Improving Latent Dirichlet Allocation: On Reliability of the Novel Method LDAPrototype
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ABSTRACT:
SUBMITTER: Metais E
PROVIDER: S-EPMC7298183 | biostudies-literature | 2020 May
REPOSITORIES: biostudies-literature
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Project description:This work presents an alternative method to represent documents based on LDA (Latent Dirichlet Allocation) and how it affects to classification algorithms, in comparison to common text representation. LDA assumes that each document deals with a set of predefined topics, which are distributions over an entire vocabulary. Our main objective is to use the probability of a document belonging to each topic to implement a new text representation model. This proposed technique is deployed as an extension of the Weka software as a new filter. To demonstrate its performance, the created filter is tested with different classifiers such as a Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Naive Bayes in different documental corpora (OHSUMED, Reuters-21578, 20Newsgroup, Yahoo! Answers, YELP Polarity, and TREC Genomics 2015). Then, it is compared with the Bag of Words (BoW) representation technique. Results suggest that the application of our proposed filter achieves similar accuracy as BoW but greatly improves classification processing times.
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Project description:MotivationA cancer genome includes many mutations derived from various mutagens and mutational processes, leading to specific mutation patterns. It is known that each mutational process leads to characteristic mutations, and when a mutational process has preferences for mutations, this situation is called a 'mutation signature.' Identification of mutation signatures is an important task for elucidation of carcinogenic mechanisms. In previous studies, analyses with statistical approaches (e.g. non-negative matrix factorization and latent Dirichlet allocation) revealed a number of mutation signatures. Nonetheless, strictly speaking, these existing approaches employ an ad hoc method or incorrect approximation to estimate the number of mutation signatures, and the whole picture of mutation signatures is unclear.ResultsIn this study, we present a novel method for estimating the number of mutation signatures-latent Dirichlet allocation with variational Bayes inference (VB-LDA)-where variational lower bounds are utilized for finding a plausible number of mutation patterns. In addition, we performed cluster analyses for estimated mutation signatures to extract novel mutation signatures that appear in multiple primary lesions. In a simulation with artificial data, we confirmed that our method estimated the correct number of mutation signatures. Furthermore, applying our method in combination with clustering procedures for real mutation data revealed many interesting mutation signatures that have not been previously reported.Availability and implementationAll the predicted mutation signatures with clustering results are freely available at http://www.f.waseda.jp/mhamada/MS/index.html. All the C++ source code and python scripts utilized in this study can be downloaded on the Internet (https://github.com/qkirikigaku/MS_LDA).Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6853711 | biostudies-literature
Project description:We propose a novel multivariate method to analyse biodiversity data based on the Latent Dirichlet Allocation (LDA) model. LDA, a probabilistic model, reduces assemblages to sets of distinct component communities. It produces easily interpretable results, can represent abrupt and gradual changes in composition, accommodates missing data and allows for coherent estimates of uncertainty. We illustrate our method using tree data for the eastern United States and from a tropical successional chronosequence. The model is able to detect pervasive declines in the oak community in Minnesota and Indiana, potentially due to fire suppression, increased growing season precipitation and herbivory. The chronosequence analysis is able to delineate clear successional trends in species composition, while also revealing that site-specific factors significantly impact these successional trajectories. The proposed method provides a means to decompose and track the dynamics of species assemblages along temporal and spatial gradients, including effects of global change and forest disturbances.
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Project description:International trade is one of the classic areas of study in economics. Its empirical analysis is a complex problem, given the amount of products, countries and years. Nowadays, given the availability of data, the tools used for the analysis can be complemented and enriched with new methodologies and techniques that go beyond the traditional approach. This new possibility opens a research gap, as new, data-driven, ways of understanding international trade, can help our understanding of the underlying phenomena. The present paper shows the application of the Latent Dirichlet allocation model, a well known technique in the area of Natural Language Processing, to search for latent dimensions in the product space of international trade, and their distribution across countries over time. We apply this technique to a dataset of countries' exports of goods from 1962 to 2016. The results show that this technique can encode the main specialisation patterns of international trade. On the country-level analysis, the findings show the changes in the specialisation patterns of countries over time. As traditional international trade analysis demands expert knowledge on a multiplicity of indicators, the possibility of encoding multiple known phenomena under a unique indicator is a powerful complement for traditional tools, as it allows top-down data-driven studies.
| S-EPMC7861422 | biostudies-literature
Project description:Mutation signatures are defined as the distribution of specific mutations such as activity of AID/APOBEC family proteins. Previous studies have reported numerous signatures, using matrix factorization methods for mutation catalogs. Different mutation signatures are active in different tumor types; hence, signature activity varies greatly among tumor types and becomes sparse. Because of this, many previous methods require dividing mutation catalogs for each tumor type. Here, we propose parallelized latent Dirichlet allocation (PLDA), a novel Bayesian model to simultaneously predict mutation signatures with all mutation catalogs. PLDA is an extended model of latent Dirichlet allocation (LDA), which is one of the methods used for signature prediction. It has parallelized hyperparameters of Dirichlet distributions for LDA, and they represent the sparsity of signature activities for each tumor type, thus facilitating simultaneous analyses. First, we conducted a simulation experiment to compare PLDA with previous methods (including SigProfiler and SignatureAnalyzer) using artificial data and confirmed that PLDA could predict signature structures as accurately as previous methods without searching for the optimal hyperparameters. Next, we applied PLDA to PCAWG (Pan-Cancer Analysis of Whole Genomes) mutation catalogs and obtained a signature set different from the one predicted by SigProfiler. Further, we have shown that the mutation spectrum represented by the predicted signature with PLDA provides a novel interpretability through post-analyses.
