Illumina - Revision history

Bradley Monk: /* Blog & Wiki */

2020-05-09T14:48:39Z

Blog & Wiki

← Older revision		Revision as of 07:48, 9 May 2020
Line 19:		Line 19:


			[[File:Brad blog.png\|800px]]

	=== Misc. Project Video Clips ===		=== Misc. Project Video Clips ===

Bradley Monk: /* IDOT Comprehensive Truck Study */

2020-05-09T14:46:36Z

IDOT Comprehensive Truck Study

← Older revision		Revision as of 07:46, 9 May 2020
Line 11:		Line 11:
	[https://docs.google.com/document/d/1jipA2VhNGgy6ZGEqzvUUvVtH4WntWplYURRsingBTH8/edit Here is a draft] of the comprehensive report I made for the Illinois Department of Transportation Truck Study. Again this study might not be relevant other than to illustrate how I coped with messy data, and some interesting data viz. e.g. below is showing how, for a particular highway, traffic volume (z-axis) has changed between 1975-2015 (year) especially entering Chicago (milepost 1-40). It also shows the pavement quality rating as the colored topographic plot (pins represent major reconstructions).		[https://docs.google.com/document/d/1jipA2VhNGgy6ZGEqzvUUvVtH4WntWplYURRsingBTH8/edit Here is a draft] of the comprehensive report I made for the Illinois Department of Transportation Truck Study. Again this study might not be relevant other than to illustrate how I coped with messy data, and some interesting data viz. e.g. below is showing how, for a particular highway, traffic volume (z-axis) has changed between 1975-2015 (year) especially entering Chicago (milepost 1-40). It also shows the pavement quality rating as the colored topographic plot (pins represent major reconstructions).


			[[File:Truck study fig.png]]

	=== Blog & Wiki ===		=== Blog & Wiki ===

Bradley Monk at 14:45, 9 May 2020

2020-05-09T14:45:07Z

← Older revision		Revision as of 07:45, 9 May 2020
Line 1:		Line 1:
	Hi Kimberly,		Hi Kimberly, below you will find various examples of data science, programming, bioinformatics, and other misc. stuff I thought might be relevant. Thanks again for your time!

Bradley Monk at 14:42, 9 May 2020

2020-05-09T14:42:30Z

← Older revision		Revision as of 07:42, 9 May 2020
Line 2:		Line 2:


	~~Here is the link to the figures for the machine learning & genomics chapter of my dissertation (and accepted~~ PNAS paper~~), that we briefly looked over~~ on ~~Tuesday: [http://bit.ly/forillumina]. (n.b. you asked about functional mapping of the new implicated SNPs; slides 165-181 use FUMA to explore, a bit, this relationship).~~		===PNAS paper on Alzheimer's Disease genomics & Machine Learning paper ===

			Here is the link to the figures for the machine learning & genomics chapter of my dissertation (and accepted PNAS paper), that we briefly looked over on Tuesday: http://bit.ly/forillumina. (n.b. you asked about functional mapping of the new implicated SNPs; slides 165-181 use FUMA to explore, a bit, this relationship).


			=== IDOT Comprehensive Truck Study ===

	[https://docs.google.com/document/d/1jipA2VhNGgy6ZGEqzvUUvVtH4WntWplYURRsingBTH8/edit Here is a draft] of the comprehensive report I made for the Illinois Department of Transportation Truck Study. Again this study might not be relevant other than to illustrate how I coped with messy data, and some interesting data viz. e.g. below is showing how, for a particular highway, traffic volume (z-axis) has changed between 1975-2015 (year) especially entering Chicago (milepost 1-40). It also shows the pavement quality rating as the colored topographic plot (pins represent major reconstructions).		[https://docs.google.com/document/d/1jipA2VhNGgy6ZGEqzvUUvVtH4WntWplYURRsingBTH8/edit Here is a draft] of the comprehensive report I made for the Illinois Department of Transportation Truck Study. Again this study might not be relevant other than to illustrate how I coped with messy data, and some interesting data viz. e.g. below is showing how, for a particular highway, traffic volume (z-axis) has changed between 1975-2015 (year) especially entering Chicago (milepost 1-40). It also shows the pavement quality rating as the colored topographic plot (pins represent major reconstructions).


