Discovery of Gene-disease Associations from Biomedical Texts

Abstract

Due to the ever-expanding growth of biomedical publications, biologists have to retrieve up-to-date information from vast literatures to ensure they do not neglect certain significant publications. It becomes more and more important to deal with the extraction problem from the biomedical texts in an automatic way. The paper focuses on automatically identifying the relationships between human genetic diseases and genes from the biomedical literatures. The experimental data is retrieved from Mendelian Inheritance in Man (MIM) literatures of morbid in Online Mendelian Inheritance in Man (OMIM) database. We propose a hybrid method combining the rule learning and the statistical techniques. To collect the corpus used in the research, the first step is to find the sentences that include both the related human genetic diseases and genes mentioned from the morbid file, and they are regarded as the correct sentences. In the second step, the sentences that neither have the related human genetic diseases nor the genes mentioned from the morbid file are randomly selected, and they are regarded as the incorrect sentences. Next, the Memory-Based Shallow Parser is utilized to analyze these sentences to get some information in order to find rules in the following step. Then, some learning rules are obtained with a rule learner, ALEPH system. These generated rules are applied to catch the pairs of human genetic diseases and genes within one sentence. In the following, the study proposes a statistical approach, called Z-score method, to determine whether the pairs are valid or not. Finally, the experiments are made with considering some constraints and different numbers of rules. Furthermore, the evaluation metrics in the experiments are precision, recall rates, and F-scores.

Literature Evidence in Open Targets – a target validation platform

“…For example in [9] different weights are assigned to the different features for calculating the confidence scores of the identified associations. In our study, we assign weights from the range of [1][2][3][4][5][6][7][8][9][10] which is wide enough to pick different weights for different sections based on their potential confidence. The following formulas, CS1 and CS2 are used to calculate the confidence scores for abstracts and full text articles respectively:…”

“…Our main aim is to provide a scalable, robust and continuous text-mining service to the community for a real-world and very important applicationtarget validation. Many of the previous studies focused on extracting gene-disease association from the literature [5][6][7]. However, only a few of them specifically focused on developing methods for integrated resources; DisGeNET [8] and DISEASES [9] for example cover various types of evidence for target validation.…”

Automatic extraction of gene-disease associations from literature using joint ensemble learning

“…From among this, one of the long-standing goals of computational biology is evidently discovering the roles of candidate genes associated with a specific disease [ 11 ]. Researchers approached the problem of this relation extraction task by implementing certain techniques that can be broadly classified as a pattern or rule-based [ 12 ], co-occurrence statistics based [ 13 , 14 ] and supervised learning approaches [ 15 – 17 ]. Among these supervised learning approaches are popular and in supervised learning, a set of features that can reflect the relationship between the entities along with a kernel function is used for relation extraction [ 15 – 17 ].…”

Literature evidence in open targets - a target validation platform

“…Our main aim is to provide a scalable, robust and continuous text-mining service to the community for a real-world and very important application—target validation. Many of the previous studies focused on extracting gene-disease association from the literature [ 5 – 7 ]. However, only a few of them specifically focused on developing methods for integrated resources; DisGeNET [ 8 ] and DISEASES [ 9 ] for example cover various types of evidence for target validation.…”