Independent Component Analysis-motivated Approach to Classificatory Decomposition of Cortical Evoked Potentials

BACKGROUND: Independent Component Analysis (ICA) proves to be useful in the analysis of neural activity, as it allows for identification of distinct sources of activity. Applied to measurements registered in a controlled setting and under exposure to an external stimulus, it can facilitate analysis of the impact of the stimulus on those sources. The link between the stimulus and a given source can be verified by a classifier that is able to "predict" the condition a given signal was registered under, solely based on the components. However, the ICA's assumption about statistical independence of sources is often unrealistic and turns out to be insufficient to build an accurate classifier. Therefore, we propose to utilize a novel method, based on hybridization of ICA, multi-objective evolutionary algorithms (MOEA), and rough sets (RS), that attempts to improve the effectiveness of signal decomposition techniques by providing them with "classification-awareness." RESULTS: The preliminary results described here are very promising and further investigation of other MOEAs and/or RS-based classification accuracy measures should be pursued. Even a quick visual analysis of those results can provide an interesting insight into the problem of neural activity analysis. CONCLUSION: We present a methodology of classificatory decomposition of signals. One of the main advantages of our approach is the fact that rather than solely relying on often unrealistic assumptions about statistical independence of sources, components are generated in the light of a underlying classification problem itself

SPARSE CODING AND ROUGH SET THEORY-BASED HYBRID APPROACH TO THE CLASSIFICATORY DECOMPOSITION OF CORTICAL EVOKED POTENTIALS

ABSTRACT This paper presents a novel approach to classification of decomp osed cortical evoked potentials (EPs). The decomposition is based on learning of a sparse set of basis functions using an Artificial Neural Network (ANN). The basis functions are generated according to a probabilistic model of the data. In contrast to the traditional signal decomposition techniques (i.e. Principle Component Analysis or Independent Component Analysis), this allows for an overcomplete representation of the data (i.e. number of basis functions that is greater than the dimensionality of the input signals). Obviously, this can be of a great advantage. However, there arises an issue of selecting the most significant components from the whole collection. This is especially important in classification problems based upon the decomposed representation of the data, where only those components that provide a substantial discernibility between EPs of different groups are relevant. To deal with this problem, we propose an approach based on the Rough Set theory's (RS) feature selection mechanisms. We design a sparse coding-and RS-based hybrid system capable of signal decomposition and, based on a reduced component set, signal classification

Incorporation of biological knowledge into distance for clustering genes-1

<p><b>Copyright information:</b></p><p>Taken from "Incorporation of biological knowledge into distance for clustering genes"</p><p>Bioinformation 2007;1(10):396-405.</p><p>Published online 10 Apr 2007</p><p>PMCID:PMC1896054.</p><p></p>stered with DIANA with (triangles) and without (circles) functional informatio

Incorporation of biological knowledge into distance for clustering genes-3

<p><b>Copyright information:</b></p><p>Taken from "Incorporation of biological knowledge into distance for clustering genes"</p><p>Bioinformation 2007;1(10):396-405.</p><p>Published online 10 Apr 2007</p><p>PMCID:PMC1896054.</p><p></p>tered with DIANA with (triangles) and without (circles) functional informatio

Incorporation of biological knowledge into distance for clustering genes-0

<p><b>Copyright information:</b></p><p>Taken from "Incorporation of biological knowledge into distance for clustering genes"</p><p>Bioinformation 2007;1(10):396-405.</p><p>Published online 10 Apr 2007</p><p>PMCID:PMC1896054.</p><p></p>stered with the UPGMA method with (triangles) and without (circles) functional informatio

Domain enhanced lookup time accelerated BLAST

BACKGROUND: BLAST is a commonly-used software package for comparing a query sequence to a database of known sequences; in this study, we focus on protein sequences. Position-specific-iterated BLAST (PSI-BLAST) iteratively searches a protein sequence database, using the matches in round i to construct a position-specific score matrix (PSSM) for searching the database in round i + 1. Biegert and Söding developed Context-sensitive BLAST (CS-BLAST), which combines information from searching the sequence database with information derived from a library of short protein profiles to achieve better homology detection than PSI-BLAST, which builds its PSSMs from scratch. RESULTS: We describe a new method, called domain enhanced lookup time accelerated BLAST (DELTA-BLAST), which searches a database of pre-constructed PSSMs before searching a protein-sequence database, to yield better homology detection. For its PSSMs, DELTA-BLAST employs a subset of NCBI’s Conserved Domain Database (CDD). On a test set derived from ASTRAL, with one round of searching, DELTA-BLAST achieves a ROC(5000) of 0.270 vs. 0.116 for CS-BLAST. The performance advantage diminishes in iterated searches, but DELTA-BLAST continues to achieve better ROC scores than CS-BLAST. CONCLUSIONS: DELTA-BLAST is a useful program for the detection of remote protein homologs. It is available under the “Protein BLAST” link at http://blast.ncbi.nlm.nih.gov. REVIEWERS: This article was reviewed by Arcady Mushegian, Nick V. Grishin, and Frank Eisenhaber