	~~Here is a link to my blog, where I mostly talk about data science stuff: [http://bradleymonk.com/wp]~~		=== Blog & Wiki ===

	Here is a link to my ~~wiki (~~this wiki ~~actually)~~ where I post a variety of resources~~: [http://bradleymonk.com/w] (for example~~ how to model [[Brownian Motion]])		Here is a [http://bradleymonk.com/wp link to my blog], where I mostly talk about data science stuff. Also feel free to browse this wiki, where I post a variety of resources, like how to model [[Brownian Motion]]).


	~~=== Below are some short video clips that demo some of the side projects I've worked on. ===~~

			=== Misc. Project Video Clips ===

	I ~~also~~ have experience generating simulations based on biological measurements (e.g. molecular kinetics, diffusion coefficients, association/dissociation rates). Here's one showing the diffusion of receptors along the surface of a dendritic membrane. Those receptors can enter and exit dendritic spine areas that are simulating actin polymerization.		I have experience generating simulations based on biological measurements (e.g. molecular kinetics, diffusion coefficients, association/dissociation rates). Here's one showing the diffusion of receptors along the surface of a dendritic membrane. Those receptors can enter and exit dendritic spine areas that are simulating actin polymerization.

	# First I create a 3D mesh dendritic segments using Fenics-Dolfin in Python~~: (youtube_clip)~~		# First I create a 3D mesh dendritic segments using Fenics-Dolfin in Python
	# I then worked out the dynamics for actin polymerization in dendritic spines~~: (youtube_clip1, youtube_clip2)~~		# I then worked out the dynamics for actin polymerization in dendritic spines
	# Then I worked on a simulation of receptors diffusing along a 'ruffled' membrane ~~(youtube_clip)~~		# Then I worked on a simulation of receptors diffusing along a 'ruffled' membrane
	# Finally I put all of these components together to show how dendritic spines capture receptors using actin filaments ~~(youtube_clip)~~		# Finally I put all of these components together to show how dendritic spines capture receptors using actin filaments



	{{#ev:youtube\|tDKUU0SqbSA}}		{{#ev:youtube\|tDKUU0SqbSA}}

			{{#ev:youtube\|QBx8F_5_y0g}}

			{{#ev:youtube\|JH-hGjzhEFQ}}

			{{#ev:youtube\|QV2h8Ey0VJs}}

			{{#ev:youtube\|6ZNnBGgea0Y}}






			I created a program that is now used by all the labs in the Center for Neural Circuits, that imports a video of neural activity (GCaMP activity), and automatically finds ROIs, based on a number of segmentation techniques. It then isolates that activity of each ROI for further processing:


			{{#ev:youtube\|gTqc4Xl-AfI}}



			Here's another app that I made, that's become popular in the neuro department - it's a tool to quantify neuron morphology (youtube_clip)

			{{#ev:youtube\|XlezCcrSB-Y}}




			I also have experience working with DAQs to interface software and hardware:

			{{#ev:youtube\|_SIy9LQ7ZL0}}
			{{#ev:youtube\|Q7MpFoIaztY}}



	I created a program that is now used by all the labs in the Center for Neural Circuits, that imports a video of neural activity (GCaMP activity), and automatically finds ROIs, based on a number of segmentation techniques. It then isolates that activity of each ROI for further processing: (youtube_clip)

			This one is a little silly, but if you've ever wanted to get the raw data from a graph in a published paper, I made a program that will parse data from an image of a scatter plot and create a table of the raw data:

	~~Here's another app everyone in the neuro department is using - it's a tool to quantify neuron morphology (youtube_clip)~~		{{#ev:youtube\|J1AV0GAjx0U}}


	~~I also have experience working with DAQs to interface software and hardware (youtube_clip1, youtube_clip2)~~


