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Topic Modeling: A Comprehensive Review
2018
EAI Endorsed Transactions on Scalable Information Systems
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It includes classification hierarchy, Topic modelling methods, Posterior Inference techniques, different evolution models of latent Dirichlet allocation (LDA) and its applications in different areas of ...
Topic modelling is the new revolution in text mining. It is a statistical technique for revealing the underlying semantic structure in large collection of documents. ...
Drugs impact different pathways within the cell and the active pathway may be different within cell types. ...
doi:10.4108/eai.13-7-2018.159623
fatcat:lu6al57vp5aahbytyejhqrlzry
Asymmetric author-topic model for knowledge discovering of big data in toxicogenomics
2015
Frontiers in Pharmacology
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Finally, we illustrated the ability of our model to identify the evidence of potential reduction of animal use. ...
The analogy between text corpus and large-scale genomic data enables the application of text mining tools, like probabilistic topic models, to explore hidden patterns of genomic data and to the extension ...
Acknowledgments MC is grateful to the National Center for Toxicological Research (NCTR) of U. S. Food and Drug Administration (FDA) for internship opportunity through Oak Ridge Institute for Science ...
doi:10.3389/fphar.2015.00081
pmid:25941488
pmcid:PMC4403303
fatcat:eiij26abovfj3cquu35iust5ou
Systematic identification of latent disease-gene associations from PubMed articles
2018
PLoS ONE
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We take advantage of both Latent Dirichlet Allocation (LDA) modeling and network-based analysis for their capabilities of detecting latent associations and reducing noises for large volume data respectively ...
Literature mining is one of the commonly used methods to retrieve and extract information from scientific publications for understanding these associations. ...
Latent Dirichlet Allocation (LDA) is a generative computational model aiming to explain sets of observations by unobserved variable groups [6] . ...
doi:10.1371/journal.pone.0191568
pmid:29373609
pmcid:PMC5786305
fatcat:pqqptau7s5ctnp6iid3roe4q2e
A latent variable model for chemogenomic profiling
2005
Bioinformatics
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We show that this model is useful for summarizing the relationship among treatments and genes affected by those treatments in a compendium of microarray profiles. ...
The model also incorporates the functional annotation of known genes to guide the clustering procedure. Results: We applied our model to the clustering of 79 chemogenomic experiments in yeast. ...
A.P.A. and P.F. would also like to acknowledge support from the National Cancer Institute of the National Institutes of Health and the Howard Hughes Medical Institute for support during the period of this ...
doi:10.1093/bioinformatics/bti515
pmid:15919724
fatcat:6afdf7a7w5bahiziz2ko7zyegu
A Survey on Journey of Topic Modeling Techniques from SVD to Deep Learning
2017
International Journal of Modern Education and Computer Science
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Here we present a survey on journey of topic modeling techniques comprising Latent Dirichlet Allocation (LDA) and non-LDA based techniques and the reason for classify the techniques into LDA and non-LDA ...
Purpose of this survey is to explore the topic modeling techniques since Singular Value Decomposition (SVD) topic model to the latest topic models in deep learning. ...
The properties of a hierarchical Dirichlet bigram language model [15] were used to explore the hierarchical generative probabilistic model for latent topic variables and n-gram statistics. ...
doi:10.5815/ijmecs.2017.07.06
fatcat:nadnmsoj4zdi7onlxivrne6gqm
Finding Complex Biological Relationships in Recent PubMed Articles Using Bio-LDA
2011
PLoS ONE
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provides a useful resource for the discovery of recent information concerning genes, diseases, compounds and the interactions between them. ...
In this paper, we describe an algorithm called Bio-LDA that uses extracted biological terminology to automatically identify latent topics, and provides a variety of measures to uncover putative relations ...
Other advanced extensions of LDA model include supervised Latent Dirichlet Allocation (sLDA) [16] and dynamic topic model [12] . ...
doi:10.1371/journal.pone.0017243
pmid:21448266
pmcid:PMC3063155
fatcat:mxtdsdbvszeotmqdmmll2azfuu
What's Hot and What's Not? - Exploring Trends in Bioinformatics Literature Using Topic Modeling and Keyword Analysis
[chapter]
2017
Lecture Notes in Computer Science
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On the other hand, interest in drug discovery has plateaued after the early 2000s. ...
Scientists exploring a new area of research are interested to know the "hot" topics in that area in order to make informed choices. ...
While there are several topic modeling algorithms [6, 10, 11] , Latent Dirichlet Allocation (LDA) [6] is one of the most widely used approaches and has been shown to be effective at finding distinct ...
doi:10.1007/978-3-319-59575-7_25
fatcat:j4k5bw3yovfrpivs66klmplfje
Beyond Topics: Discovering Latent Healthcare Objectives from Event Sequences
[article]
2021
arXiv
pre-print
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Topic models, such as Latent Dirichlet Allocation (LDA), are used to identify latent patterns in EHR data. ...
