nips nips2002 nips2002-119 nips2002-119-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Jason Weston, Olivier Chapelle, Vladimir Vapnik, André Elisseeff, Bernhard Schölkopf
Abstract: We consider the learning problem of finding a dependency between a general class of objects and another, possibly different, general class of objects. The objects can be for example: vectors, images, strings, trees or graphs. Such a task is made possible by employing similarity measures in both input and output spaces using kernel functions, thus embedding the objects into vector spaces. We experimentally validate our approach on several tasks: mapping strings to strings, pattern recognition, and reconstruction from partial images. 1
[1] N. Cristianini, A. Elisseeff, and J. Shawe-Taylor. On optimizing kernel alignment . Technical Report 2001-087, NeuroCOLT, 200l.
[2] I. Frank and J . Friedman. A Statistical View of Some Chemometrics Regression Tools. Technometrics , 35(2):109- 147, 1993.
[3] T. Graepel, R. Herbrich, P. Bollmann-Sdorra, and K Obermayer. Classification on pairwise proximity data. NIPS, 11:438- 444, 1999.
[4] T. Hastie, R. Tibshirani, and J. Friedman. The Elem ents of Statistical Learning. Springer-Verlag, New York , 200l.
[5] D. Haussler. Convolutional kernels on discrete structures. Technical Report UCSCCRL-99-10 , Computer Science Department, University of California at Santa Cruz , 1999.
[6] J . Li, A. N. Michel, and W . Porod. Analysis and synthesis of a class of neural networks: linear systems operating on a closed hypercube. IEEE Trans . on Circuits and Systems, 36(11) :1405- 22 , 1989.
[7] H. Lodhi , C. Saunders, J . Shawe-Taylor, N . Cristianini, and C. Watkins. Text classification using string kernels. Journal of Machine Learning Research, 2:419- 444 , 2002.
[8] S. Mika, G. Ratsch, J. Weston , B. Sch6lkopf, and K-R. Miiller. Fisher discriminant analysis with kernels. In Y.-H. Hu , J . Larsen , E. Wilson, and S. Douglas, editors, N eural N etworks for Signal Processing IX, pages 41- 48 . IEEE, 1999.
[9] C. Saunders, V. Vovk , and A. Gammerman. Ridge regression learning algorithm in dual variables. In J . Shavlik, editor, Machine Learning Proceedings of the Fifteenth International Conference(ICML '98), San Francisco, CA , 1998. Morgan Kaufmann.
[10] B. Sch6lkopf, S. Mika, C. Burges, P. Knirsch, K-R. Miiller, G. Ratsch, and A. J. Smola. Input space vs. feature space in kernel-based methods. IEEE-NN, 10(5):10001017, 1999.
[11] B. Sch6lkopf and A. J. Smola. Learning with K ern els. MIT Press, Cambridge, MA, 2002.
[12] V . Vapnik. Statistical Learning Theory. John Wiley and Sons, New York, 1998.


{% extends 'base/base.html' %} {% block navMenu %}
Target Prediction
Find the targets of a small molecule.
Similarities
Find similar molecules using different approaches.
Drug Information
Find information for a small molecule.


Thomas Vanderwyst
Interactive Media Design Porfolio
Traditional
Digital
Photography
Programming
Contact and About


{% extends "template" %} {% block content %}
Success! Your Philips Hue Hub has been paired with this computer.


