Predicting potential target genes in molecular biology experiments using machine learning and multifaceted data sources.

Experimental analysis of functionally related genes is key to understanding biological phenomena. The selection of genes to study is a crucial and challenging step, as it requires extensive knowledge of the literature and diverse biomedical data resources. Although software tools that predict relationships between genes are available to accelerate this process, they do not directly incorporate experiment information derived from the literature.

[Deep Learning and Natural Language Processing].

The field of natural language processing (NLP) has seen rapid advances in the past several years since the introduction of deep learning techniques. A variety of NLP tasks including syntactic parsing, machine translation, and summarization can now be performed by relatively simple combinations of general neural network models such as recurrent neural networks and attention mechanisms. This manuscript gives a brief introduction to deep learning and an overview of the current deep learning-based NLP technology.

Identifying synonymy between relational phrases using word embeddings.

Many text mining applications in the biomedical domain benefit from automatic clustering of relational phrases into synonymous groups, since it alleviates the problem of spurious mismatches caused by the diversity of natural language expressions. Most of the previous work that has addressed this task of synonymy resolution uses similarity metrics between relational phrases based on textual strings or dependency paths, which, for the most part, ignore the context around the relations. To overcome this shortcoming, we employ a word embedding technique to encode relational phrases.

Wide-coverage relation extraction from MEDLINE using deep syntax.

Background: Relation extraction is a fundamental technology in biomedical text mining. Most of the previous studies on relation extraction from biomedical literature have focused on specific or predefined types of relations, which inherently limits the types of the extracted relations. With the aim of fully leveraging the knowledge described in the literature, we address much broader types of semantic relations using a single extraction framework.

Discovering and visualizing indirect associations between biomedical concepts.

Motivation: Discovering useful associations between biomedical concepts has been one of the main goals in biomedical text-mining, and understanding their biomedical contexts is crucial in the discovery process. Hence, we need a text-mining system that helps users explore various types of (possibly hidden) associations in an easy and comprehensible manner.

Results: This article describes FACTA+, a real-time text-mining system for finding and visualizing indirect associations between biomedical concepts from MEDLINE abstracts.

AGRA: analysis of gene ranking algorithms.

Unlabelled: Often, the most informative genes have to be selected from different gene sets and several computer gene ranking algorithms have been developed to cope with the problem. To help researchers decide which algorithm to use, we developed the analysis of gene ranking algorithms (AGRA) system that offers a novel technique for comparing ranked lists of genes. The most important feature of AGRA is that no previous knowledge of gene ranking algorithms is needed for their comparison.

Integration of metabolic databases for the reconstruction of genome-scale metabolic networks.

Background: Genome-scale metabolic reconstructions have been recognised as a valuable tool for a variety of applications ranging from metabolic engineering to evolutionary studies. However, the reconstruction of such networks remains an arduous process requiring a high level of human intervention. This process is further complicated by occurrences of missing or conflicting information and the absence of common annotation standards between different data sources.

PathText: a text mining integrator for biological pathway visualizations.

Metabolic and signaling pathways are an increasingly important part of organizing knowledge in systems biology. They serve to integrate collective interpretations of facts scattered throughout literature. Biologists construct a pathway by reading a large number of articles and interpreting them as a consistent network, but most of the models constructed currently lack direct links to those articles.

Accelerating the annotation of sparse named entities by dynamic sentence selection.

Background: Previous studies of named entity recognition have shown that a reasonable level of recognition accuracy can be achieved by using machine learning models such as conditional random fields or support vector machines. However, the lack of training data (i.e.

How to make the most of NE dictionaries in statistical NER.

Background: When term ambiguity and variability are very high, dictionary-based Named Entity Recognition (NER) is not an ideal solution even though large-scale terminological resources are available. Many researches on statistical NER have tried to cope with these problems. However, it is not straightforward how to exploit existing and additional Named Entity (NE) dictionaries in statistical NER.

FACTA: a text search engine for finding associated biomedical concepts.

Unlabelled: FACTA is a text search engine for MEDLINE abstracts, which is designed particularly to help users browse biomedical concepts (e.g. genes/proteins, diseases, enzymes and chemical compounds) appearing in the documents retrieved by the query.

Normalizing biomedical terms by minimizing ambiguity and variability.

Background: One of the difficulties in mapping biomedical named entities, e.g. genes, proteins, chemicals and diseases, to their concept identifiers stems from the potential variability of the terms.

Filling the gaps between tools and users: a tool comparator, using protein-protein interaction as an example.

Recently, several text mining programs have reached a near-practical level of performance. Some systems are already being used by biologists and database curators. However, it has also been recognized that current Natural Language Processing (NLP) and Text Mining (TM) technology is not easy to deploy, since research groups tend to develop systems that cater specifically to their own requirements.

Learning string similarity measures for gene/protein name dictionary look-up using logistic regression.

Motivation: One of the bottlenecks of biomedical data integration is variation of terms. Exact string matching often fails to associate a name with its biological concept, i.e.

Automatic recognition of topic-classified relations between prostate cancer and genes using MEDLINE abstracts.

Background: Automatic recognition of relations between a specific disease term and its relevant genes or protein terms is an important practice of bioinformatics. Considering the utility of the results of this approach, we identified prostate cancer and gene terms with the ID tags of public biomedical databases. Moreover, considering that genetics experts will use our results, we classified them based on six topics that can be used to analyze the type of prostate cancers, genes, and their relations.

Extraction of gene-disease relations from Medline using domain dictionaries and machine learning.

We describe a system that extracts disease-gene relations from Medline. We constructed a dictionary for disease and gene names from six public databases and extracted relation candidates by dictionary matching. Since dictionary matching produces a large number of false positives, we developed a method of machine learning-based named entity recognition (NER) to filter out false recognitions of disease/gene names.

Improving the performance of dictionary-based approaches in protein name recognition.

Dictionary-based protein name recognition is often a first step in extracting information from biomedical documents because it can provide ID information on recognized terms. However, dictionary-based approaches present two fundamental difficulties: (1) false recognition mainly caused by short names; (2) low recall due to spelling variations. In this paper, we tackle the former problem using machine learning to filter out false positives and present two alternative methods for alleviating the latter problem of spelling variations.

Indexing anatomical concepts to OMIM Clinical Synopsis using the UMLS Metathesaurus.

As a first step toward the quantitative comparison of clinical features of diseases, we indexed the text descriptions in the Clinical Synopsis section of the Online Mendelian Inheritance in Man (OMIM) with concepts for the body parts, organs, and tissues contained in the Metathesaurus of the Unified Medical Language System (UMLS). We also indexed the text with the diseases and disorders having links to body parts specified in the thesaurus. The vocabulary size was approximately 177,540 representations for 81,435 concepts, and 2,161 concepts were indexed to 3,779 OMIM entries.