Effective use of latent semantic indexing and computational linguistics in biological and biomedical applications

Chen, Hongyu; Martin, Bronwen; Daimon, Caitlin M.; Maudsley, Stuart

doi:10.3389/fphys.2013.00008

Cited by 31 publications

(28 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Latent semantic indexingbased analysis correlates the strength of association between specific genes in a dataset with user-defined interrogation terms. Individual processing investigates the links between scientifically relevant words and individual transcripts, while collective processing generates a hierarchical word cloud indicating the word groups most strongly associated with the entire input dataset (Chen et al, 2013b). As illustrated, at the individual processing level, arrestin biased signaling correlates most prominently with regulation of protein kinase activity and phosphorylation (Fig.…”

Section: Understanding Bias At a Systems Levelmentioning

confidence: 99%

Fulfilling the Promise of "Biased" G Protein–Coupled Receptor Agonism

2015

Self Cite

View full text Add to dashboard Cite

The fact that over 30% of current pharmaceuticals target heptahelical G protein-coupled receptors (GPCRs) attests to their tractability as drug targets. Although GPCR drug development has traditionally focused on conventional agonists and antagonists, the growing appreciation that GPCRs mediate physiologically relevant effects via both G protein and non-G protein effectors has prompted the search for ligands that can "bias" downstream signaling in favor of one or the other process. Biased ligands are novel entities with distinct signaling profiles dictated by ligand structure, and the potential prospect of biased ligands as better drugs has been pleonastically proclaimed. Indeed, preclinical proof-of-concept studies have demonstrated that both G protein and arrestin pathwayselective ligands can promote beneficial effects in vivo while simultaneously antagonizing deleterious ones. But along with opportunity comes added complexity and new challenges for drug discovery. If ligands can be biased, then ligand classification becomes assay dependent, and more nuanced screening approaches are needed to capture ligand efficacy across several dimensions of signaling. Moreover, because the signaling repertoire of biased ligands differs from that of the native agonist, unpredicted responses may arise in vivo as these unbalanced signals propagate. For any given GPCR target, establishing a framework relating in vitro efficacy to in vivo biologic response is crucial to biased drug discovery. This review discusses approaches to describing ligand efficacy in vitro, translating ligand bias into biologic response, and developing a systemslevel understanding of biased agonism in vivo, with the overall goal of overcoming current barriers to developing biased GPCR therapeutics.

show abstract

Section: Understanding Bias At a Systems Levelmentioning

confidence: 99%

Fulfilling the Promise of "Biased" G Protein–Coupled Receptor Agonism

2015

Self Cite

View full text Add to dashboard Cite

show abstract

“…ω denotes the actual observation words in an applicable topic (Blei et al, 2003). and latent semantic analysis suffer information loss due to dimension reduction (Babaee, Mihai, & Gerhard, 2013;Chen, Martin, Daimon, & Maudsley, 2013 the biomimicry process that uses this tool is proposed to assist designers and engineers in performing technology development and product design. Figure 4 shows the overall framework of this research with two modules.…”

Section: Biomimicry and Bio-trizmentioning

confidence: 99%

“…LDA has the most effective Document–Topic–Keyword analysis form in text‐mining fields. In traditional text‐mining approaches, keyword analysis and latent semantic analysis suffer information loss due to dimension reduction (Babaee, Mihai, & Gerhard, ; Chen, Martin, Daimon, & Maudsley, ). However, LDA complements this limitation and shows outstanding performance.…”

Section: Introductionmentioning

confidence: 99%

A systematic approach for new technology development by using a biomimicry‐based TRIZ contradiction matrix

et al. 2018

View full text Add to dashboard Cite

The concept of biomimicry is to bring innovative ideas from nature to find new solutions for technology development or product design. However, there are few studies on proposing a method that connects nature's solutions to technology development in a systematic manner. Since previous studies assume that nature is fundamentally different from technologies, biological approaches should be separated from a technological TRIZ to solve technological problems. This research aims at proposing a systematic process to construct a biomimicry‐based TRIZ contradiction matrix that can be utilized to devise innovative ideas for new technology development by solving a contradictory problem based on biological solutions. To build a contradiction matrix that reflects both technological and biological views, inventive principles in biomimicry fields are extracted by text mining and matched in a cell of an existing TRIZ contradiction matrix by latent dirichlet allocation (LDA). In addition, a biomimicry‐based technology development process is suggested by using 40 inventive principles, 39 engineering parameters of the TRIZ contradiction matrix, and biomimicry cases. The proposed approach can be used as a tool to assist practitioners in solving technological problems and developing new technology by considering biological solutions as well as technological solutions.

show abstract

“…This model is seen as classical text mining or natural language processing tools that support information retrieval. LSI is used here to optimize the retrieval technique that indexes and uses SVD that identifies the pattern in an unstructured collection of text and find relationship between patterns [8]. Regex is used to define a search pattern based on the character strings.…”

Section: Review Of Related Literaturesmentioning

confidence: 99%

Analysis on the use of Latent Semantic Indexing (LSI) for document classification and retrieval system of PNP files

Aquino

Chavez

2018

MATEC Web Conf.

View full text Add to dashboard Cite

Document classification is the process of categorizing documents from many mixed files automatically [1]. In this paper, an approach to classification of documents for admin-case files of Philippine National Police (PNP) using Latent Semantic Indexing (LSI) method is proposed. The model for this that represents term-to-term, document-todocument and term-to-document relationships has been applied. Regular Expression is implemented also to define a search pattern based on character strings which the LSI used to establish the semantic relevance of the character strings to the search term or keyword. The aim of the study is to evaluate the performance of LSI in classifying PNP documents; experimentation was done using software to test the capability of LSI towards text retrieval. Indexing is according to the pattern matched in the collection of text that uses model of SVD. Based on tests, documents were indexed based on file relationships and was able to return a search result as the retrieved information from PNP files. Weights are used to check the accuracy of the method; the positive values identified in query similarity are regarded as the most relevant among the related searches, meaning, the query word matches words in a text file and it returns a query result.

show abstract

Effective use of latent semantic indexing and computational linguistics in biological and biomedical applications

Cited by 31 publications

References 44 publications

Fulfilling the Promise of "Biased" G Protein–Coupled Receptor Agonism

Fulfilling the Promise of "Biased" G Protein–Coupled Receptor Agonism

A systematic approach for new technology development by using a biomimicry‐based TRIZ contradiction matrix

Analysis on the use of Latent Semantic Indexing (LSI) for document classification and retrieval system of PNP files

Contact Info

Product

Resources

About