guest lecture at U of Arkansas bioinformatics seriees

https://ualr.edu/bioinformatics/education-series/ Friday, October 16 at 2:00 pm CST Recorded Session available at https://youtu.be/6q9xHV5VznM Speaker Topic Dr. Hong Qin is an Associate Professor in the Department of Computer Science and Engineering at the University of Tennessee – Chattanooga who uses computational and mathematical approaches to investigate biomedical and biological questions. One focus is to develop probabilistic gene network models to infer network changes during cellular aging. We build gene network models from heterogeneous genomics data sets, including protein interactions, gene expression data sets, RNAseq data sets, protein mass-spec data sets, high-throughput phenotypic screens, and gene annotations. We are developing machine-learning methods to automatically estimate cellular lifespan from time-lapsed images. We are also applying engineering principles to study molecular, biological, and ecological networks. We are developing deep-learning methods for better classification and prediction using heterogeneous biomedical and biological large data sets. Dr. Hong Qin is a recipient of a NSF CAREER award 2015-2020. Qin’s expertise: Graph reliability modeling; Bioinformatics; Computational genomics; Mathematical modeling; Systems Biology; Cellular aging; Gene network analysis and modeling Dr. Qin will present how to use R to analyze COVID 19 data. R (along with bio-python and bio-perl) is one of the top choices for analyzing life science data. R is open source, runs on multiple platforms (Windows, Linux, MacOS, and cloud), has excellent packages for analyzing genomic data (e.g., Bioconductor), and has several nice interfaces for developing and running code (R Studio and Jupyter Notebook). The R code designed for this demo is available from https://github.com/hongqin/Use-R-in-CoLab/blob/master/Learn_R_UALR_CoLab.ipynb. This tutorial runs in Google’s CoLab cloud so no local installation is needed.

USA county population

 

USDA Economic Research Service

https://www.ers.usda.gov/data-products/county-level-data-sets/download-data/

stanford saftey in systems programming

 

https://reberhardt.com/blog/2020/10/05/designing-a-new-class-at-stanford-safety-in-systems-programming.html#should-there-be-a-safety-class 

Heckman correction

 

https://en.wikipedia.org/wiki/Heckman_correction

"Conceptually, this is achieved by explicitly modelling the individual sampling probability of each observation (the so-called selection equation) together with the conditional expectation of the dependent variable (the so-called outcome equation)" 

The Heckman used Mills ratio to model right-censored data as 'selection bias'. 

selection bias corrected by Mills ratio (inverse of Gompertz mortality rate?)

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3659766  

`selection bias' using the inverse Mills ratio neglected by epidemiologists.

https://en.wikipedia.org/wiki/Mills_ratio

Mills ratio is the Survival function / pdf, 

The Gompertz function is u = - dS/dt * 1/S, so, HQ thinks Mills ratio is the inversion of the Gompertz mortality rate. 

related to 

https://en.wikipedia.org/wiki/Heckman_correction

what is entropy in statistics

 

smoothing parameter

 

over-smoothed curves may lose some details

rough or over-sensitive smoothed curse may be too noisy.  An ideal choice of smoothing parameter can be obtained by appropriately contructed error measures, such as residuals. 

Ref: Sharma, 2000, J of hydrology, seasonal to interannaul rainfall probabilistic forecasts for improved water supply management. 

mutual information for aging potential

 

https://youtu.be/U9h1xkNELvY

mutual information criterion for time series analysis

 

mutual information criterion for time series analysis

cran 

https://rdrr.io/cran/tseriesChaos/man/mutual.html 

AI popular book

 

https://www.aiperspectives.com/ai-handbook/

how to cite ERS5-land

 

How to cite ERA5-Land

(1) Please acknowledge the use of ERA5-Land as stated in the Copernicus C3S/CAMS License agreement:

• "5.1.2 Where the Licensee communicates or distributes Copernicus Products to the public, the Licensee shall inform the recipients of the source by using the following or any similar notice: 'Generated using Copernicus Climate Change Service Information [Year]'.

• 5.1.3 Where the Licensee makes or contributes to a publication or distribution containing adapted or modified Copernicus Products, the Licensee shall provide the following or any similar notice: 'Contains modified Copernicus Climate Change Service Information [Year]';

Any such publication or distribution covered by clauses 5.1.1 and 5.1.2 shall state that neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus Information or Data it contains."

(2) cite the ERA5-Land dataset (as part of the bibliography) as follows:

• For "ERA5-Land hourly data from 1981 to present" downloaded from the Climate Data Store:

Muñoz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (<date of access>), 10.24381/cds.e2161bac

• For "ERA5-Land monthly averaged data from 1981 to present" downloaded from the Climate Data Store:

Muñoz Sabater, J., (2019): ERA5-Land monthly averaged data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (<date of access>), 10.24381/cds.68d2bb30

video to video synthesis

 

https://arxiv.org/abs/1808.06601

cross corelation

https://youtu.be/6ao9-39zw40

absolute humidity

Bukhari, 2020, Enviromental Resaerch and Public Health, MDPI

cross correlation between two time series

correlation is a linear measure of similarity between two signals. Cross-correlation is somewhat a generalization of the correlation measure as it takes into account the lag of one signal relative to the other. If lag == 0, then correlation = cross-correlation. Cross-correlation is particularly important to assess the causal relationship between two signals in time. If you suspect that there is a non-linear relationship between the two signals, then you should consider measures such as mutual information and partial mutual information, which are the information-theoretic equivalent of correlation and cross-correlation. 

You can refer to the comment above for information on how to compute correlation and cross-correlation. Check this paper instead (and references therein) for details on mutual information and partial mutual information http://www.sciencedirect.com/science/article/pii/S0022169400003462 https://youtu.be/6ao9-39zw40 https://youtu.be/L6YJqhbsuFY  

 ccf in R Ziwei Ma:
I checked the ccf function which calculates the correlation for x_{t+h} and y_t, so in our case, the ccf( dailyCases (x), dew_points (y) ) report they are positively relative, and the peak happen at arround 7 which says dailyCases lags dew_points or dew_points predict dailyCases in 7 days. 

The following website have more details there. https://online.stat.psu.edu/stat510/lesson/8/8.2