	~~This one is~~ a ~~little silly, but if you've ever wanted to get~~ the ~~raw data from~~ a ~~graph in~~ a ~~published paper~~, ~~I made a program~~ that ~~will parse data from an image of a scatter plot and create~~ a ~~table~~ of ~~the raw data (youtube_clip)~~		I created a program that recorded user behavior as they interacted with our iPhone/Android app. It would record the current app screen and also tracked a user's finger (via a sticker) as they interacted with the app. This allowed us to determine, for example, if they kept tapping on regions of the screen that were not clickable. This let us optimize the app in a number of ways to make it more user friendly:


			{{#ev:youtube\|WNNO6NZ2FfY}}

	I created a program that recorded user behavior as they interacted with our iPhone/Android app. It would record the current app screen and also tracked a user's finger (via a sticker) as they interacted with the app. This allowed us to determine, for example, if they kept tapping on regions of the screen that were not clickable. This let us optimize the app in a number of ways to make it more user friendly (youtube_clip)



	__NOTOC__		__NOTOC__

Bradley Monk at 14:33, 9 May 2020

2020-05-09T14:33:21Z

← Older revision		Revision as of 07:33, 9 May 2020
Line 22:		Line 22:
	# Then I worked on a simulation of receptors diffusing along a 'ruffled' membrane (youtube_clip)		# Then I worked on a simulation of receptors diffusing along a 'ruffled' membrane (youtube_clip)
	# Finally I put all of these components together to show how dendritic spines capture receptors using actin filaments (youtube_clip)		# Finally I put all of these components together to show how dendritic spines capture receptors using actin filaments (youtube_clip)



			{{#ev:youtube\|tDKUU0SqbSA}}

Bradley Monk at 14:32, 9 May 2020

2020-05-09T14:32:03Z

← Older revision		Revision as of 07:32, 9 May 2020
Line 2:		Line 2:


	~~---~~
	Here is the link to the figures for the machine learning & genomics chapter of my dissertation (and accepted PNAS paper), that we briefly looked over on Tuesday: [http://bit.ly/forillumina]. (n.b. you asked about functional mapping of the new implicated SNPs; slides 165-181 use FUMA to explore, a bit, this relationship).		Here is the link to the figures for the machine learning & genomics chapter of my dissertation (and accepted PNAS paper), that we briefly looked over on Tuesday: [http://bit.ly/forillumina]. (n.b. you asked about functional mapping of the new implicated SNPs; slides 165-181 use FUMA to explore, a bit, this relationship).

Line 9:		Line 8:


			Here is a link to my blog, where I mostly talk about data science stuff: [http://bradleymonk.com/wp]

			Here is a link to my wiki (this wiki actually) where I post a variety of resources: [http://bradleymonk.com/w] (for example how to model [[Brownian Motion]])

	~~Here is a link to my blog, where I mostly talk about data science stuff: [http://bradleymonk.com/wp]~~

	~~Here is a link to my wiki (this wiki actually) where~~ I ~~post a variety of resources: [http://bradleymonk~~.~~com/w] (for example how to model [[brownian motion]])~~		=== Below are some short video clips that demo some of the side projects I've worked on. ===


	~~Below are some short video clips that demo some of the side projects I've worked on.~~
	I also have experience generating simulations based on biological measurements (e.g. molecular kinetics, diffusion coefficients, association/dissociation rates). Here's one showing the diffusion of receptors along the surface of a dendritic membrane. Those receptors can enter and exit dendritic spine areas that are simulating actin polymerization.		I also have experience generating simulations based on biological measurements (e.g. molecular kinetics, diffusion coefficients, association/dissociation rates). Here's one showing the diffusion of receptors along the surface of a dendritic membrane. Those receptors can enter and exit dendritic spine areas that are simulating actin polymerization.