Main Content
Breadcrumb
Privacy Policy
This privacy policy sets out how North Queensland Ensembles Inc. uses and protects any information that you give North Queensland Ensembles Inc. when you use this website.
North Queensland Ensembles Inc. is committed to ensuring that your privacy is protected. Should we ask you to provide certain information by which you can be identified when using this website, then you can be assured that it will only be used in accordance with this privacy statement.
North Queensland Ensembles Inc. may change this policy from time to time by updating this page. You should check this page from time to time to ensure that you are happy with any changes. This policy is effective from 01-January-2017.
What we collect
We may collect the following information:
- Name and job title
- Contact information including email address
- Demographic information such as postcode, preferences and interests
- Other information relevant to customer surveys and/or offers
What we do with the information we gather
We require this information to understand your needs and provide you with a better service, and in particular for the following reasons:
- Internal record keeping.
- We may use the information to improve our products and services.
- We may periodically send promotional emails about new products, special offers or other information which we think you may find interesting using the email address which you have provided.
- From time to time, we may also use your information to contact you for market research purposes. We may contact you by email, phone, fax or mail. We may use the information to customise the website according to your interests.
Security
We are committed to ensuring that your information is secure. In order to prevent unauthorised access or disclosure we have put in place suitable physical, electronic and managerial procedures to safeguard and secure the information we collect online.
How we use cookies
A cookie is a small file which asks permission to be placed on your computer’s hard drive. Once you agree, the file is added and the cookie helps analyse web traffic or lets you know when you visit a particular site. Cookies allow web applications to respond to you as an individual. The web application can tailor its operations to your needs, likes and dislikes by gathering and remembering information about your preferences.
We use traffic log cookies to identify which pages are being used. This helps us analyse data about webpage traffic and improve our website in order to tailor it to customer needs. We only use this information for statistical analysis purposes and then the data is removed from the system.
Overall, cookies help us provide you with a better website, by enabling us to monitor which pages you find useful and which you do not. A cookie in no way gives us access to your computer or any information about you, other than the data you choose to share with us.
You can choose to accept or decline cookies. Most web browsers automatically accept cookies, but you can usually modify your browser setting to decline cookies if you prefer. This may prevent you from taking full advantage of the website.
Links to other websites
Our website may contain links to other websites of interest. However, once you have used these links to leave our site, you should note that we do not have any control over that other website. Therefore, we cannot be responsible for the protection and privacy of any information which you provide whilst visiting such sites and such sites are not governed by this privacy statement. You should exercise caution and look at the privacy statement applicable to the website in question
Controlling your personal information
You may choose to restrict the collection or use of your personal information in the following ways:
- Whenever you are asked to fill in a form on the website, look for the box that you can click to indicate that you do not want the information to be used by anybody for direct marketing purposes
- If you have previously agreed to us using your personal information for direct marketing purposes, you may change your mind at any time by writing to or emailing us at<EMAIL_ADDRESS>We will not sell, distribute or lease your personal information to third parties unless we have your permission or are required by law to do so. We may use your personal information to send you promotional information about third parties which we think you may find interesting if you tell us that you wish this to happen.
You may request details of personal information which we hold about you under the Data Protection Act 1998. A small fee will be payable. If you would like a copy of the information held on you please write to PO Box 576, Townsville Qld 4810.
If you believe that any information we are holding on you is incorrect or incomplete, please write to or email us as soon as possible, at the above address. We will promptly correct any information found to be incorrect.


Before Change
return incumbent, inc_perf
// if first ever run, then assume current challenger to be the incumbent
if self.first_run and not incumbent:
self.logger.info("First run, no incumbent provided; challenger is assumed to be the incumbent")
incumbent = challenger
self.current_challenger = None
self.first_run = False
self.logger.debug("Intensify on %s", challenger)
if hasattr(challenger, "origin"):
self.logger.debug("Configuration origin: %s", challenger.origin)
After Change
self.logger.info("First run, no incumbent provided; challenger is assumed to be the incumbent")
incumbent = challenger
else:
inc_runs = run_history.get_runs_for_config(incumbent, only_max_observed_budget=True)
if len(inc_runs) > 0:
self.logger.debug("Skipping RUN_FIRST_CONFIG stage since incumbent is already run before")
self.stage = IntensifierStage.RUN_INCUMBENT
self.logger.debug("Intensify on %s", challenger)
if hasattr(challenger, "origin"):
self.logger.debug("Configuration origin: %s", challenger.origin)


