Ten quick tips for machine learning in computational biology

By Davide Chicco. 2017

777 citations recorded by the source corpus.

Machine learning has become a pivotal tool for many projects in computational biology, bioinformatics, and health informatics. Nevertheless, beginners and biomedical researchers often do not have enough experience to run a data mining project effectively, and therefore can follow incorrect practices, that may lead to common mistakes or over-optimistic results. With this review, we present ten quick tips to take advantage of machine learning in any computational biology context, by avoiding some common errors that we observed hundreds of times in multiple bioinformatics projects. We believe our ten suggestions can strongly help any machine learning practitioner to carry on a successful project in computational biology and related sciences.

Subfields: Molecular Biology

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Citations in the corpus

PotentialNet for Molecular Property Prediction (2018, background): “Such sensitivity underscores the criticality of rigorous cross-validation 35,36 .”
Cyberbullying Detection With Fairness Constraints (2020, methodology): “As accuracy, area under the receiver operating characteristic curve, and F1 score may lead to misleading conclusions on imbalanced datasets (such as here), we evaluate our models with Matthews correlation coefficient (MCC) which does not exhibit such drawback [60], [61], [62], [63].”
Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone (2020, methodology): “To choose the top hyper-parameters, we used a grid search and selected the models that generated the highest Matthews correlation coefficient [83].”
Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images (2018, background): “Literature studies reveal that PeerJ reviewing PDF | (2018:01:23017:2:0:NEW 5 Mar 2018) 351 MCC is an informative single score to evaluate the performance of a binary classifier in a confusion 352 matrix context (Chicco, 2017).”
The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification (2023, methodology): “We randomly split each dataset into training set (80% patients’ profiles, randomly selected) and test set (20% remaining patients’ profiles), that we used as hold-out validation set [62].”
The Matthews Correlation Coefficient (MCC) is More Informative Than Cohen’s Kappa and Brier Score in Binary Classification Assessment (2021, background): “With the growing spread of the Matthews correlation coef-ﬁcient [15], [16], specialized blogs about machine learning and technology started to discuss this rate, too.”
Sensor data quality: a systematic review (2020, methodology): “Based on the example discussed by Chicco [106], assume that a classifier is trained on a heavily imbalanced dataset with 95 normal data and 5 anomalous data.”
The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation (2021, methodology): “We took advantage of the validation set for the hyper-parameter optimization grid search [16]: number k of neighbors for k-NN, and cost C value for the SVM.”
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation (2020, methodology): “…the entries of M , MCC reads as follows: (worst value: –1; best value: +1) MCC is the only binary classification rate that generates a high score only if the binary predictor was able to correctly predict the majority of positive data instances and the majority of negative data instances [80, 97].”