Biostat and data science related funding

NIH grant reporter (search the funded NIH grants)
https://projectreporter.nih.gov/reporter.cfm


Active NIH funding opportunities
https://grants.nih.gov/grants/guide/search_results.htm?year=active&scope=rfa


NIH, statFund
https://statfund.cancer.gov/funding/

BD2K
https://commonfund.nih.gov/bd2k

NIH grant search
https://projectreporter.nih.gov/reporter.cfm

EG: Driver node cannot be indispensable node

Note that, I also found that a driver node cannot be indispensable; it has to be either dispensable or neutral. 

I confirmed it with the genes that was removed so far. 

They are either neutral or dispensable.

bibtex citation style, BMC articles

For books, BMC articles require address or publisher. Other wise, it print three question marks, "???"

Dynamic networks reveal key players in aging

Faisal and Melenkovic 2014  bioinforamtics
https://academic.oup.com/bioinformatics/article/30/12/1721/2748134#supplementary-data


Todo: apply this in yeast. network controllability analysis, -> reliability analysis

differential privacy

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

structural controllability and edge weight

structural controllability only considers topology. So networks with the same topology but different weight have exactly the same number of minimum driver nodes.

This suggests that gene duplication will provide controllability advantage.

minimum AIC value indicates the preferred model

minimum AIC value indicates the preferred model

Note that likelihood is proportional to sample size. So, AIC comparison between different samplesize are not meaningful.

https://en.wikipedia.org/wiki/Akaike_information_criterion#How_to_apply_AIC_in_practice

multiple plots in ggplot2

https://cran.r-project.org/web/packages/egg/vignettes/Ecosystem.html

tibble versus data frame

Tibbles are a modern take on data frames. They keep the features that have stood the test of time, and drop the features that used to be convenient but are now frustrating (i.e. converting character vectors to factors).

https://cran.r-project.org/web/packages/tibble/vignettes/tibble.html

ggplot superscript

https://stackoverflow.com/questions/47946232/ggplot2-annotation-with-text-sub-superscript-and-calculated-values

biomedical data tools

https://commonfund.nih.gov/bd2k/resources#Indexing

data privacy

• PrivaSeq(link is external): A tool base for quantification and analysis of the individual characterizing information leakage, which can be used to link phenotype datasets to genotype datasets and reveal sensitive information in linking attacks.
• PopMedNet(link is external): A scalable and extensible open-source informatics platform designed to facilitate the implementation and operation of distributed health data networks.
• PeerSMC(link is external): A web-browser based tool allowing for two or more parties to conduct secure multiparty computation. 

The data visualization awards are making a wide range of large biomedical datasets easier to use and interpret, including brain scan imaging, geo-referenced data, health care systems dynamic data, and genomics data.

• GGV(link is external): The Geography of Genetic Variants (GGV) browser is a web services software implementation of EEMS. EEMS is a new and innovative method for visualizing and analyzing population genetics data and other such geo-tagged biomedical data.
• HSD ontology(link is external): A novel method for identifying and extracting healthcare systems dynamics (HSD) data, and for integrating these data with "traditional" electronic health record (EHR) data. HSD data take into account the dynamics of the healthcare system when interpreting medical records. (For example, the date when a patient developed a disease can be inferred from when they received a diagnosis, scheduled a doctor visit, tests were ordered, etc.)
• Caleydo Web(link is external): Caleydo Web is a suite of web based methods and software tools designed to meet current needs for visualization and analysis of complex, heterogeneous biomedical data.
• Vials(link is external): Vials is a novel visual analysis tool for analyzing splicing patterns in RNA-seq data

The data wrangling awards are developing new methods and tools to improve the utility of big datasets by making them easier to share, integrate, and transform.

• IRRMC(link is external): The Integrated Resource for Reproducibility in Macromolecular Crystallography is a public database of x ray crystallography data, which provides a method for cleaning, collecting, and providing metadata for raw x-ray diffraction datasets.
• Fitmunk(link is external): A new program for the automatic building of amino-acid side chains in protein crystal structures.
• MODMatcher(link is external): A computational approach to identify and correct sample labeling errors in the multiple types of molecular data that can be used in subsequent integrative analyses.
• ActMiR(link is external): A software tool that infers the activity of miRNAs from expression data of target genes.
• AutoEEG(link is external) and MERCuRY: New methods to process EEG cohort datasets and clinical records, align epileptic events, and identify seizure onset patterns that are of direct impact to clinicians studying epilepsy.
• Mygeneinfo.com(link is external) and myvarientinfo.com: Open source, high-performance, and continuously-updated data application programming interfaces (APIs) for accessing comprehensive, structured gene and variant annotations. The integration of multiple information streams into a community platform for annotating gene and genetic variation data significantly reduces siloing and duplication of effort across multiple databases and their user communities.
• AsterixDB(link is external): A data management tool enabling ready access to and use of behavioral and other health-relevant data contained in social media streams developed primarily for HIV risk behavioral research.
• geQTL(link is external): A sparse regression method that can detect both group-wise and individual associations between SNPs and expression traits.