Money Play. Should Red double? If doubled, should White take?
pips Red=87 White=108
(CorrectAnswer= 0/0, ErrorScore= 0)
Another classic middle game double. Red has threats and the position is volatile but that dead checker on the ace-point is a distinct weakness.
However, turning the cube activates gammons and Red will win a fair few of those so the double is very clear – it is nearly a quadruple blunder not to double here.
Equally well, Red is not favourite to close White out and White has a four-point board of his own. The take is therefore equally clear, it would be a triple blunder to drop.
So the double and the take are both very clear.
However, turning the cube activates gammons and Red will win a fair few of those so the double is very clear – it is nearly a quadruple blunder not to double here.
Equally well, Red is not favourite to close White out and White has a four-point board of his own. The take is therefore equally clear, it would be a triple blunder to drop.
So the double and the take are both very clear.
Cube analysis 2-ply cubeless equity +0.451 0.629 0.291 0.004 - 0.371 0.098 0.004 Cubeful equities: 1. Double, take +0.655 2. Double, pass +1.000 (+0.345) 3. No double +0.435 (-0.220) Proper cube action: Double, take


We collected kidneys from 188 Diversity Outbred mice (93 females and 95 males) at 6, 12, and 18 months of age (63, 62, and 63 animals respectively). The right kidney was homogenized and an aliquot was used for RNA-sequencing and another for shotgun proteomics. A week prior to sacrifice, we also collected urine samples for renal physiological phenotyping (albumin, phosphate, and creatinine levels).
We fitted a linear model for all 6,667 genes with both mRNA and protein expression information.
\[ y_{i} \sim Age_{i} + Sex_{i} + Generation_{i} \] From linear model, we recorded slope and p-value generated from each mRNA and protein in the downstream processes. Additionally for a subset of genes with both mRNA and protein information, we also extracted the slope coefficient from.
The following bash script is designed to submit an Rscript to a cluster that utilizes PBS submission. You must submit the .Rdata and name the output file in this order.
For this analysis we used:
# Make sure correct paths and used and paste below into cluster to run:
qsub -v I="./Rdata/DO188b_kidney_noprobs.RData ./Anova_output/kidney_anova_slope_output.csv",script=anova_tests_slope_pairs Rsubmit_args.sh
The figures below visualizes the distribution of p-values that indicates whether the gene have significant changes in mRNA/protein as a result of age, sex, and age:sex interaction.
We found that sex effect on mRNA was stronger than the effects of Age, which is to be expected. We also saw that the proteins seem to be strongly affected by both sex and age.
We can illustrate the direction of change in mRNA and protein expression with age using the slope calculated from the linear model in the previous section.
In our manuscript, we used clusterProfiler to identify enriched biological functions and pathways of each of the 4 significant cluster of genes. For more information please refer to our manuscript HERE. (needs link)
Previously we showed how mRNA and protein expression changed with age. We want to highlight a few genes to explain what is known as “The ecological fallacy”, whereby the correlation of the total population is not the same as individual age group correlation.
We picked Slc5a12 and Flot1 as the best examples to demonstrate this ecological fallacy.
At the whole population level, the mRNA expression of Slc5a12 decreased with age and the protein expression increased with age. However, when broken down by age groups, the mRNA and protein expressions are positively correlated. When were compare between the age groups we do not see a shift between 6 month and 12 months, but at 18 months of age the average of the group shifts to the top right. The upward shift indicates that the protein expression increased but the leftward shift indicates the decrease of mRNA expression.