Line 38:		Line 38:

	I created a program that recorded user behavior as they interacted with our iPhone/Android app. It would record the current app screen and also tracked a user's finger (via a sticker) as they interacted with the app. This allowed us to determine, for example, if they kept tapping on regions of the screen that were not clickable. This let us optimize the app in a number of ways to make it more user friendly (youtube_clip)		I created a program that recorded user behavior as they interacted with our iPhone/Android app. It would record the current app screen and also tracked a user's finger (via a sticker) as they interacted with the app. This allowed us to determine, for example, if they kept tapping on regions of the screen that were not clickable. This let us optimize the app in a number of ways to make it more user friendly (youtube_clip)

	--





	--		__NOTOC__
	~~__________________________~~
	~~Bradley MonkMoco & Malinow LabUCSD Center for Neural Circuits~~

Bradley Monk: Created page with "Hi Kimberly, --- Here is the link to the figures for the machine learning & genomics chapter of my dissertation (and accepted PNAS paper), that we briefly looked over on Tue..."

2020-05-09T14:25:58Z

Created page with "Hi Kimberly, --- Here is the link to the figures for the machine learning & genomics chapter of my dissertation (and accepted PNAS paper), that we briefly looked over on Tue..."

New page

Hi Kimberly,

---
Here is the link to the figures for the machine learning & genomics chapter of my dissertation (and accepted PNAS paper), that we briefly looked over on Tuesday: [http://bit.ly/forillumina]. (n.b. you asked about functional mapping of the new implicated SNPs; slides 165-181 use FUMA to explore, a bit, this relationship).

[https://docs.google.com/document/d/1jipA2VhNGgy6ZGEqzvUUvVtH4WntWplYURRsingBTH8/edit Here is a draft] of the comprehensive report I made for the Illinois Department of Transportation Truck Study. Again this study might not be relevant other than to illustrate how I coped with messy data, and some interesting data viz. e.g. below is showing how, for a particular highway, traffic volume (z-axis) has changed between 1975-2015 (year) especially entering Chicago (milepost 1-40). It also shows the pavement quality rating as the colored topographic plot (pins represent major reconstructions).

Here is a link to my blog, where I mostly talk about data science stuff: [http://bradleymonk.com/wp]

Here is a link to my wiki (this wiki actually) where I post a variety of resources: [http://bradleymonk.com/w] (for example how to model [[brownian motion]])

Below are some short video clips that demo some of the side projects I've worked on.
I also have experience generating simulations based on biological measurements (e.g. molecular kinetics, diffusion coefficients, association/dissociation rates). Here's one showing the diffusion of receptors along the surface of a dendritic membrane. Those receptors can enter and exit dendritic spine areas that are simulating actin polymerization.

# First I create a 3D mesh dendritic segments using Fenics-Dolfin in Python: (youtube_clip)
# I then worked out the dynamics for actin polymerization in dendritic spines: (youtube_clip1, youtube_clip2)
# Then I worked on a simulation of receptors diffusing along a 'ruffled' membrane (youtube_clip)
# Finally I put all of these components together to show how dendritic spines capture receptors using actin filaments (youtube_clip)

I created a program that is now used by all the labs in the Center for Neural Circuits, that imports a video of neural activity (GCaMP activity), and automatically finds ROIs, based on a number of segmentation techniques. It then isolates that activity of each ROI for further processing: (youtube_clip)

Here's another app everyone in the neuro department is using - it's a tool to quantify neuron morphology (youtube_clip)

I also have experience working with DAQs to interface software and hardware (youtube_clip1, youtube_clip2)

This one is a little silly, but if you've ever wanted to get the raw data from a graph in a published paper, I made a program that will parse data from an image of a scatter plot and create a table of the raw data (youtube_clip)

I created a program that recorded user behavior as they interacted with our iPhone/Android app. It would record the current app screen and also tracked a user's finger (via a sticker) as they interacted with the app. This allowed us to determine, for example, if they kept tapping on regions of the screen that were not clickable. This let us optimize the app in a number of ways to make it more user friendly (youtube_clip)

--

--
__________________________
Bradley MonkMoco & Malinow LabUCSD Center for Neural Circuits