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Protein–protein interaction predictions using text mining methods

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TLDR
This review focuses on text-mining based computational methodologies, aiming to extract information for proteins and their interactions from public repositories such as literature and various biological databases, and comments on the biological databases which hold such information and the benchmark datasets that can be used for evaluating new tools.
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This article is published in Methods.The article was published on 2015-03-01. It has received 79 citations till now. The article focuses on the topics: Biological database & Protein–protein interaction prediction.

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Information retrieval and text mining technologies for chemistry

TL;DR: This Review provides a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting information demands of chemical information contained in scientific literature, patents, technical reports, or the web.
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The language of proteins: NLP, machine learning & protein sequences.

TL;DR: The success, promise and pitfalls of applying NLP algorithms to the study of proteins, and methods for encoding the information of proteins as text and analyzing it with NLP methods, reviewing classic concepts such as bag-of-words, k-mers/n-grams and text search.
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A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts

TL;DR: An analysis of 15 million English scientific full-text articles published during the period 1823–2016 is presented, showing the development in article length and publication sub-topics during these nearly 250 years.
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Network Medicine in the Age of Biomedical Big Data.

TL;DR: This review explores ways in which the network medicine approach, aided by phenotype-specific biomedical data, can be gainfully applied and provides a lexicon for researchers from biological sciences and network theory to come on the same page to work on research areas that require interdisciplinary expertise.
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Visualizing genome and systems biology: technologies, tools, implementation techniques and trends, past, present and future

TL;DR: The past, present and future of genomic and systems biology visualization is discussed, and the latest libraries and programming languages that enable more effective, efficient and faster approaches for visualizing biological concepts are focused on.
References
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Random Forests

TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
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Gene Ontology: tool for the unification of biology

TL;DR: The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing.
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The Pfam protein families database

TL;DR: The definition and use of family-specific, manually curated gathering thresholds are explained and some of the features of domains of unknown function (also known as DUFs) are discussed, which constitute a rapidly growing class of families within Pfam.
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A novel genetic system to detect protein-protein interactions.

TL;DR: A novel genetic system to study protein-protein interactions between two proteins by taking advantage of the properties of the GAL4 protein of the yeast Saccharomyces cerevisiae, which may be applicable as a general method to identify proteins that interact with a known protein by the use of a simple galactose selection.
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KEGG for linking genomes to life and the environment

TL;DR: KEGG PATHWAY is now supplemented with a new global map of metabolic pathways, which is essentially a combined map of about 120 existing pathway maps, and the KEGG resource is being expanded to suit the needs for practical applications.
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