5 Cardio-oncology: Network-Based Prediction …
85
DisGeNET, DisGeNET is a comprehensive database for collecting diseaseassociated genes [86]. In October 26, 2018, DisGeNET contains over 561,119 associations connecting 17,074 genes and over 20,000 diseases, disorders, and traits by
integrating expert-curated databases and text-mined data.
To improve the data quality during data integration, medical terms of diseases,
disorders, and traits are often annotated by Medical Subject Headings (MeSH) and
Unified Medical Language System (UMLS) vocabularies (https://www.nlm.nih.gov/
mesh/MBrowser.html) [87]. In addition, protein-coding genes are further annotated
by gene Entrez ID, chromosome location, and the official gene symbols from the
National Center for Biotechnology Information (NCBI) database [88]. A detailed
description of disease-gene annotation data integration is provided in Table 5.2.
5.2.5 Network Proximity
Given the set of drug targets, X, and the set of disease proteins, Y, we can calculate the network topological distance d(x, y) between nodes x and y in the human
protein-protein interactome. In general, there are four different distance measures
that take into account the path lengths between drug targets (X) and the set of disease
proteins (Y ): (a) the closest measure, representing the average shortest path length
between targets of X and the nearest proteins of Y; (b) the shortest measure, representing the average shortest path length among all targets of drugs; (c) the kernel
measure, down-weighting longer paths via an exponential penalty; and (d) the center measure, representing the shortest path length among all targets of drugs with
the greatest closeness centrality among proteins in X and Y. We define those four
distance measures in Eqs. (5.2–5.6).
Closest, cd XY =
1
X + Y
⎛
⎝
x∈X
min
y∈Y
d(x, y) +
y∈Y
min
x∈X
d(x, y)
⎞
⎠
(5.2)
Shortest, sd XY =
1
X + Y
x∈X,y∈Y
d(x, y)
(5.3)
Kernel, kd XY =
−1
X + Y
⎛
⎝
x∈X
ln
y∈Y
e
−(d(x,y)+1)
Y
+
y∈Y
ln
x∈X
e
−(d(x,y)+1)
X
⎞
⎠
(5.4)
Centre,
cd
XY
= d(centre X , centre Y )
(5.5)
where centre Y , the topological center of X, is defined as
centre Y = argmin u∈Y
y∈Y
d(y, u)
(5.6)
85
DisGeNET, DisGeNET is a comprehensive database for collecting diseaseassociated genes [86]. In October 26, 2018, DisGeNET contains over 561,119 associations connecting 17,074 genes and over 20,000 diseases, disorders, and traits by
integrating expert-curated databases and text-mined data.
To improve the data quality during data integration, medical terms of diseases,
disorders, and traits are often annotated by Medical Subject Headings (MeSH) and
Unified Medical Language System (UMLS) vocabularies (https://www.nlm.nih.gov/
mesh/MBrowser.html) [87]. In addition, protein-coding genes are further annotated
by gene Entrez ID, chromosome location, and the official gene symbols from the
National Center for Biotechnology Information (NCBI) database [88]. A detailed
description of disease-gene annotation data integration is provided in Table 5.2.
5.2.5 Network Proximity
Given the set of drug targets, X, and the set of disease proteins, Y, we can calculate the network topological distance d(x, y) between nodes x and y in the human
protein-protein interactome. In general, there are four different distance measures
that take into account the path lengths between drug targets (X) and the set of disease
proteins (Y ): (a) the closest measure, representing the average shortest path length
between targets of X and the nearest proteins of Y; (b) the shortest measure, representing the average shortest path length among all targets of drugs; (c) the kernel
measure, down-weighting longer paths via an exponential penalty; and (d) the center measure, representing the shortest path length among all targets of drugs with
the greatest closeness centrality among proteins in X and Y. We define those four
distance measures in Eqs. (5.2–5.6).
Closest, cd XY =
1
X + Y
⎛
⎝
x∈X
min
y∈Y
d(x, y) +
y∈Y
min
x∈X
d(x, y)
⎞
⎠
(5.2)
Shortest, sd XY =
1
X + Y
x∈X,y∈Y
d(x, y)
(5.3)
Kernel, kd XY =
−1
X + Y
⎛
⎝
x∈X
ln
y∈Y
e
−(d(x,y)+1)
Y
+
y∈Y
ln
x∈X
e
−(d(x,y)+1)
X
⎞
⎠
(5.4)
Centre,
cd
XY
= d(centre X , centre Y )
(5.5)
where centre Y , the topological center of X, is defined as
centre Y = argmin u∈Y
y∈Y
d(y, u)
(5.6)