Scatter search-based identification of local patterns with positive and negative correlations in gene expression data

Biclustering of Smart Building Electric Energy Consumption Data

Nowadays, smart buildings can collect data regarding the electric energy consumption, which can then be analyzed to gain insights or to predict or identify abnormal energy consumption. Numerous models are applied to face this problem but they are based on a global point of view and cannot detect local patterns of abnormal consumption. This work lies in the former option, as we propose a way to analyze energy consumption data from smart buildings. In particular, we use energy consumption data collected by various buildings over a five-year period. These data were analyzed to gain insight into the functioning of the considered buildings, with the aim of detecting anomalous situations, which could indicate that some energy usage policy should be changed or that there is a fault in the sensor network. In particular, we propose an approach based on biclustering, which allows obtaining subgroups of buildings that show a similar behaviour over a specific period of time. To the best of our knowledge, this is the first application of biclustering to energy consumption data analysis. Results confirm that the proposed approach can help policy makers in detecting irregular situations, which can provide hints on how to improve the efficiency of buildings.

Pairwise gene GO-based measures for biclustering of high-dimensional expression data

Background

Biclustering algorithms search for groups of genes that share the same behavior under a subset of samples in gene expression data. Nowadays, the biological knowledge available in public repositories can be used to drive these algorithms to find biclusters composed of groups of genes functionally coherent. On the other hand, a distance among genes can be defined according to their information stored in Gene Ontology (GO). Gene pairwise GO semantic similarity measures report a value for each pair of genes which establishes their functional similarity. A scatter search-based algorithm that optimizes a merit function that integrates GO information is studied in this paper. This merit function uses a term that addresses the information through a GO measure.

Results

The effect of two possible different gene pairwise GO measures on the performance of the algorithm is analyzed. Firstly, three well known yeast datasets with approximately one thousand of genes are studied. Secondly, a group of human datasets related to clinical data of cancer is also explored by the algorithm. Most of these data are high-dimensional datasets composed of a huge number of genes. The resultant biclusters reveal groups of genes linked by a same functionality when the search procedure is driven by one of the proposed GO measures. Furthermore, a qualitative biological study of a group of biclusters show their relevance from a cancer disease perspective.

Conclusions

It can be concluded that the integration of biological information improves the performance of the biclustering process. The two different GO measures studied show an improvement in the results obtained for the yeast dataset. However, if datasets are composed of a huge number of genes, only one of them really improves the algorithm performance. This second case constitutes a clear option to explore interesting datasets from a clinical point of view.