ArticleBMC bioinformatics2019
Analyzing gene expression data for pediatric and adult cancer diagnosis using logic learning machine and standard supervised methods.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Assessing psychological resilience and its influencing factors in the MSM population by machine learning.Scientific reports · 2025Article
- AutoTFCNNY: A multi-instance neural network for enhanced early cancer detection using TCR data.PloS one · 2025Article
- Uncovering Predictors of Lipid Goal Attainment in Type 2 Diabetes Outpatients Using Logic Learning Machine: Insights from the AMD Annals and AMD Artificial Intelligence Study Group.Journal of clinical medicine · 2023Article
- Transparent machine learning suggests a key driver in the decision to start insulin therapy in individuals with type 2 diabetes.Journal of diabetes · 2023Article
- Multiple instance neural networks based on sparse attention for cancer detection using T-cell receptor sequences.BMC bioinformatics · 2022Article
- LLM-PBC: Logic Learning Machine-Based Explainable Rules Accurately Stratify the Genetic Risk of Primary Biliary Cholangitis.Journal of personalized medicine · 2022Article
- Artificial intelligence for precision medicine in autoimmune liver disease.Frontiers in immunology · 2022Review
- Identifying Subgroups of Patients With Autism by Gene Expression Profiles Using Machine Learning Algorithms.Frontiers in psychiatry · 2021Article
- Determinants of good metabolic control without weight gain in type 2 diabetes management: a machine learning analysis.BMJ open diabetes research & care · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLogic Learning Machine (LLM) is an innovative method of supervised analysis capable of constructing models based on simple and intelligible rules. In this investigation the performance of LLM in classifying patients with cancer was evaluated using a set of eight publicly available gene expression databases for cancer diagnosis. LLM accuracy was assessed by summary ROC curve (sROC) analysis and estimated by the area under an sROC curve (sAUC). Its performance was compared in cross validation with that of standard supervised methods, namely: decision tree, artificial neural network, support vector machine (SVM) and k-nearest neighbor classifier.
resultsLLM showed an excellent accuracy (sAUC = 0.99, 95%CI: 0.98-1.0) and outperformed any other method except SVM.
conclusionsLLM is a new powerful tool for the analysis of gene expression data for cancer diagnosis. Simple rules generated by LLM could contribute to a better understanding of cancer biology, potentially addressing therapeutic approaches.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.