In contrast to Slc5a12, Flot1 is an example where by the mRNA expression increases with age and the protein expression decreases with age at the whole population level. At the level of the individual age groups, we observe a bottom-right shift with an increase in age. The downward shift indicates a decrease in protein expression, and a rightward shift indicates a increase in mRNA expression with age.
In addition to gene expression and protein expression quantification, we also measured metabolites in the urine samples (albumin, phosphate, and creatinine) collected from the same 188 animals 1 week prior to their sacrifice. From the analysis conducted in previous sections, we identified Slc34a1, Slc34a3, and Lrp2 in group D to be of particular interest. Genes in group D have decreasing mRNA expression and increasing protein expression increasing with age at the whole population level.
Slc34a1 and Slc34a3 each encode a phosphate co-transporter (NPT2C and NPT2A) in localized in the proximal tubule for phosphate reabsorption. For both genes we observe we confirmed global expression levels of decreasing mRNA and increasing protein levels for both females and males.
We also observe positive correlation between mRNA and protein levels at individual age groups. Both Slc34a1 and Slc34a3 show a shift towards the top-left corner with increasing age, which is reflective of the observations made at the whole population level.
As expected for mediators of phosphate reabsorption, we confirmed an increase in Slc34a1 and Slc34a3 both lead to decreasing levels of urinary phosphate levels.
Lrp2 encodes for megalin, a receptor for albumin reabsorption also localized in the proximal tubule. Similar to Slc34a1 and Slc34a3 the global levels of Lrp2 mRNA and protein are respectively decreasing and increasing with age.
We also observe the global trend reflected individually with age, driven by the top-left shift with increasing age. Additionally we also observe overall higher expression level of both Lrp2 mRNA and protein in females (circle).
We were able to measure albumin levels form 120 DO mice. 90 of these mice had urinary albumin levels < 1 mg/dL and 67 animals had 0 mg/dL or below detection levels of urinary albumin. This lack of detectable albumin heavily skewed our data as it can be seen in the plot below. As these DO animals were not specifically genetically engineered to develop renal failure, we only expect a few animals with albuminuria. In our cohort of 188 DO mice, we have 5 animals with microalbuminuria (> 30 mg/dL of urinary albumin).
Using mRNA expression (eQTL) and protein expression (pQTL) as phenotypes in a quantitative trait loci (QTL) mapping approach, we were able to identify regions that regulate age- and sex-associated changes in gene expression.
For more information on QTL analyses, Karl Broman’s R/qtl2 user guide is great resource for understanding and getting started with QTL scans.
We calculated 3 different models of QTL:
Simplistically the additive QTL calculates an additive model that takes into account age, sex, generation of the animals as additive covariates.
\[ Y_{i} \sim Age_{i} + Sex_{i} + Generatio_{i}n + QTL_{gi}\] Sex-interactive QTL calculates two models, additive model and sex-interactive model, whereby the interactive model incorporates and interactive term for Sex. The sex interactive LOD scores are calculated by subtracting the additive model from the sex-interactive model.
\[ Y_{i} \sim Age_{i} + Sex_{i} + Generation_{i} + QTL_{gi} + QTL_{gi} * Sex_{i}\]
Age-interactive QTL calculates two models, additive model and age-interactive model, whereby the interactive model incorporates and interactive term for Age. The age-interactive LOD scores are calculated by subtracting the additive model from the age-interactive model.
\[ Y_{i} \sim Age_{i} + Sex_{i} + Generation_{i} + QTL_{gi} + QTL_{gi} * Age_{i}\]
In R, the the basic R/qtl2 code used for both additive and interactive QTL scan are below.
# Load QTL2 library
library(qtl2)
library(qtl2convert)
# Load probs data
load("./RNAseq_data/DO188b_kidney.RData")
# Reformat from DOQTL format to meet qtl2 format specification
probs <- probs_doqtl_to_qtl2(genoprobs, snps, pos_column = "bp")
snps$chr <- as.character(snps$chr)
