A Review of Recent Advancement in Integrating Omics Data with Literature Mining towards Biomedical Discoveries.
In the past decade, the volume of "omics" data generated by the different high-throughput technologies has expanded exponentially. The managing, storing, and analyzing of this big data have been a great challenge for the researchers, especially when moving towards the goal of generating testable data-driven hypotheses, which has been the promise of the high-throughput experimental techniques. Different bioinformatics approaches have been developed to streamline the downstream analyzes by providing independent information to interpret and provide biological inference. Text mining (also known as literature mining) is one of the commonly used approaches for automated generation of biological knowledge from the huge number of published articles. In this review paper, we discuss the recent advancement in approaches that integrate results from omics data and information generated from text mining approaches to uncover novel biomedical information.
Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, USA.
Kalpana
K
Raja
Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, USA.
Matthew
M
Patrick
Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, USA.
Yilin
Y
Gao
Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, USA.
Desmond
D
Madu
Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, USA.
Yuyang
Y
Yang
Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
Lam C
0000-0003-1627-5722
LC
Tsoi