| S-EPMC7600398 | biostudies-literature
Project description:The human microbiome consists of a community of microbes in varying abundances and is shown to be associated with many diseases. An important first step in many microbiome studies is to identify possible distinct microbial communities in a given data set and to identify the important bacterial taxa that characterize these communities. The data from typical microbiome studies are high dimensional count data with excessive zeros due to both absence of species (structural zeros) and low sequencing depth or dropout. Although methods have been developed for identifying the microbial communities based on mixture models of counts, these methods do not account for excessive zeros observed in the data and do not differentiate structural from sampling zeros. In this paper, we introduce a zero-inflated Latent Dirichlet Allocation model (zinLDA) for sparse count data observed in microbiome studies. zinLDA builds on the flexible Latent Dirichlet Allocation model and allows for zero inflation in observed counts. We develop an efficient Markov chain Monte Carlo (MCMC) sampling procedure to fit the model. Results from our simulations show zinLDA provides better fits to the data and is able to separate structural zeros from sampling zeros. We apply zinLDA to the data set from the American Gut Project and identify microbial communities characterized by different bacterial genera.
| S-EPMC7862749 | biostudies-literature
Project description:MicroRNAs (miRNAs) are small non-coding RNAs that cause mRNA degradation and translational inhibition. They are important regulators of development and cellular homeostasis through their control of diverse processes. Recently, great efforts have been made to elucidate their regulatory mechanism, but the functions of most miRNAs and their precise regulatory mechanisms remain elusive. With more and more matched expression profiles of miRNAs and mRNAs having been made available, it is of great interest to utilize both expression profiles to discover the functional regulatory networks of miRNAs and their target mRNAs for potential biological processes that they may participate in.We present a probabilistic graphical model to discover functional miRNA regulatory modules at potential biological levels by integrating heterogeneous datasets, including expression profiles of miRNAs and mRNAs, with or without the prior target binding information. We applied this model to a mouse mammary dataset. It effectively captured several biological process specific modules involving miRNAs and their target mRNAs. Furthermore, without using prior target binding information, the identified miRNAs and mRNAs in each module show a large proportion of overlap with predicted miRNA target relationships, suggesting that expression profiles are crucial for both target identification and discovery of regulatory modules.
| S-EPMC2995118 | biostudies-literature
Project description:Agricultural greenhouse plant images with complicated scenes are difficult to precisely manually label. The appearance of leaf disease spots and mosses increases the difficulty in plant segmentation. Considering these problems, this paper proposed a statistical image segmentation algorithm MSBS-LDA (Mean-shift Bandwidths Searching Latent Dirichlet Allocation), which can perform unsupervised segmentation of greenhouse plants. The main idea of the algorithm is to take advantage of the language model LDA (Latent Dirichlet Allocation) to deal with image segmentation based on the design of spatial documents. The maximum points of probability density function in image space are mapped as documents and Mean-shift is utilized to fulfill the word-document assignment. The proportion of the first major word in word frequency statistics determines the coordinate space bandwidth, and the spatial LDA segmentation procedure iteratively searches for optimal color space bandwidth in the light of the LUV distances between classes. In view of the fruits in plant segmentation result and the ever-changing illumination condition in greenhouses, an improved leaf segmentation method based on watershed is proposed to further segment the leaves. Experiment results show that the proposed methods can segment greenhouse plants and leaves in an unsupervised way and obtain a high segmentation accuracy together with an effective extraction of the fruit part.
| S-EPMC6026534 | biostudies-literature
Project description:The recent outbreak of novel Coronavirus disease or COVID-19 is declared a pandemic by the World Health Organization (WHO). The availability of social media platforms has played a vital role in providing and obtaining information about any ongoing event. However, consuming a vast amount of online textual data to predict an event's trends can be troublesome. To our knowledge, no study analyzes the online news articles and the disease data about coronavirus disease. Therefore, we propose an LDA-based topic model, called PAN-LDA (Pandemic-Latent Dirichlet allocation), that incorporates the COVID-19 cases data and news articles into common LDA to obtain a new set of features. The generated features are introduced as additional features to Machine learning(ML) algorithms to improve the forecasting of time series data. Furthermore, we are employing collapsed Gibbs sampling (CGS) as the underlying technique for parameter inference. The results from experiments suggest that the obtained features from PAN-LDA generate more identifiable topics and empirically add value to the outcome.
| S-EPMC8505021 | biostudies-literature
Project description:BACKGROUND:Artificial intelligence (AI)-based technologies develop rapidly and have myriad applications in medicine and health care. However, there is a lack of comprehensive reporting on the productivity, workflow, topics, and research landscape of AI in this field. OBJECTIVE:This study aimed to evaluate the global development of scientific publications and constructed interdisciplinary research topics on the theory and practice of AI in medicine from 1977 to 2018. METHODS:We obtained bibliographic data and abstract contents of publications published between 1977 and 2018 from the Web of Science database. A total of 27,451 eligible articles were analyzed. Research topics were classified by latent Dirichlet allocation, and principal component analysis was used to identify the construct of the research landscape. RESULTS:The applications of AI have mainly impacted clinical settings (enhanced prognosis and diagnosis, robot-assisted surgery, and rehabilitation), data science and precision medicine (collecting individual data for precision medicine), and policy making (raising ethical and legal issues, especially regarding privacy and confidentiality of data). However, AI applications have not been commonly used in resource-poor settings due to the limit in infrastructure and human resources. CONCLUSIONS:The application of AI in medicine has grown rapidly and focuses on three leading platforms: clinical practices, clinical material, and policies. AI might be one of the methods to narrow down the inequality in health care and medicine between developing and developed countries. Technology transfer and support from developed countries are essential measures for the advancement of AI application in health care in developing countries.
| S-EPMC6858616 | biostudies-literature