image based deeplearning in medical diagnoses

Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning

tensorflow
image net

http://www.cell.com/cell/fulltext/S0092-8674(18)30154-5

git add remote upstream

applejack:single_cell_segmentation hqin$ git remote -v 

origin https://github.com/QinLab/single_cell_segmentation.git (fetch)

origin https://github.com/QinLab/single_cell_segmentation.git (push)

$ git remote add upstream https://github.com/opnumten/single_cell_segmentation 

applejack:single_cell_segmentation hqin$ git remote -v

origin https://github.com/QinLab/single_cell_segmentation.git (fetch)

origin https://github.com/QinLab/single_cell_segmentation.git (push)

upstream https://github.com/opnumten/single_cell_segmentation (fetch)

upstream https://github.com/opnumten/single_cell_segmentation (push)

~$ git fetch upstream

remote: Counting objects: 9, done.

remote: Total 9 (delta 6), reused 6 (delta 6), pack-reused 3

Unpacking objects: 100% (9/9), done.

From https://github.com/opnumten/single_cell_segmentation

 * [new branch]      master     -> upstream/master

~$ git checkout master

Already on 'master'

Your branch is up-to-date with 'origin/master'.

applejack:single_cell_segmentation hqin$ 

applejack:single_cell_segmentation hqin$ git status

On branch master

Your branch is up-to-date with 'origin/master'.

nothing to commit, working tree clean

Reference:
https://help.github.com/articles/configuring-a-remote-for-a-fork/
https://help.github.com/articles/syncing-a-fork/

tensorflow in jyputer

it seems that entire python packages need to reinstalled under tensorflow enviroment.

https://stackoverflow.com/questions/43216256/running-tensorflow-in-jupyter-notebook/43259471

In Ubuntu64 VM rainbowdash,
source activiate tensorflow
pip install jupyter notebook
pip install pandas

(tensorflow) hqin@rainbowdash:~$ conda list > /media/sf_hqin/conda_list.txt
(tensorflow) hqin@rainbowdash:~$

# packages in environment at /home/hqin/anaconda3/envs/tensorflow:
#
absl-py                   0.2.2                     <pip>
astor                     0.6.2                     <pip>
backcall                  0.1.0                     <pip>
bleach                    1.5.0                     <pip>
certifi                   2016.2.28                py36_0  
decorator                 4.3.0                     <pip>
entrypoints               0.2.3                     <pip>
gast                      0.2.0                     <pip>
grpcio                    1.13.0                    <pip>
html5lib                  0.9999999                 <pip>
ipykernel                 4.8.2                     <pip>
ipython                   6.4.0                     <pip>
ipython-genutils          0.2.0                     <pip>
ipywidgets                7.2.1                     <pip>
jedi                      0.12.1                    <pip>
Jinja2                    2.10                      <pip>
jsonschema                2.6.0                     <pip>
jupyter                   1.0.0                     <pip>
jupyter-client            5.2.3                     <pip>
jupyter-console           5.2.0                     <pip>
jupyter-core              4.4.0                     <pip>
Markdown                  2.6.11                    <pip>
MarkupSafe                1.0                       <pip>
mistune                   0.8.3                     <pip>
nbconvert                 5.3.1                     <pip>
nbformat                  4.4.0                     <pip>
notebook                  5.5.0                     <pip>
numpy                     1.14.5                    <pip>
openssl                   1.0.2l                        0  
pandas                    0.23.1                    <pip>
pandocfilters             1.4.2                     <pip>
parso                     0.3.0                     <pip>
pexpect                   4.6.0                     <pip>
pickleshare               0.7.4                     <pip>
pip                       9.0.1                    py36_1  
pip                       10.0.1                    <pip>
prompt-toolkit            1.0.15                    <pip>
protobuf                  3.6.0                     <pip>
ptyprocess                0.6.0                     <pip>
Pygments                  2.2.0                     <pip>
python                    3.6.2                         0  
python-dateutil           2.7.3                     <pip>
pytz                      2018.5                    <pip>
pyzmq                     17.0.0                    <pip>
qtconsole                 4.3.1                     <pip>
readline                  6.2                           2  
Send2Trash                1.5.0                     <pip>
setuptools                39.2.0                    <pip>
setuptools                36.4.0                   py36_1  
simplegeneric             0.8.1                     <pip>
six                       1.11.0                    <pip>
sqlite                    3.13.0                        0  
tensorboard               1.8.0                     <pip>
tensorflow                1.8.0                     <pip>
termcolor                 1.1.0                     <pip>
terminado                 0.8.1                     <pip>
testpath                  0.3.1                     <pip>
tk                        8.5.18                        0  
tornado                   5.0.2                     <pip>
traitlets                 4.3.2                     <pip>
wcwidth                   0.1.7                     <pip>
Werkzeug                  0.14.1                    <pip>
wheel                     0.31.1                    <pip>
wheel                     0.29.0                   py36_0  
widgetsnbextension        3.2.1                     <pip>
xz                        5.2.3                         0  
zlib                      1.2.11                        0