Automated Extraction of Information From Texts of Scientific Publications: Insights Into HIV Treatment Strategies

Abstract

Text analysis can help to identify named entities (NEs) of small molecules, proteins, and genes. Such data are very important for the analysis of molecular mechanisms of disease progression and development of new strategies for the treatment of various diseases and pathological conditions. The texts of publications represent a primary source of information, which is especially important to collect the data of the highest quality due to the immediate obtaining information, in comparison with databases. In our study, we aimed at the development and testing of an approach to the named entity recognition in the abstracts of publications. More specifically, we have developed and tested an algorithm based on the conditional random fields, which provides recognition of NEs of (i) genes and proteins and (ii) chemicals. Careful selection of abstracts strictly related to the subject of interest leads to the possibility of extracting the NEs strongly associated with the subject. To test the applicability of our approach, we have applied it for the extraction of (i) potential HIV inhibitors and (ii) a set of proteins and genes potentially responsible for viremic control in HIV-positive patients. The computational experiments performed provide the estimations of evaluating the accuracy of recognition of chemical NEs and proteins (genes). The precision of the chemical NEs recognition is over 0.91; recall is 0.86, and the F1-score (harmonic mean of precision and recall) is 0.89; the precision of recognition of proteins and genes names is over 0.86; recall is 0.83; while F1-score is above 0.85. Evaluation of the algorithm on two case studies related to HIV treatment confirms our suggestion about the possibility of extracting the NEs strongly relevant to (i) HIV inhibitors and (ii) a group of patients i.e., the group of HIV-positive individuals with an ability to maintain an undetectable HIV-1 viral load overtime in the absence of antiretroviral therapy. Analysis of the results obtained provides insights into the function of proteins that can be responsible for viremic control. Our study demonstrated the applicability of the developed approach for the extraction of useful data on HIV treatment.

Chemical named entity recognition in the texts of scientific publications using the naïve Bayes classifier approach

“…Most AI-based approaches initially convert text into vectors or use sparse word text representation created with preprocessing of a text corpus, and vector preparation (for instance, such approaches include word embedding preparation or the one-hot-encoding technique). It should be noted that the performance of CNER using the naïve-Bayes approach, in general, is comparable with most of earlier published methods [ 16 , 18 , 22 – 25 ], while it is slightly lower comparing to some other approaches based on the results of fivefold CV [ 19 , 45 , 46 ].…”

“…Many various artificial intelligence (AI) approaches aimed at chemical and biological named entity recognition have been developed [ 15 , 18 , 21 ]. Most approaches that have been under recent development for several years are based on the usage of neural networks with different variants of long-short term memory (LSTM) architecture or conditional random fields (CRF) [ 16 , 42 ].…”

“…An obvious disadvantage of the rule-based and dictionary-based methods is a limited number of CNEs that can be recognized due to the fixed size of dictionaries or rule numbers. Machine learning or artificial intelligence approaches [ 16 , 17 ] mainly use support vector machines [ 18 ] or artificial neural networks [ 8 , 14 ] including deep learning architectures [ 19 ]. Typically, these methods can reach an accuracy of approximately 85–95% [ 8 , 14 , 17 , 18 ].…”

“…There is a constant need in development of approaches providing possibility of their use by many researchers in the bridging field of chemoinformatics including medicinal chemistry, computational biology, drug discovery, material science, etc. The new methods aimed at easy-to-use, accurate and fast CNER are still in demand [ 16 ].…”

“…Some methods are sensitive to imbalanced data [20]. Long-short term memory networks (LSTM) [12][13][14][21][22][23][24] or conditional random fields (CRF) [16,25] are efficiently applied to the task of named entity extraction. The architecture of neural networks can be modified according to the particular task of CNER [26][27][28].…”