A computer-implemented method of generating a dynamic pathway map, DPM, comprising: receiving genomic patient data D; converting each of a plurality of cancer-related pathways into a distinct probabilistic model by creating a factor graph φ for each pathway, wherein the factor graph includes a plurality of entities and variables x i associated with said entities, each variable x i indicating a differential state of the respective entity in comparison to a control level of the respective entity, said differential state comprising an activated, a nominal, or a deactivated state relative to the control level, wherein the activated state is encoded as 1, the nominal state is encoded as 0, and the deactivated state is encoded as -1; wherein the factor graph further includes directed edges labelled with either positive or negative influence from a respective first entity to a respective second entity based on interactions defined in the pathway; wherein a factor is computed for each variable x i, said factor based on a value corresponding to a majority vote of the variables of parent entities of the entity corresponding to variable x i, wherein votes of the parent entities are based on the states of said parent entities multiplied by positive or negative sign according to a configuration of the respective edge, and wherein the factor indicates whether the entity corresponding to variable x i corresponds to said majority vote; computing a respective single integrated pathway activity, IPA, for every entity i based on a log-likelihood ratio for entity i being in a given state a, said log-likelihood ratio based on a prior probability (P(x i = a|φ) computed prior to observing patient's data and on a probability (P(x i = a,D|φ) that x i is in state along with observed patient's data, wherein IPA(i) is defined as IPA i = { L i 1 L i 1 > L i, − 1 and L i 1 > L i 0 − L i, − 1 L i, − 1 > L i 1 and L i, − 1 > L i 0 0 otherwise. wherein L (i, a) is is defined as L i a = log P D, x i = a | Φ P D, x i ≠ a | Φ − log P x i = a | Φ P x i ≠ a | Φ = log P D | x i = a, Φ P D | x i ≠ a, Φ.; wherein P(x i =a|φ) is defined as wherein P(x i = a, D|φ) is defined as and wherein A i (a) represents the singleton assignment set {x i =a} and S ⊂ Ai(a) X represents the set of all possible assignments of a set of variables that are consistent with the assignments in A i (a) and S ⊂ Ai(a) ∪ D X represents the set of all possible assignments of a set of variables that are consistent with the assignments in A i(a) ∪ D; and determining which IP As are significantly different from a negative distribution comprising a permutation of all patients and across the genes in the pathway. receiving genomic patient data D; converting each of a plurality of cancer-related pathways into a distinct probabilistic model by creating a factor graph φ for each pathway, wherein the factor graph includes a plurality of entities and variables x i associated with said entities, each variable x i indicating a differential state of the respective entity in comparison to a control level of the respective entity, said differential state comprising an activated, a nominal, or a deactivated state relative to the control level, wherein the activated state is encoded as 1, the nominal state is encoded as 0, and the deactivated state is encoded as -1; wherein the factor graph further includes directed edges labelled with either positive or negative influence from a respective first entity to a respective second entity based on interactions defined in the pathway; wherein a factor is computed for each variable x i, said factor based on a value corresponding to a majority vote of the variables of parent entities of the entity corresponding to variable x i, wherein votes of the parent entities are based on the states of said parent entities multiplied by positive or negative sign according to a configuration of the respective edge, and wherein the factor indicates whether the entity corresponding to variable x i corresponds to said majority vote; computing a respective single integrated pathway activity, IPA, for every entity i based on a log-likelihood ratio for entity i being in a given state a, said log-likelihood ratio based on a prior probability (P(x i = a|φ) computed prior to observing patient's data and on a probability (P(x i = a,D|φ) that x i is in state along with observed patient's data, wherein IPA(i) is defined as IPA i = { L i 1 L i 1 > L i, − 1 and L i 1 > L i 0 − L i, − 1 L i, − 1 > L i 1 and L i, − 1 > L i 0 0 otherwise. wherein L (i, a) is is defined as L i a = log P D, x i = a | Φ P D, x i ≠ a | Φ − log P x i = a | Φ P x i ≠ a | Φ = log P D | x i = a, Φ P D | x i ≠ a, Φ.; wherein P(x i =a|φ) is defined as wherein P(x i = a, D|φ) is defined as and wherein A i (a) represents the singleton assignment set {x i =a} and S ⊂ Ai(a) X represents the set of all possible assignments of a set of variables that are consistent with the assignments in A i (a) and S ⊂ Ai(a) ∪ D X represents the set of all possible assignments of a set of variables that are consistent with the assignments in A i(a) ∪ D; and determining which IP As are significantly different from a negative distribution comprising a permutation of all patients and across the genes in the pathway.