The sequential nature of EHRs is captured by CaSE's event-level representations, revealing latent healthcare objectives. ...
A significant method for topic modelling is Latent Dirichlet Allocation (LDA) [2] , and is part of a larger family of Bayesian approaches to clustering grouped data [24] . ...
arXiv:2110.01160v1
fatcat:jrrqknfwgfbcdk7ncqgl3kvn7y
Sparse discriminative latent characteristics for predicting cancer drug sensitivity from genomic features
2019
PLoS Computational Biology
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We use ideas from Bayesian nonparametrics to automatically infer the appropriate number of these latent characteristics. ...
Drug screening studies typically involve assaying the sensitivity of a range of cancer cell lines across an array of anti-cancer therapeutics. ...
choose λ for the regression approaches and the model complexity for LOBICO (using the same 8 complexity settings as used in the LOBICO paper). ...
doi:10.1371/journal.pcbi.1006743
pmid:31136571
pmcid:PMC6555538
fatcat:kaxgf2wufzgkdpvzxmvktsf4na
On mining latent treatment patterns from electronic medical records
2014
Data mining and knowledge discovery
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In this paper, we are concerned with the problem of utilizing the heterogeneous EMRs to assist CP analysis and improvement. ...
Discovered treatment patterns, as actionable knowledge representing the best practice for most patients in most time of their treatment processes, form the backbone of CPs, and can be exploited to help ...
The authors would like to give special thanks to all experts who cooperated in the evaluation of the proposed method. ...
doi:10.1007/s10618-014-0381-y
fatcat:e4rrs4363nazdnlgelpbvv6ns4
Application of dynamic topic models to toxicogenomics data
2016
BMC Bioinformatics
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The biological meaning underlying each topic was interpreted using diverse sources of information such as functional analysis of the pathways and therapeutic uses of the drugs. ...
The intrinsic complexity of time series data requires appropriate computational algorithms for data interpretation. ...
Food and Drug Administration, or the United States government. Mention of trade names or commercial products does not constitute endorsement or recommendation for use. ...
doi:10.1186/s12859-016-1225-0
pmid:27766956
pmcid:PMC5073961
fatcat:dprdgddi5bb6hgxxx2pepxpfjq
Of text and gene – using text mining methods to uncover hidden knowledge in toxicogenomics
2014
BMC Systems Biology
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patterns was observed across all three assay systems, indicating a possibility of using in vitro systems with careful designs (such as the choice of dose and time point), to replace the in vivo testing ...
Although the results indicated a challenge to extrapolate the in vitro results to the in vivo situation, we did notice that, for some drugs but not for all the drugs, the similarity in gene expression ...
Disclaimer The views presented in this article do not necessarily reflect current or future opinion or policy of the US Food and Drug Administration. ...
doi:10.1186/s12918-014-0093-3
pmid:25115450
pmcid:PMC4236689
fatcat:lbupmk56tfamfke5by4sx7vsji
A Data-driven Latent Semantic Analysis for Automatic Text Summarization using LDA Topic Modelling
[article]
2022
arXiv
pre-print
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This study presents the Latent Dirichlet Allocation (LDA) approach used to perform topic modelling from summarised medical science journal articles with topics related to genes and diseases. ...
The efficacy of the LDA and the extractive summarization methods were measured using Latent Semantic Analysis (LSA) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics to evaluate the ...
Latent Dirichlet Allocation Latent Dirichlet Allocation (LDA) is a technique applied in topic modelling introduced by [15] . ...
arXiv:2207.14687v3
fatcat:zejd3bijenbblkos5ebvwwyywa
Publication Landscape Analysis on Gliomas: How Much Has Been Done in the Past 25 Years?
2020
Frontiers in Oncology
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Latent Dirichlet allocation (LDA) was applied to the abstracts to identify publications' research topics with greater specificity. ...
The purpose of this study was to analyze the landscape of glioma-related research over the past 25 years using machine learning and text analysis. ...
We used latent Dirichlet allocation (LDA) to identify the research topics in each article with greater specificity. ...
doi:10.3389/fonc.2019.01463
pmid:32038995
pmcid:PMC6988829
fatcat:xf4habmnunavrjfbkchutuax6i
Discovering Medication Patterns for High-Complexity Drug-Using Diseases through Electronic Medical Records
2019
IEEE Access
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2019) Discovering medication patterns for high-complexity drug-using diseases through electronic medical records. ...
Similarly, an improved Latent Dirichlet allocation (LDA) model [34] that is also a probabilistic model was applied to discover the changing trends of medical behaviors over time from EMRs. ...
the dosage of drug, which is less important to the clinical pathway and medication scheme. ...
doi:10.1109/access.2019.2937892
fatcat:ce4i7g6rdzhyxffbh36yssyqk4
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