First CRISPR Gene Editing Drug

Pharma advances  

The first CRISPR gene-editing drug, designed to treat blood disorders, could be on the market by 2023. Here’s what it means for the future of drug development.

By Sy MUKHERJEE 

Until recently, CRISPR—the gene-editing technology that won scientists Jennifer Doudna and Emmanuelle Charpentier the 2020 Nobel Prize in chemistry—sounded more like science fiction than medicine; lab-created molecular scissors are used to snip out problematic DNA sections in a patient’s cells to cure them of disease. But soon we could see regulators approve the very first treatment using this gene-editing technology in an effort to combat rare inherited blood disorders that affect millions across the globe.

In a $900 million collaboration, rare disease specialist Vertex and CRISPR Therapeutics developed the therapy, dubbed exa-cel (short for exagamglogene autotemcel). It has already amassed promising evidence that it can help patients with beta thalassemia and sickle cell disease (SCD), both of which are genetic blood diseases that are relatively rare in the U.S. but somewhat more common inherited conditions globally.  ... ' 

Data Science for an Internet of Things

More extracts form the Open Gardens blog by Ajit.   Following now.

Creating an open methodology for Internet of Things (IoT) Analytics: Data science for Internet of Things  January, 2016 By Ajit.

A methodology for solving problems with DataScience for Internet of Things  July 21, 2016 By ajit

Useful, largely nontechnical views of enterprise driven issues in delivering data science.

Evolving CRISP-DM

We examined, but never formally used Crisp-DM.  ( Cross Industry Standard Process for Data Mining ) Even if you do not use it formally, its worth looking at as a checklist of considerations for analytics process oriented applications.  In this piece, it is looked at for application to Internet of things analytics.    Considering its test for a related applications.

(Update)   See also ASUM-DM, a further extension of CRISP-DM, that I am now examining.   More description and discussion of that here.

An Architecture of Data Science

Nicely done piece by Vincent Granville, about the 'architectural' considerations of data science.  In roughly the order you should progress, but understanding you may have to revisit many elements more than once.   Mostly nontechnical.  This is useful for the entire spectrum of project participants, from scientists to decision makers.  A template for understanding?

" ... In this article, I summarize the components of any data science / machine learning / statistical project, as well as the cross-dependencies between these components. This will give you a general idea of what a data science or other analytic project is about. .... " 

In comparison, related architecture, see also CRISP-DM.

Cross Industry Standard Process for Data Mining

CRISP-DM ( Cross Industry Standard Process for Data Mining) which we plugged into as far back as 07, seems to have disappeared, along with its web site.  Data mining also appears to have been folded into the study and methods of data science.  In particular the decision tree aspects.  That is too bad, because the decision oriented pieces were particularly understandable to executives.    We found the tree methods indispensable.  A WP article still covers the basics . The six fundamental aspects of the process are:

  1.) Business Understanding
  2.) Data Understanding
  3.) Data Preparation
  4.) Modeling
  5.) Evaluation
  6.) Deployment

With lots of details included in each segment. Each element makes excellent points about what is to be done, what resources are needed, who is responsible and where the results go.    I see that SPSS Modeler (formerly Clementine) still embraces the concept.   Does anyone still teach this?

Process Model for Data Mining

This CRISP-DM model (updated) was looked at within the enterprise. Re-examined. It attempts to be very detailed and universal. Worth a look though it does not seem to have been updated for some time.

' ... The CRISP-DM project developed an industry- and tool-neutral data mining process model. Starting from the embryonic knowledge discovery processes used in early data mining projects and responding directly to user requirements, this project defined and validated a data mining process that is applicable in diverse industry sectors. This methodology makes large data mining projects faster, cheaper, more reliable and more manageable. Even small scale data mining investigations benefit from using CRISP-DM...."