22 papers · ranked by Valyu relevance
Lin Liu, Lin Tang, Wen Dong, Shaowen Yao + 1 more
Background With the rapid accumulation of biological datasets, machine learning methods designed to automate data analysis are urgently needed. In recent years, so-called topic models that originated from the field of natural language processing have been receiving much attention in bioinformatics because of their…
Etana Fikadu Dinsa, Mrinal Das, Teklu Urgessa Abebe
Afaan Oromo is a resource-scarce language with limited tools developed for its processing, posing significant challenges for natural language tasks. The tools designed for English do not work efficiently for Afaan Oromo due to the linguistic differences and lack of well-structured resources. To address this challenge…
Ben Curran, Kyle Higham, Elisenda Ortiz, Demival Vasques Filho + 1 more
'Floriana Gargiulo'] Quantitative methods to describe the participation to debate of Members of Parliament and the parties they belong to are lacking. Here we propose a new approach that combines topic modeling with complex networks techniques, and use it to characterize the political discourse at the New Zealand…
Eric Austin, Shraddha Makwana, Amine Trabelsi, Christine Largeron + 1 more
Topic modeling aims to discover latent themes in collections of text documents. It has various applications across fields such as sociology, opinion analysis, and media studies. In such areas, it is essential to have easily interpretable, diverse, and coherent topics. An efficient topic modeling technique should…
Hamza H.M. Altarturi, Muntadher Saadoon, Nor Badrul Anuar, Daniel de Oliveira
An immense volume of digital documents exists online and offline with content that can offer useful information and insights. Utilizing topic modeling enhances the analysis and understanding of digital documents. Topic modeling discovers latent semantic structures or topics within a set of digital textual documents.…
Katherine Redfield Chang, Xinghua Lou, Theofanis Karaletsos, Christopher Crosbie + 3 more
Using a variety of techniques including Topic Modeling, Principal Component Analysis and Bi-clustering, we explore electronic patient records in the form of unstructured clinical notes and genetic mutation test results. Our ultimate goal is to gain insight into a unique body of clinical data, specifically regarding the…
Ahmed El-Kishky, Yanglei Song, Chi Wang, Clare R. Voss + 1 more
While most topic modeling algorithms model text corpora with unigrams, human interpretation often relies on inherent grouping of terms into phrases. As such, we consider the problem of discovering topical phrases of mixed lengths. Existing work either performs post processing to the results of unigram-based topic…
Sergei Koltcov, Anton Surkov, Vladimir Filippov, Vera Ignatenko + 1 more
'Davide Chicco'] Topic modeling is a widely used instrument for the analysis of large text collections. In the last few years, neural topic models and models with word embeddings have been proposed to increase the quality of topic solutions. However, these models were not extensively tested in terms of stability and…
Rebecca Danning, Zheng Tracy Ke, Rong Ma, Xihong Lin
Count data are ubiquitous across many applications in which understanding hidden patterns, or latent structure, is of interest. Topic modeling is a powerful tool for detecting latent structure in count data. However, standard topic modeling methods are often constrained by their restrictive assumptions, susceptible to…
Uttam Chauhan, Shrusti Shah, Dharati Shiroya, Dipti Solanki + 9 more
'Zeel Patel' 'Jitendra Bhatia' 'Sudeep Tanwar' 'Ravi Sharma' 'Verdes Marina' 'Maria Simona Raboaca' 'Chang Choi' 'Kiho Lim' 'Gyuho Choi'] Topic modeling is a machine learning algorithm based on statistics that follows unsupervised machine learning techniques for mapping a high-dimensional corpus to a low-dimensional…
Ryan Wesslen
—Topic models are a family of statistical-based algorithms to summarize, explore and index large collections of text documents. After a decade of research led by computer scientists, topic models have spread to social science as a new generation of data-driven social scientists have searched for tools to explore large…
Chris Gropp, Alexander Herzog, Ilya Safro, Paul W. Wilson + 1 more
Topic modeling, a method for extracting the underlying themes from a collection of documents, is an increasingly important component of the design of intelligent systems enabling the sense-making of highly dynamic and diverse streams of text data. Traditional methods such as Dynamic Topic Modeling (DTM) do not lend…
Saranzaya Magsarjav, Melissa Humphries, Jonathan Tuke, Lewis Mitchell
Topic modelling in Natural Language Processing uncovers hidden topics in large, unlabelled text datasets. It is widely applied in fields such as information retrieval, content summarisation, and trend analysis across various disciplines. However, probabilistic topic models can produce different results when rerun due…
Wesam Elshamy
Topic models are probabilistic models for discovering topical themes in collections of documents. In real world applications, these models provide us with the means of organizing what would otherwise be unstructured collections. They can help us cluster a huge collection into different topics or find a subset of the…
Filippo Valle, Michele Caselle, Matteo Osella
The availability of high-dimensional transcriptomic datasets is increasing at a tremendous pace, together with the need for suitable computational tools. Clustering and dimensionality reduction methods are popular go-to methods to identify basic structures in these datasets. At the same time, different topic modeling…
Preethi K. Periyakoil, Melanie H. Smith, Meghana Kshirsagar, Daniel Ramirez + 5 more
Single-cell RNA sequencing studies have revealed the heterogeneity of cell states present in the rheumatoid arthritis (RA) synovium. However, it remains unclear how these cell types interact with one another in situ and how synovial microenvironments shape observed cell states. Here, we use spatial transcriptomics (ST)…
Filippo Valle, Matteo Osella, Michele Caselle
The integration of transcriptional data with other layers of information, such as the post-transcriptional regulation mediated by microRNAs, can be crucial to identify the driver genes and the subtypes of complex and heterogeneous diseases such as cancer. This paper presents an approach based on topic modeling to…
Marzieh Khodaei, Scott V. Edwards, Peter Beerli
Methods for rapidly inferring the evolutionary history of species or populations with genome-wide data are progressing, but computational constraints still limit our abilities in this area. We developed an alignment-free method to infer genome-wide phylogenies and implemented it in the Python package TopicContml. The…
Authors not listed
Protein-ligand interaction prediction with proteochemometric (PCM) models can provide valuable insights during early drug discovery and chemical safety assessment. These models have benefitted from the large amount of data available in bioactivity databases. However, an issue that is often overlooked when using this…
Sanjar Adilov
Generative neural networks have shown promising results in de novo drug design. Recent studies suggest that one of the efficient ways to produce novel molecules matching target properties is to model SMILES sequences using deep learning in a way similar to language modeling in natural language processing. In this…
Julian Ivanov, Alan Lipkus, Haitao Chen, Chris Aultman + 3 more
A novel bibliometric methodology based on natural language data processing for identifying emerging topics in science is presented. Along with the usual practice of data collection and preprocessing, our method includes a natural language processing (NLP) technique and an innovative mathematical function data…
Pulan Yu
Associative classification mining (ACM) integrating association rule mining and classification has become a significant tool for knowledge discovery, especially in the chemical domain. Its major advantage is providing high accuracy as well as chemically interpretable models. Additionally, it is able to find…