Invention of 3Mint for feature grouping and scoring in multi-omics

Yazici, Miray Unlu; Marron, J. S.; Bakır-Güngör, Burcu; Zou, Fei; Yousef, Malik

doi:10.3389/fgene.2023.1093326

Cited by 13 publications

(3 citation statements)

References 82 publications

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“…These are some of those tools: maTE ( Yousef et al, 2019 ) which uses microRNA target gene information for grouping the genes; miRcorrNet ( Yousef et al, 2021a ) and miRModuleNet ( Yousef et al, 2022 ), which detect feature sets via concurrently analyzing mRNA and miRNA expression datasets; CogNet ( Yousef et al, 2021b ) and PriPat ( Yousef et al, 2023 ) that use KEGG pathway information for grouping the genes; GediNet ( Qumsiyeh et al, 2022 ) that uses disease gene associations, miRdisNET ( Jabeer et al, 2023 ) that uses miRNA target gene information while assigning the genes into sets, GeNetOntology ( Ersoz et al, 2023 ) uses the Ontology information for grouping the genes. 3Mint ( Unlu Yazici et al, 2023 ) is a recent tool that integrates 3-omics datasets in order to detect groups and apply the G-S-M model.…”

Section: Methodsmentioning

confidence: 99%

TextNetTopics Pro, a topic model-based text classification for short text by integration of semantic and document-topic distribution information

Voskergian,

Bakir-Gungor,

Yousef

2023

Front. Genet.

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With the exponential growth in the daily publication of scientific articles, automatic classification and categorization can assist in assigning articles to a predefined category. Article titles are concise descriptions of the articles’ content with valuable information that can be useful in document classification and categorization. However, shortness, data sparseness, limited word occurrences, and the inadequate contextual information of scientific document titles hinder the direct application of conventional text mining and machine learning algorithms on these short texts, making their classification a challenging task. This study firstly explores the performance of our earlier study, TextNetTopics on the short text. Secondly, here we propose an advanced version called TextNetTopics Pro, which is a novel short-text classification framework that utilizes a promising combination of lexical features organized in topics of words and topic distribution extracted by a topic model to alleviate the data-sparseness problem when classifying short texts. We evaluate our proposed approach using nine state-of-the-art short-text topic models on two publicly available datasets of scientific article titles as short-text documents. The first dataset is related to the Biomedical field, and the other one is related to Computer Science publications. Additionally, we comparatively evaluate the predictive performance of the models generated with and without using the abstracts. Finally, we demonstrate the robustness and effectiveness of the proposed approach in handling the imbalanced data, particularly in the classification of Drug-Induced Liver Injury articles as part of the CAMDA challenge. Taking advantage of the semantic information detected by topic models proved to be a reliable way to improve the overall performance of ML classifiers.

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Section: Methodsmentioning

confidence: 99%

TextNetTopics Pro, a topic model-based text classification for short text by integration of semantic and document-topic distribution information

Voskergian,

Bakir-Gungor,

Yousef

2023

Front. Genet.

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“…The G-S-M approach has been used by other bioinformatics tools. An example of such tools are: miRModuleNet ( Yousef, Goy & Bakir-Gungor, 2022 ), which detects groups via calculating the correlations between the mRNA and miRNA expression profiles; Integrating of Gene Ontology ( Yousef, Sayıcı& Bakir-Gungor, 2021 ) that uses Gene Ontology information for grouping; PriPath ( Yousef et al, 2023 ) that uses KEGG pathways for grouping; GediNet ( Qumsiyeh, Showe & Yousef, 2022 ) that uses disease gene associations as groups; 3Mint ( Unlu Yazici et al, 2023 ) that employs mRNA expression, miRNA expression and methylation profiles for grouping; and miRdisNET ( Jabeer et al, 2023 ) that uses miRNA target gene information while creating the groups.…”

