A novel sparse coding algorithm for classification of tumors based on gene expression data

Abstract

High-dimensional genomic and proteomic data play an important role in many applications in medicine such as prognosis of diseases, diagnosis, prevention and molecular biology, to name a few. Classifying such data is a challenging task due to the various issues such as curse of dimensionality, noise and redundancy. Recently, some researchers have used the sparse representation (SR) techniques to analyze high-dimensional biological data in various applications in classification of cancer patients based on gene expression datasets. A common problem with all SR-based biological data classification methods is that they cannot utilize the topological (geometrical) structure of data. More precisely, these methods transfer the data into sparse feature space without preserving the local structure of data points. In this paper, we proposed a novel SR-based cancer classification algorithm based on gene expression data that takes into account the geometrical information of all data. Precisely speaking, we incorporate the local linear embedding algorithm into the sparse coding framework, by which we can preserve the geometrical structure of all data. For performance comparison, we applied our algorithm on six tumor gene expression datasets, by which we demonstrate that the proposed method achieves higher classification accuracy than state-of-the-art SR-based tumor classification algorithms.

Affine Transformation Based Ontology Sparse Vector Learning Algorithm

“…When it comes to the field of chemical and biology, the situation may become very complex since we need to deal with high dimensional data or big data. Under this background, sparse vector learning algorithms are introduced in biology and chemical ontology computation (see Afzali et al [17], Khormuji, and Bazrafkan [18], Ciaramella and Borzi [19], Lorincz et al [20], Saadat et al [21], Yamamoto et al [22], Lorintiu et al [23], Mesnil and Ruzzene [24], Gopi et al [25], and Dowell and Pinson [26] for more details). For example, if we aim to find what kind of genes causes a certain genetic disease, there are millions of genes in human's bodies and the computation task is complex and tough.…”

Sparse representation learning derives biological features with explicit gene weights from the Allen Mouse Brain Atlas

“…Essentially, we asked if it was possible to generate representations of biology from transcriptomic data, in a single step, without implicit priors. For a more advanced approach to dimensionality reduction and clustering, we explored “sparse” learning methods, optimized for building localized features from a minimal number of input elements sparsely distributed within a large dataset [ 11 ].…”

Sparse Representation Learning Derives Biological Features with Explicit Gene Weights from the Allen Mouse Brain Atlas

“…Essentially, we asked if it was possible to generate representations of biology from a transcriptomic data, in a single step, without implicit priors. To eliminate the need for dimensionality reduction and clustering, we explored "sparse" learning methods, optimized for building localized features from a minimal number of input elements sparsely distributed within a large dataset (Kolali Khormuji and Bazrafkan, 2016).…”