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How is it used for drug discovery and development

Implementing an NLP pipeline can be completed with packages such as NLTK (Natural Language Toolkit)100 and spacy101 in python102 and there are many tutorial on line.103 This tutorial is summarized here. The first step with natural language processing is to process sentences one word at a time. This is known as word tokenization. For example, take the sentence “What drugs were approved last year?”, you would split this into individual word tokens; “what,” “drugs,” “were,” “approved,” “last,” “year” and “?”. We can then remove punctuation which is often useful as it is treated as a separate token and you may find punctuation being highlighted as the most common token. However, punctuation can really affect the meaning of a sentence. It is also possible to remove stop words which are those that occur frequently such as “and” or “the” and therefore can be beneficial to remove to focus on the more informative text. Next, we predict the parts of speech for example determining the type of word (noun, adjective, etc.) and how each word is separated. Following this, lemmatization is performed where we reduce a word to its simplest form. For example, run, runner and running would be reduced to the lemma formation run. These all are referring to the same concept and reducing to the lemma formation helps a computer interpret as such. The next stage is known as dependency parsing which works out how all the words within a sentence relate to each other. Following this, there is a range of analysis we can perform next such as named entity recognition where a model predicts what each word (or if you have done further processing steps to identify phrases rather than single words) refers to such as names, locations, organizations, and objects. Steps like coreference resolution (identifying expressions that are related to a particular entity)104 can be used to identify what we are talking about even after the name has been mentioned for example: “Random forest is a popular machine learning algorithm. It can perform both classification and regression tasks” The “it” on its own may be difficult to determine what we are talking about but taking the whole two sentences we can understand that in this case “it” refers to the random forest algorithm. This step is difficult and as technology improves—we should expect to see this improve.

The use of ontologies (relationship structure within a concept) decrease linguistic ambiguity by mapping words to standardized terms and to establish a hierarchy of lower and higher-level terms105 MedDRA is an example of the medical dictionary used by HCPs, pharmaceutical companies and regulators.106 Another example includes SNOMED CT, which standardized clinical terminology with the use of concept codes, descriptions, relationships, and reference sets.107 One can download more ontologies dedicated to drug safety-related NLP from online repositories, for example, BioPortal or via NCBO Ontology Recommender Service.

Several NLP techniques have become standard tools available in text processing tools. For example, a search engine such as Apache Solr has built-in functions that allow for: tokenization (splitting text into tokens), stemming (removing stems from words), ignoring diacritics (which sometimes is helpful for non-English texts), conversion to lowercase words, applying phonetic algorithms like Metaphone. Omics technologies generate a lot of data that needs computational approaches to pull out the useful information, by means of data mining.23 These data can be used to help identify and validate potential drug targets.

Another essential NLP method in NLP is building the n-gram model of a corpus (a collection of texts). N-grams are tuples (2-gram is a doublet, 3-gram is triplet, and 4-gram is a quadruplet) of letters or words that appear consecutively in the original text, for example, when the text is “GAATTC,” the 3-grams are “GAA,” “AAT,” “ATT,” and “TTC”. N-grams are most commonly used in machine translations and computational biology, especially in analyzing DNA and RNA sequences (e.g., finding sequences with similar n-gram profiles). A generalization of an n-gram is a skip-gram in which the elements constituting a group do not have to be next to each other in the original sequence [e.g., grouping Nth word with (N  +  2)th word]. Google OpenRefine free online tool allows user to apply n-gram fingerprints for noisy data curation, transformation, and mining.

N-gram is a special case of Markov models. A simple Markov model called Markov chain is a set of possible states together with probabilities of transitioning between two states (every pair A, B of states has attached probabilities of the transition A  →  B and B  →  A). Markov models identified high cumulative toxicity during a phase II trial ifosfamide plus doxorubicin and granulocyte colony-stimulating factor in soft tissue sarcoma in female patients.108

Another NLP technique which has become popular in recent years is word2vec. It relies on word n-grams or skip-grams to find which words have a similar meaning. word2vec relies on the distributional hypothesis: that words with similar distributions in sentences have similar meanings. The internal representation of every word in such word2vec model is a numerical vector, usually with hundreds or more dimensions (hence the “vec” part in the name of the method). An interesting property of this method is that the word vectors can be assessed for their similarity (e.g., by computing a dot product of two vectors), or even added or subtracted (e.g., a word2vec model can be trained to compute that “king − man + woman = queen” which in other words could be expressed as “king is to man what queen is to woman”). This model has lots of potential in natural language applications, but there are also other applications such as BioVec, an application of word2vec to proteins and gene vectors.109 Extracting drug-drug interactions110 and drug repurposing111 are two more examples of word2vec application.