Section: Grouping Features With Biological Domain Knowledgementioning

confidence: 99%

Review of feature selection approaches based on grouping of features

Kuzudisli,

Bakir-Gungor,

Bulut

et al. 2023

PeerJ

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With the rapid development in technology, large amounts of high-dimensional data have been generated. This high dimensionality including redundancy and irrelevancy poses a great challenge in data analysis and decision making. Feature selection (FS) is an effective way to reduce dimensionality by eliminating redundant and irrelevant data. Most traditional FS approaches score and rank each feature individually; and then perform FS either by eliminating lower ranked features or by retaining highly-ranked features. In this review, we discuss an emerging approach to FS that is based on initially grouping features, then scoring groups of features rather than scoring individual features. Despite the presence of reviews on clustering and FS algorithms, to the best of our knowledge, this is the first review focusing on FS techniques based on grouping. The typical idea behind FS through grouping is to generate groups of similar features with dissimilarity between groups, then select representative features from each cluster. Approaches under supervised, unsupervised, semi supervised and integrative frameworks are explored. The comparison of experimental results indicates the effectiveness of sequential, optimization-based (i.e., fuzzy or evolutionary), hybrid and multi-method approaches. When it comes to biological data, the involvement of external biological sources can improve analysis results. We hope this work’s findings can guide effective design of new FS approaches using feature grouping.

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“…The G-S-M approach has been used in other studies that consider the pre-existing biological knowledge (Yousef et al, 2019(Yousef et al, , 2021a(Yousef et al, ,c, 2022aQumsiyeh et al, 2022;Yousef and Voskergian, 2022;Ersoz et al, 2023;Jabeer et al, 2023). Additionally it was modified to integrate two-omics datasets such as the miRcorrNet and miRModuleNet tools (Yousef et al, 2021a(Yousef et al, , 2022b; and even to integrate 3 omics datasets such as 3Mint tool (Unlu Yazici et al, 2023). Interested readers can find further details about those approaches in our recent reviews (Yousef et al, 2021b;Kuzudisli et al, 2023).…”

Section: Microbiomegsmmentioning

confidence: 99%

microBiomeGSM: the identification of taxonomic biomarkers from metagenomic data using grouping, scoring and modeling (G-S-M) approach

Bakir-Gungor,

Temiz,

Jabeer

et al. 2023

Front. Microbiol.

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Numerous biological environments have been characterized with the advent of metagenomic sequencing using next generation sequencing which lays out the relative abundance values of microbial taxa. Modeling the human microbiome using machine learning models has the potential to identify microbial biomarkers and aid in the diagnosis of a variety of diseases such as inflammatory bowel disease, diabetes, colorectal cancer, and many others. The goal of this study is to develop an effective classification model for the analysis of metagenomic datasets associated with different diseases. In this way, we aim to identify taxonomic biomarkers associated with these diseases and facilitate disease diagnosis. The microBiomeGSM tool presented in this work incorporates the pre-existing taxonomy information into a machine learning approach and challenges to solve the classification problem in metagenomics disease-associated datasets. Based on the G-S-M (Grouping-Scoring-Modeling) approach, species level information is used as features and classified by relating their taxonomic features at different levels, including genus, family, and order. Using four different disease associated metagenomics datasets, the performance of microBiomeGSM is comparatively evaluated with other feature selection methods such as Fast Correlation Based Filter (FCBF), Select K Best (SKB), Extreme Gradient Boosting (XGB), Conditional Mutual Information Maximization (CMIM), Maximum Likelihood and Minimum Redundancy (MRMR) and Information Gain (IG), also with other classifiers such as AdaBoost, Decision Tree, LogitBoost and Random Forest. microBiomeGSM achieved the highest results with an Area under the curve (AUC) value of 0.98% at the order taxonomic level for IBDMD dataset. Another significant output of microBiomeGSM is the list of taxonomic groups that are identified as important for the disease under study and the names of the species within these groups. The association between the detected species and the disease under investigation is confirmed by previous studies in the literature. The microBiomeGSM tool and other supplementary files are publicly available at: https://github.com/malikyousef/microBiomeGSM.

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Invention of 3Mint for feature grouping and scoring in multi-omics

Cited by 13 publications

References 82 publications

TextNetTopics Pro, a topic model-based text classification for short text by integration of semantic and document-topic distribution information

TextNetTopics Pro, a topic model-based text classification for short text by integration of semantic and document-topic distribution information

Review of feature selection approaches based on grouping of features

microBiomeGSM: the identification of taxonomic biomarkers from metagenomic data using grouping, scoring and modeling (G-S-M) approach

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