. 2023 Aug 25;24(Suppl 1):321. doi: 10.1186/s12859-023-05429-3

Table 4.

Different and common points between Iggy and MajS

	Iggy	MajS
Instance (input)	$∙$ An interaction graph, whose edges are directed and labelled as activation or inhibition. $∙$ A list of discrete observa- tions on some IG nodes.	$∙$ An interaction graph, whose edges are directed and labelled as activation or inhibition. $∙$ A list of discrete observa- tions on some IG nodes.
Search space/ Guess	$∙$ Depends on node, sign $\in$ {“ − ”, “0”, “ $+$ ”} (3 $^{\| V \|})$ $∙$ 1-influence repair added by node	$∙$ Depends on node, sign $\in$ {“ − ”, “0”, “ $+$ ”} , weight $\in$ [0, 100] (3 $^{\| V \|} \times 101^{\| V \|})$ $∙$ K-influences repair added by node
Logical rules	$∙$ Experimental observation signs are kept. $∙$ A node signed as “0” must receive only one influence signed as “0” or at least one “ $+$ ” and one “−” in- fluence. $∙$ A signed node must be jus- tified by at least one signed influence.	$∙$ Experimental observation signs are kept. $∙$ A node is signed “0” ei- ther if it only receives 0- influences or the same pro- portion of signed “ −”, “ $+$ ” influences. $∙$ A node is signed following the majority sign from all its received influences.
Optimisation	$∙$ Minimise the number of added repairs	$∙$ Minimise the number of added repairs
Projection (predicted nodes)	$∙$ Six levels of possible pre- diction: 1 - 2 notPlus (-, 0) 3 0 4 notMinus (0, $+)$ 5 $+$ 6 CHANGE ( $+$ , -)	$∙$ Majoritarian sign $∙$ Statistical information on the weight distribution (average, standard devia- tion)

Iggy

MajS

Instance (input)

$∙$ An interaction graph,

whose edges are directed and labelled as activation or inhibition.

$∙$ A list of discrete observa- tions on some IG nodes.

$∙$ An interaction graph,

whose edges are directed and labelled as activation or inhibition.

$∙$ A list of discrete observa- tions on some IG nodes.

Search space/ Guess

$∙$ Depends on node, sign $\in$

{“ − ”, “0”, “ $+$ ”} (3 $^{| V |})$

$∙$ 1-influence repair added by

node

$∙$ Depends on node, sign $\in$

{“ − ”, “0”, “ $+$ ”} , weight

$\in$ [0, 100] (3 $^{| V |} \times 101^{| V |})$

$∙$ K-influences repair added

by node

Logical rules

$∙$ Experimental observation

signs are kept.

$∙$ A node signed as “0” must receive only one influence signed as “0” or at least one “ $+$ ” and one “−” in- fluence.

$∙$ A signed node must be jus- tified by at least one signed influence.

$∙$ Experimental observation

signs are kept.

$∙$ A node is signed “0” ei- ther if it only receives 0- influences or the same pro- portion of signed “ −”, “ $+$ ” influences.

$∙$ A node is signed following the majority sign from all its received influences.

Optimisation

$∙$ Minimise the number of

added repairs

$∙$ Minimise the number of

added repairs

Projection (predicted nodes)

$∙$ Six levels of possible pre- diction:

1 -

2 notPlus (-, 0)

3 0

4 notMinus (0, $+)$

5 $+$

6 CHANGE ( $+$ , -)

$∙$ Majoritarian sign

$∙$ Statistical information on the weight distribution (average, standard devia- tion)