snps$chr[snps$chr=="X"] <- "20" #change to numeric for creating map later
map <- map_df_to_list(map = snps, pos_column = "bp")
# Create matrix for each model
## Additive
addcovar <- model.matrix(~ Sex + Age + Generation, data=annot.samples)
## Sex-interactive
sex_intcovar <- model.matrix(~ Sex, data = annot.samples)
## Age-interactive
age_intcovar <- model.matrix(~ Age, data = annot.samples)
# scan additive model:
LODadd <- scan1(genoprobs=probs,
kinship=Glist,
pheno=expr.protein[,p],
addcovar=addcovar[,-1],
cores=3,
reml=TRUE)
# scan full model for age-interaction:
LODint <- scan1(genoprobs=probs,
kinship=Glist,
pheno=expr.protein[,p],
addcovar=addcovar[,-1],
intcovar=age_intcovar[,-1], # replace with sex_intcovar for sex-interaction
cores=3,
reml=TRUE)
# get marker with maximum LOD score difference between full and additive scans:
maxMarker <- function(full, add){
diff <- as.data.frame(full)
colnames(diff) <- "FullLOD"
diff$AddLOD <- add[,1]
diff$IntAgeLODDiff <- diff$FullLOD - diff$AddLOD
max <- diff[which(diff$IntAgeLODDiff == max(diff$IntAgeLODDiff, na.rm = TRUE)[1])[1],]
max$IntAgeChr <- str_split_fixed(rownames(max),"_",2)[,1]
max$IntAgePos <- snps[snps$marker == rownames(max),]$bp
return(max)
}
Max <- maxMarker(full = LODint, add = LODadd)
}
# output
output <- output %>% mutate(
FullLOD = Max$FullLOD,
AddLOD = Max$AddLOD,
IntAgeChr = Max$IntAgeChr,
IntAgePos = Max$IntAgePos,
IntAgeLODDiff = Max$IntAgeLODDiff
)
For every mRNA and pQTL we have expression from, we calculated the additive eQTL and pQTL models. We plotted the genomic position of the highest LOD score (y-axis) in relation to the genomic position of the gene (y-axis) for genes with LOD scores > 7.5.
We see strong clusters of eQTL and pQTL on the diagonal axis, indicating that a majority of gene expression are regulated locally (cis-QTL).
We plotted the sex-interactive eQTL and pQTL for genes with a LOD score > 7.5. Compared to the additive model, sex-associated changes in mRNA are less locally regulated, and we begin to see more distant eQTL peaks. Similarly sex-interactive pQTL show very little local regulation.
We plotted the age-interactive eQTL and pQTL for genes with a LOD score > 7.5. Age-associated changes in gene and protein expression are mainly regulated by distant genes, and we begin to see dark vertical band. This vertical cluster of eQTL on distal chromosome 12, indicate that a gene/genes on chromosome 12 pleiotropically regulates the age-associated changes in mRNA expression of many genes. Similarly, in the pQTL global map, a distant band on distal chromosome 15 seem to have similar effects on age-associated changes in protein expression.
## R version 3.5.1 (2018-07-02)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS 10.14
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] scales_1.0.0 bindrcpp_0.2.2 ggsci_2.9 gridExtra_2.3
## [5] forcats_0.3.0 stringr_1.3.1 dplyr_0.7.6 purrr_0.2.5
## [9] readr_1.1.1 tidyr_0.8.1 tibble_1.4.2 ggplot2_3.0.0
## [13] tidyverse_1.2.1
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.19 cellranger_1.1.0 pillar_1.3.0 compiler_3.5.1
## [5] plyr_1.8.4 bindr_0.1.1 tools_3.5.1 digest_0.6.17
## [9] lubridate_1.7.4 jsonlite_1.5 evaluate_0.11 nlme_3.1-137
## [13] gtable_0.2.0 lattice_0.20-35 pkgconfig_2.0.2 rlang_0.2.2
## [17] cli_1.0.1 rstudioapi_0.8 yaml_2.2.0 haven_1.1.2
## [21] withr_2.1.2 xml2_1.2.0 httr_1.3.1 knitr_1.20
## [25] hms_0.4.2 rprojroot_1.3-2 tidyselect_0.2.4 glue_1.3.0
## [29] R6_2.3.0 readxl_1.1.0 rmarkdown_1.10 modelr_0.1.2
## [33] magrittr_1.5 backports_1.1.2 htmltools_0.3.6 rvest_0.3.2
## [37] assertthat_0.2.0 colorspace_1.3-2 labeling_0.3 stringi_1.2.4
## [41] lazyeval_0.2.1 munsell_0.5.0 broom_0.5.0 crayon_1.3.4
Last updated: 25 October, 2018
Copyright © 2018 Yuka Takemon. MIT license.


Chrome Logo
Source
Kittens
Source: Sam Dutton
Flag of Mexico
Source
Repeated Background
Source
Gradient Background
Source


This project was created as a way to help combat the many challenges of the homeless population in San Francisco. Your donations will go toward organizations that are trying to better the living conditions of the homeless population in San Francisco.