Posts Tagged ‘cbb752’

Google’s new app lets users conduct scientific research on their phones | The Verge

Sunday, March 4th, 2018

https://play.google.com/store/apps/details?id=com.google.android.apps.forscience.whistlepunk&hl=en

https://itunes.apple.com/us/app/science-journal-by-google/id1251205555?mt=8

http://www.theverge.com/2016/5/22/11735532/google-science-journal-app-research-android

perhaps collect your own mhealth data

Points of significance: Machine learning: supervised methods

Sunday, March 4th, 2018

Points of significance – #MachineLearning: supervised methods https://www.Nature.com/articles/nmeth.4551 Nice discussion of the k in k-NN & the slack parm. C, penalizing misclassified points in SVM — both which act somewhat analogously as regularizers. Good for #teaching

Genic Intolerance to Functional Variation and the Interpretation of Personal Genomes

Sunday, February 25th, 2018

Genic Intolerance to Functional Variation & the Interpretation of Personal Genomes
http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1003709 Nice plot of the number of rare v common variants in each gene to find outliers particularly tolerant to impactful (eg #LOF) mutations

http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1003709

Petrovski et al ’13

Vertabelo for db design

Sunday, February 11th, 2018

https://www.vertabelo.com/

Webinar Invitation: General Data Science Overview

Sunday, October 29th, 2017

Webinar Invitation
General Data Science Overview
Date: November 1st
Time: 11:00 a.m. EDT

QT:{{”

How can we effectively and efficiently teach statistical thinking and computation to students with little to no background in either? How can we equip them with the skills and tools for reasoning with various types of data and leave them wanting to learn more?

In this talk we describe an introductory data science course that is our (working) answer to these questions. ….

Webinar Recordings:

We try to record every webinar we host and post all materials on our website. http://www.rstudio.com/resources/webinars/
“}}

Training Calendar | Research Data Support

Saturday, September 16th, 2017

http://researchdata.yale.edu/training-calendarthe Research Data Support website has published a unified calendar for data and research skills training provided by the Library, Center for Research Computing, Medical Library, and Center for Teaching and Learning.

DataScience related courses at Yale

Thursday, July 27th, 2017

The Research Data Consultation Group (http://researchdata.yale.edu/) has considered aggregating data science training information into a unified calendar.

Also, there’s an instruction calendar at the library
(http://csssi.yale.edu/instruction/workshop-and-instruction-calendar)

Naive Bayes Classification explained with Python code

Tuesday, May 16th, 2017

Naive #Bayes Classification explained with Python code
http://www.DataScienceCentral.com/profiles/blogs/naive-bayes-classification-explained-with-python-code Nice worked example; good for #teaching HT @KirkDBorne

Explore Erudite – BD2K Training Coordinating Center

Tuesday, December 6th, 2016

http://bigdatau.org/explore_erudite

Needleman–Wunsch algorithm – Wikipedia

Friday, November 11th, 2016

https://en.wikipedia.org/wiki/Needleman%E2%80%93Wunsch_algorithm

relates to

https://en.wikipedia.org/wiki/Wagner%E2%80%93Fischer_algorithm

https://en.wikipedia.org/wiki/Michael_J._Fischer

QT:{{"

Historical notes and algorithm development[edit]

The original purpose of the algorithm described by Needleman and Wunsch was to find similarities in the amino acid sequences of two proteins.[1]

Needleman and Wunsch describe their algorithm explicitly for the case when the alignment is penalized solely by the matches and mismatches, and gaps have no penalty (d=0). The original publication from 1970 suggests the recursion

A better dynamic programming algorithm with quadratic running time for the same problem (no gap penalty) was first introduced[3] by David Sankoff in 1972. Similar quadratic-time algorithms were discovered independently by T. K. Vintsyuk[4] in 1968 for speech processing ("time warping"), and by Robert A. Wagner and Michael J. Fischer[5] in 1974 for string matching.

"}}