Abstract: Protein kinases participate in a myriad of cellular
processes of major biomedical interest. The in vivo substrate
specificity of these enzymes is a process determined by several
factors, and despite several years of research on the topic, is still
far from being totally understood. In the present work, we have
quantified the contributions to the kinase substrate specificity of
i) the phosphorylation sites and their surrounding residues in the
sequence and of ii) the association of kinases to adaptor or scaffold
proteins. We have used position-specific scoring matrices (PSSMs),
to represent the stretches of sequences phosphorylated by 93 families
of kinases. We have found negative correlations between the number
of sequences from which a PSSM is generated and the statistical
significance and the performance of that PSSM. Using a subset
of 22 statistically significant PSSMs, we have identified specificity
determinant residues (SDRs) for 86% of the corresponding kinase
families. Our results suggest that different SDRs can function as
positive or negative elements of substrate recognition by the different
families of kinases. Additionally, we have found that human proteins
with known function as adaptors or scaffolds (kAS) tend to interact
with a significantly large fraction of the substrates of the kinases to
which they associate. Based on this characteristic we have identified
a set of 279 potential adaptors/scaffolds (pAS) for human kinases,
which is enriched in Pfam domains and functional terms tightly
related to the proposed function. Moreover, our results show that
for 74.6% of the kinase–pAS association found, the pAS colocalize
with the substrates of the kinases they are associated to. Finally, we
have found evidence suggesting that the association of kinases to
adaptors and scaffolds, may contribute significantly to diminish the
in vivo substrate crossed-specificity of protein kinases. In general, our
results indicate the relevance of several SDRs for both the positive
and negative selection of phosphorylation sites by kinase families and
also suggest that the association of kinases to pAS proteins may be
an important factor for the localization of the enzymes with their set
of substrates.
Abstract: This research focus on the intrusion detection system (IDS) development which using artificial immune system (AIS) with population based incremental learning (PBIL). AIS have powerful distinguished capability to extirpate antigen when the antigen intrude into human body. The PBIL is based on past learning experience to adjust new learning. Therefore we propose an intrusion detection system call PBIL-AIS which combine two approaches of PBIL and AIS to evolution computing. In AIS part we design three mechanisms such as clonal selection, negative selection and antibody level to intensify AIS performance. In experimental result, our PBIL-AIS IDS can capture high accuracy when an intrusion connection attacks.
Abstract: The temporal nature of negative selection is an under exploited area. In a negative selection system, newly generated antibodies go through a maturing phase, and the survivors of the phase then wait to be activated by the incoming antigens after certain number of matches. These without having enough matches will age and die, while these with enough matches (i.e., being activated) will become active detectors. A currently active detector may also age and die if it cannot find any match in a pre-defined (lengthy) period of time. Therefore, what matters in a negative selection system is the dynamics of the involved parties in the current time window, not the whole time duration, which may be up to eternity. This property has the potential to define the uniqueness of negative selection in comparison with the other approaches. On the other hand, a negative selection system is only trained with “normal" data samples. It has to learn and discover unknown “abnormal" data patterns on the fly by itself. Consequently, it is more appreciate to utilize negation selection as a system for pattern discovery and recognition rather than just pattern recognition. In this paper, we study the potential of using negative selection in discovering unknown temporal patterns.
Abstract: Spatial trends are one of the valuable patterns in geo
databases. They play an important role in data analysis and
knowledge discovery from spatial data. A spatial trend is a regular
change of one or more non spatial attributes when spatially moving
away from a start object. Spatial trend detection is a graph search
problem therefore heuristic methods can be good solution. Artificial
immune system (AIS) is a special method for searching and
optimizing. AIS is a novel evolutionary paradigm inspired by the
biological immune system. The models based on immune system
principles, such as the clonal selection theory, the immune network
model or the negative selection algorithm, have been finding
increasing applications in fields of science and engineering.
In this paper, we develop a novel immunological algorithm based
on clonal selection algorithm (CSA) for spatial trend detection. We
are created neighborhood graph and neighborhood path, then select
spatial trends that their affinity is high for antibody. In an
evolutionary process with artificial immune algorithm, affinity of
low trends is increased with mutation until stop condition is satisfied.