Breast cancer prediction with transcriptome profiling using feature selection and machine learning methods

• Published in: BMC bioinformatics Vol. 23; no. 1; pp. 1 - 9
• Main Authors: Taghizadeh, Eskandar, Heydarheydari, Sahel, Saberi, Alihossein, JafarpoorNesheli, Shabnam, Rezaeijo, Seyed Masoud
• Format: Journal Article
• Language: English
• Published: London BioMed Central 01.10.2022; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Accuracy; Algorithms; Analysis; Artificial intelligence; Bayesian analysis; Bioinformatics; Biological markers; Biomarkers; Biomedical and Life Sciences; Breast cancer; Business metrics; Care and treatment; Classification; Classifiers; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Datasets; Decision trees; Diagnosis; Discriminant analysis; Feature extraction; Feature selection; Gene expression; Health aspects; Hybrid systems; Learning algorithms; Life Sciences; Machine learning; Methods; Microarrays; MicroRNAs; Multilayer perceptrons; Prediction; Principal components analysis; Statistical analysis; Support vector machines; Transcriptome profiling; Transcriptomes; Variance analysis; Iran; Feature selection; Breast cancer; Transcriptome profiling; Prediction; Machine learning
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-022-04965-8

Background We used a hybrid machine learning systems (HMLS) strategy that includes the extensive search for the discovery of the most optimal HMLSs, including feature selection algorithms, a feature extraction algorithm, and classifiers for diagnosing breast cancer. Hence, this study aims to obtain a high-importance transcriptome profile linked with classification procedures that can facilitate the early detection of breast cancer. Methods In the present study, 762 breast cancer patients and 138 solid tissue normal subjects were included. Three groups of machine learning (ML) algorithms were employed: (i) four feature selection procedures are employed and compared to select the most valuable feature: (1) ANOVA; (2) Mutual Information; (3) Extra Trees Classifier; and (4) Logistic Regression (LGR), (ii) a feature extraction algorithm (Principal Component Analysis), iii) we utilized 13 classification algorithms accompanied with automated ML hyperparameter tuning, including (1) LGR; (2) Support Vector Machine; (3) Bagging; (4) Gaussian Naive Bayes; (5) Decision Tree; (6) Gradient Boosting Decision Tree; (7) K Nearest Neighborhood; (8) Bernoulli Naive Bayes; (9) Random Forest; (10) AdaBoost, (11) ExtraTrees; (12) Linear Discriminant Analysis; and (13) Multilayer Perceptron (MLP). For evaluating the proposed models' performance, balance accuracy and area under the curve (AUC) were used. Results Feature selection procedure LGR + MLP classifier achieved the highest prediction accuracy and AUC (balanced accuracy: 0.86, AUC = 0.94), followed by an LGR + LGR classifier (balanced accuracy: 0.84, AUC = 0.94). The results showed that achieved AUC for the LGR + LGR classifier belonged to the 20 biomarkers as follows: TMEM212, SNORD115-13, ATP1A4, FRG2, CFHR4, ZCCHC13, FLJ46361, LY6G6E, ZNF323, KRT28, KRT25, LPPR5, C10orf99, PRKACG, SULT2A1, GRIN2C, EN2, GBA2, CUX2, and SNORA66. Conclusions The best performance was achieved using the LGR feature selection procedure and MLP classifier. Results show that the 20 biomarkers had the highest score or ranking in breast cancer detection.