{'fraction_non_alphanumeric': 0.020076481835564052, 'fraction_numerical': 0.0, 'mean_word_length': 5.384146341463414, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Rough Overlapping Biclustering of Gene Expression Data

A great number of biclustering algorithms have been proposed for analyzing gene expression data. Many of them assume to find exclusive biclusters whose subsets of genes are co-regulated under subsets of conditions without intersection. This is not consistent with a general understanding of biological processes that many genes participate in multiple different processes. Therefore nonexclusive biclustering algorithms are required. In this paper we present a novel approach (ROB) to find potentially overlapping biclusters in the framework of generalized rough sets. Our scheme mainly consists of two phases. First, we generate a set of highly coherent seeds (original biclusters) based on two-way rough k-means clustering. And then, the seeds are iteratively adjusted (enlarged or degenerated) by adding or removing genes and conditions based on a proposed criterion. We illustrate the method on yeast gene expression data. The experiments demonstrate the effectiveness of this approach.