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Gene expression data from microarray experiments is widely used for large scale gene expression analysis which facilitates the investigation of fundamental biological processes at molecular level. Such an investigation may be helpful for various biological purposes including disease diagnosis and prognosis, biomarker detection, differentially expressed gene detection, and predicting survival rate...
Biomedical Named Entity Recognition (Bio-NER) is an important subtask of Biomedical Text Mining (BioTM), where the performance of further tasks, such as relation extraction, protein-protein interaction and hypothesis generation depend on the performance of Bio-NER. Bio-NER involves determining the biomedical named entities, such as DNA, RNA, cell types, gene and protein present in the biomedical research...
The development of tools and techniques for automatic processing of natural language texts at different levels for various applications including machine translation needs to be addressed with at most priority. Sandhi splitter is one such automated tool which acts as a preprocessing tool for morphological analyzer that identifies the morpheme boundaries in a compound word based on the Sandhi rules...
The advancement in high-throughput microarray experiments has paved a way for several transcriptomic studies across the globe by several researchers in the area of functional genomics, molecular genetics, gene discovery, differentially expressed gene detection, diagnosis and prognosis etc. As a result, the tremendous amount of data that has been produced and accumulated in various public repositories...
Gene expression data suffer from the curse of dimensionality due to the presence of several thousands of genes (features) but a small number of samples. This problem of large feature space is addressed by feature selection algorithms which aim at finding a comparatively small set of significant features by removing the redundant and irrelevant features thereby increasing the performance (e.g., higher...
Microarray technology makes it possible to measure expression level of thousands of genes simultaneously in an efficient and inexpensive manner. However, due to various complexities in processing microarrays, expression information of various genes may be missing due to unreliable measurements. The occurrence of missing values in gene expression data can adversely affect downstream analyses such as...
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