Literature Based Discovery (LBD): Towards Hypothesis Generation and Knowledge Discovery in Biomedical Text Mining

Biomedical knowledge is growing in an astounding pace with a majority of this knowledge is represented as scientific publications. Text mining tools and methods represents automatic approaches for extracting hidden patterns and trends from this semi structured and unstructured data. In Biomedical Text mining, Literature Based Discovery (LBD) is the process of automatically discovering novel associations between medical terms otherwise mentioned in disjoint literature sets. LBD approaches proven to be successfully reducing the discovery time of potential associations that are hidden in the vast amount of scientific literature. The process focuses on creating concept profiles for medical terms such as a disease or symptom and connecting it with a drug and treatment based on the statistical significance of the shared profiles. This knowledge discovery approach introduced in 1989 still remains as a core task in text mining. Currently the ABC principle based two approaches namely open discovery and closed discovery are mostly explored in LBD process. This review starts with general introduction about text mining followed by biomedical text mining and introduces various literature resources such as MEDLINE, UMLS, MESH, and SemMedDB. This is followed by brief introduction of the core ABC principle and its associated two approaches open discovery and closed discovery in LBD process. This review also discusses the deep learning applications in LBD by reviewing the role of transformer models and neural networks based LBD models and its future aspects. Finally, reviews the key biomedical discoveries generated through LBD approaches in biomedicine and conclude with the current limitations and future directions of LBD.Comment: 43 Pages, 5 Figures, 4 Table

A Survey on Adaptation Strategies for Mutation and Crossover Rates of Differential Evolution Algorithm

Differential Evolution (DE), the well-known optimization algorithm, is a tool under the roof of Evolutionary Algorithms (EAs) for solving non-linear and non-differential optimization problems. DE has many qualities in its hand, which are attributing to its popularity. DE also is known for its simplicity in solving the given problem with few control parameters: the population size (NP), the mutation rate (F) and the crossover rate (Cr). To avoid the difficulty involved in setting of suitable values for NP, F and Cr many parameter adaptation strategies are proposed in the literature. This paper is to present the working principle of the parameter adaptation strategies of F and Cr. The adaptation strategies are categorized based on the logic used by the authors, and clear insights about all the categories are presented

Comparison of our method with (Li et al [30]) in AIMed Corpus.

<p>Comparison of our method with (Li et al [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0187379#pone.0187379.ref030" target="_blank">30</a>]) in AIMed Corpus.</p

Work flow for feature extraction in both feature based kernel and DSTK.

<p>Work flow for feature extraction in both feature based kernel and DSTK.</p

Comparison of our method with other kernel based methods.

<p>Comparison of our method with other kernel based methods.</p

Word and distance feature vector.

<p>Word and distance feature vector.</p

List of corpora used for evaluation.

<p>List of corpora used for evaluation.</p

Complex sentences extracted while annotating PPI.

<p>Complex sentences extracted while annotating PPI.</p

Experimental results on three kernel feature based (K_fea), DSTK (K_DSTK) and composite (K_ckl).

<p>Experimental results on three kernel feature based (K_fea), DSTK (K_DSTK) and composite (K_ckl).</p