Software Suite for Gene and Protein Annotation Prediction and Similarity Search

Chicco, Davide; Masseroli, Marco

doi:10.1109/tcbb.2014.2382127

Cited by 29 publications

(21 citation statements)

References 24 publications

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“…For numerical datasets, in addition, the normalization (or scaling ) by feature (by column) into the [0;1] interval is often necessary to put the whole dataset into a common frame, before the machine learning algorithm process it. Latent semantic indexing (LSI), for example, is an information retrieval method which necessitates this pre-processing to be employed for prediction of gene functional annotations [ 13 ]. Data normalization into the [ min ; max ] interval, or into an interval having a particular mean (for example, 0.0) and a particular standard deviation (for example, 1.0) are also popular strategies [ 14 ].…”

Section: Tip 1: Check and Arrange Your Input Dataset Properlymentioning

confidence: 99%

Ten quick tips for machine learning in computational biology

2017

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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.

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Section: Tip 1: Check and Arrange Your Input Dataset Properlymentioning

confidence: 99%

Ten quick tips for machine learning in computational biology

2017

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“…In the future, we plan to employing alternative approaches for data missing imputation, such as oversampling through k-nearest neighbors [40] or latent semantic indexing similarity [8]. We also plan to try alternative prediction models, like probabilistic latent semantic analysis [36].…”

Section: Resultsmentioning

confidence: 99%

Peer Review #2 of "Supervised deep learning embeddings for the prediction of cervical cancer diagnosis (v0.1)"

2018

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Cervical cancer remains a significant cause of mortality all around the world, even if it can be prevented and cured by removing affected tissues in early stages. Providing universal and efficient access to cervical screening programs is a challenge that requires identifying vulnerable individuals in the population, among other steps. In this work, we present a computationally automated strategy for predicting the outcome of the patient biopsy, given risk patterns from individual medical records. We propose a machine learning technique that allows a joint and fully supervised optimization of dimensionality reduction and classification models. We also build a model able to highlight relevant properties in the low dimensional space, to ease the classification of patients. We instantiated the proposed approach with deep learning architectures, and achieved accurate prediction results (top area under the curve AUC = 0.6875) which outperform previously developed methods, such as denoising autoencoders. Additionally, we explored some clinical findings from the embedding spaces, and we validated them through the medical literature, making them reliable for physicians and biomedical researchers. Cervical cancer remains a significant cause of mortality all around the world, even though it can be prevented and cured by surgical resections in early stages. Providing universal and efficient access to cervical screening programs is a challenge that requires identifying vulnerable individuals in the population, among other steps. In this work, we present a computationally automated strategy for predicting the outcome of the patient biopsy given risk patterns from individual medical records. We propose a machine learning technique that allows a joint and fully supervised optimization of dimensionality reduction and classification models. We also build a model able to highlight relevant properties in the low dimensional space, to ease the classification of patients. We instantiated the proposed approach with deep learning architectures, and achieved accurate prediction results (top area under the curve AUC = 0.6875) which outperform previously developed methods, such as denoising autoencoders. Additionally, we explored some clinical findings from the embedding spaces, and we validated them through the medical literature, making them reliable for physicians and biomedical researchers.

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“…Feature work will also include the enhancement of the presented machinery by applying alternative techniques to handle the data class-imbalance [37, 67, 68], the application of our algorithm combination to other disease health record datasets (for example, [41]), the application of alternative machine learning algorithms (for example, latent Dirichlet allocation [76] or probabilistic latent semantic analysis [77]) for the diagnosis prediction, and the possible usage of semantic similarity measures to incorporate similarity information between features (for example, through latent semantic indexing [78]). We also plan to explore the feature dependence in the dataset, to see what feature influence which other features and how.…”

Section: Discussionmentioning

confidence: 99%

Computational prediction of diagnosis and feature selection on mesothelioma patient health records

Chicco¹,

Rovelli

2019

PLoS ONE

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BackgroundMesothelioma is a lung cancer that kills thousands of people worldwide annually, especially those with exposure to asbestos. Diagnosis of mesothelioma in patients often requires time-consuming imaging techniques and biopsies. Machine learning can provide for a more effective, cheaper, and faster patient diagnosis and feature selection from clinical data in patient records.Methods and findingsWe analyzed a dataset of health records of 324 patients having mesothelioma symptoms from Turkey. The patients had prior asbestos exposure and displayed symptoms consistent with mesothelioma. We compared probabilistic neural network, perceptron-based neural network, random forest, one rule, and decision tree classifiers to predict diagnosis of the patient records. We measured classifiers’ performance through standard confusion matrix scores such as Matthews correlation coefficient (MCC). Random forest outperformed all models tried, obtaining MCC = +0.37 on the complete imbalanced dataset and MCC = +0.64 on the under-sampled balanced dataset. We then employed random forest feature selection to identify the two most relevant dataset traits associated with mesothelioma: lung side and platelet count. These two risk factors resulted so predictive, that decision tree focusing on them achieved the second top accuracy on the complete dataset diagnosis prediction (MCC = +0.28), outperforming all other methods and even decision tree itself applied to all features.ConclusionsOur results show that machine learning can predict diagnoses of patients having mesothelioma symptoms with high accuracy, sensitivity, and specificity, in few minutes. Additionally, random forest can efficiently select the most important features of this clinical dataset (lung side and platelet count) in few seconds. The importance of pleural plaques in lung sides and blood platelets in mesothelioma diagnosis indicates that physicians should focus on these two features when reading records of patients with mesothelioma symptoms. Moreover, doctors can exploit our machinery to predict patient diagnosis when only lung side and platelet data are available.

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Software Suite for Gene and Protein Annotation Prediction and Similarity Search

Cited by 29 publications

References 24 publications

Ten quick tips for machine learning in computational biology

Ten quick tips for machine learning in computational biology

Peer Review #2 of "Supervised deep learning embeddings for the prediction of cervical cancer diagnosis (v0.1)"

Computational prediction of diagnosis and feature selection on mesothelioma patient health records

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