Databases like STRING have so many predicted as well as experimental interactions data for many organisms.However there is a bias in the data which they have?.

a)The first is gene co-occurrence which is more like a circular argument.

 

b)And the second one is that all interactions that are predicted computationally are tested for experimental interactions.This leads to a bias in the interaction data available for organisms in STRING.

How do we overcome this data bias while using STRING interaction data for analysis purpose?

a)Avoiding evidence channels of gene co-occurrence is one way.Is there any other way other than this?

 

b)How to statistically say that the bias is not governing the data or any way to test statistical significance of the data bias?

Databases like STRING have so many predicted as well as experimental interactions data for many organisms.However there is a bias in the data which they have?.

a)The first is gene co-occurrence which is more like a circular argument.

b)And the second one is that all interactions that are predicted computationally are tested for experimental interactions.This leads to a bias in the interaction data available for organisms in STRING.

How do we overcome this data bias while using STRING interaction data for analysis purpose?

a)Avoiding evidence channels of gene co-occurrence is one way.Is there any other way other than this?

b)How to statistically say that the bias is not governing the data or any way to test statistical significance of the data bias?

Databases like STRING have so many predicted as well as experimental interactions data for many organisms.However there is a bias in the data which they have?.

a)The first is gene co-occurrence which is more like a circular argument.

b)And the second one is all interactions that are predicted computationally are tested for experimental interactions.This leads to a bias in the interaction data available for organisms in STRING.

How do we overcome this data bias while using STRING interaction data for analysis purpose?

a)Avoiding evidence channels of gene co-occurrence is one way.Is there any other way other than this?

b)How to statistically say that the bias is not governing the data or any way to test statistical significance of the data bias?

Databases like STRING have so many predicted as well as experimental interactions data for many organisms.However there is a bias in the data which they have?.

a)The first is gene co-occurrence which is more like a circular argument.

b)And the second one is that all interactions that are predicted computationally are tested for experimental interactions.This leads to a bias in the interaction data available for organisms in STRING.

How do we overcome this data bias while using STRING interaction data for analysis purpose?

a)Avoiding evidence channels of gene co-occurrence is one way.Is there any other way other than this?

b)How to statistically say that the bias is not governing the data or any way to test statistical significance of the data bias?