• http://t.co/kgU80cb – nice slice/dice + map view for AIDS data. (cool data, depressing information) (h/t arnabdotorg, 2 Jun)
• Ruby script to download all papers from a given Springer LNCS proceedings book, http://pastie.org/2004661 (h/t akuhn, 1 Jun)
• Notes on Monte Carlo Methods: http://t.co/iphdiwk #mathematics (h/t gappy3000, 1 Jun)
• PLoS opens search through webservices API http://api.plos.org/ should be great for text miners (h/t gepasi, 1 Jun)
• MLDemos - Machine Learning Demonstrations & Visualizations - http://is.gd/vrXtgP (h/t jrecursive, 1 Jun)
• The Bootstrap Filter – a simple particle filter implementation in python by @mikedewar – http://bit.ly/mufc8u (h/t smarttypes, 1 Jun)
• A Tutorial on Particle Filtering and Smoothing: Fifteen years later (pdf) http://www.cs.ubc.ca/%7Earnaud/doucet_johansen_tutorialPF.pdf (h/t smarttypes, 1 Jun)
• Random Projection vs PCA vs Locally Linear Embedding on handwritten digits using @scikit_learn http://bit.ly/jJK7Qj #machinelearning (h/t ogrisel, 1 Jun)
• Should We Abandon the t-Test in the Analysis of Gene Expression Microarray Data: A Comparison of Variance Modeling… http://ff.im/EDh0A (h/t kshameer, 1 Jun)