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A novel and rigorous Multi-perturbation Shapley Value Analysis (MSA) method has been recently presented [12]. The method addresses the challenge of defining and calculating the functional causal contributions of elements of a biological system. This paper presents the first study applying MSA to the analysis of gene knockout data. The MSA identifies the(More)
The claim that genetic properties of neurons significantly influence their synaptic network structure is a common notion in neuroscience. The nematode Caenorhabditis elegans provides an exciting opportunity to approach this question in a large-scale quantitative manner. Its synaptic connectivity network has been identified, and, combined with cellular(More)
Perturbation studies, in which functional performance is measured after deletion, mutation, or lesion of elements of a biological system, have been traditionally employed in many fields in biology. The vast majority of these studies have been qualitative and have employed single perturbations, often resulting in little phenotypic effect. Recently, newly(More)
MOTIVATION With the increasing availability of cancer microarray data sets there is a growing need for integrative computational methods that evaluate multiple independent microarray data sets investigating a common theme or disorder. Meta-analysis techniques are designed to overcome the low sample size typical to microarray experiments and yield more valid(More)
Acknowledging that causal localization of function in a processing network requires a multi-lesion analysis, this paper presents a rigorous and efficient method for defining and calculating the functional contributions of network elements as well as their interactions. The method's applicability to biological networks is demonstrated in the investigation of(More)
BACKGROUND Recently, a conceptually new approach for analyzing gene networks, the Functional Influence Network (FIN) was presented. The FIN approach uses the measured performance of a given cellular function under different multi-perturbations, to identify the main functional pathways and interactions underlying its processing. Here we present and study an(More)
This study assesses the feasibility of using a multiperturbation analysis (MPA) approach for lesion-symptom mapping. We analyze the relative contribution of damage in different brain regions to the expression of spatial neglect, as revealed in line-bisection performance. The data set comprised of normalized lesion information and bisection test results from(More)
Identifying the functional roles of elements in a neural network is one of the first challenges in understanding neural information processing. Aiming at this goal, lesion studies have been used in neuroscience, most of them employing single lesions and hence limited in their ability to reveal the significance of interacting elements. This paper presents(More)
Background: Recently, a conceptually new approach for analyzing gene networks, the Functional Influence Network (FIN) was presented. The FIN approach uses the measured performance of a given cellular function under different multi-perturbations, to identify the main functional pathways and interactions underlying its processing. Here we present and study an(More)
Acknowledging that causal localization of function in a processing network requires a multi-lesion analysis, this paper presents a rigorous and efficient method for defining and calculating the functional contributions of network elements as well as their interactions. The method's applicability to biological networks is demonstrated in the investigation of(More)