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Lee ES, Durant TJ. Supervised machine learning in the mass spectrometry laboratory: A tutorial. J Mass Spectrom Adv Clin Lab 2022; 23:1-6. [PMID: 34984411 PMCID: PMC8692990 DOI: 10.1016/j.jmsacl.2021.12.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 12/02/2021] [Accepted: 12/06/2021] [Indexed: 11/19/2022] Open
Abstract
As the demand for laboratory testing by mass spectrometry increases, so does the need for automated methods for data analysis. Clinical mass spectrometry (MS) data is particularly well-suited for machine learning (ML) methods, which deal nicely with structured and discrete data elements. The alignment of these two fields offers a promising synergy that can be used to optimize workflows, improve result quality, and enhance our understanding of high-dimensional datasets and their inherent relationship with disease. In recent years, there has been an increasing number of publications that examine the capabilities of ML-based software in the context of chromatography and MS. However, given the historically distant nature between the fields of clinical chemistry and computer science, there is an opportunity to improve technological literacy of ML-based software within the clinical laboratory scientist community. To this end, we present a basic overview of ML and a tutorial of an ML-based experiment using a previously published MS dataset. The purpose of this paper is to describe the fundamental principles of supervised ML, outline the steps that are classically involved in an ML-based experiment, and discuss the purpose of good ML practice in the context of a binary MS classification problem.
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Key Words
- Amino acid
- Artificial intelligence
- CART, Classification and Regression Trees
- ML, Machine Learning
- MS, Mass Spectrometry
- Mass spectrometry
- NLL, Negative Log Loss
- PAA, Plasma Amino Acid
- PR, Precision-Recall
- PRAUC, Area Under the Precision-Recall Curve
- RL, Reinforcement Learning
- ROC, Receiver Operator Curve
- SCF, Supplemental Code File
- Supervised machine learning
- XGBT, Extreme Gradient Boosted Trees
- Xgboost
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Affiliation(s)
- Edward S. Lee
- Department of Laboratory Medicine, at Yale School of Medicine, New Haven, CT, USA
- Department of Laboratory Medicine, at Yale New Haven Hospital, New Haven, CT, USA
| | - Thomas J.S. Durant
- Department of Laboratory Medicine, at Yale School of Medicine, New Haven, CT, USA
- Department of Laboratory Medicine, at Yale New Haven Hospital, New Haven, CT, USA
- Corresponding author at: Department of Laboratory Medicine, 55 Park Street PS345D, New Haven, CT 06511, USA.
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Yang X, Yang S, Li Q, Wuchty S, Zhang Z. Prediction of human-virus protein-protein interactions through a sequence embedding-based machine learning method. Comput Struct Biotechnol J 2019; 18:153-161. [PMID: 31969974 PMCID: PMC6961065 DOI: 10.1016/j.csbj.2019.12.005] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Revised: 11/29/2019] [Accepted: 12/10/2019] [Indexed: 12/11/2022] Open
Abstract
The identification of human-virus protein-protein interactions (PPIs) is an essential and challenging research topic, potentially providing a mechanistic understanding of viral infection. Given that the experimental determination of human-virus PPIs is time-consuming and labor-intensive, computational methods are playing an important role in providing testable hypotheses, complementing the determination of large-scale interactome between species. In this work, we applied an unsupervised sequence embedding technique (doc2vec) to represent protein sequences as rich feature vectors of low dimensionality. Training a Random Forest (RF) classifier through a training dataset that covers known PPIs between human and all viruses, we obtained excellent predictive accuracy outperforming various combinations of machine learning algorithms and commonly-used sequence encoding schemes. Rigorous comparison with three existing human-virus PPI prediction methods, our proposed computational framework further provided very competitive and promising performance, suggesting that the doc2vec encoding scheme effectively captures context information of protein sequences, pertaining to corresponding protein-protein interactions. Our approach is freely accessible through our web server as part of our host-pathogen PPI prediction platform (http://zzdlab.com/InterSPPI/). Taken together, we hope the current work not only contributes a useful predictor to accelerate the exploration of human-virus PPIs, but also provides some meaningful insights into human-virus relationships.
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Key Words
- AC, Auto Covariance
- ACC, Accuracy
- AUC, area under the ROC curve
- AUPRC, area under the PR curve
- Adaboost, Adaptive Boosting
- CT, Conjoint Triad
- Doc2vec
- Embedding
- Human-virus interaction
- LD, Local Descriptor
- MCC, Matthews correlation coefficient
- ML, machine learning
- MLP, Multiple Layer Perceptron
- MS, mass spectroscopy
- Machine learning
- PPIs, protein-protein interactions
- PR, Precision-Recall
- Prediction
- Protein-protein interaction
- RBF, radial basis function
- RF, Random Forest
- ROC, Receiver Operating Characteristic
- SGD, stochastic gradient descent
- SVM, Support Vector Machine
- Y2H, yeast two-hybrid
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Affiliation(s)
- Xiaodi Yang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Shiping Yang
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Qinmengge Li
- National Demonstration Center for Experimental Biological Sciences Education, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Stefan Wuchty
- Dept. of Computer Science, University of Miami, Miami, FL 33146, USA
- Dept. of Biology, University of Miami, Miami, FL 33146, USA
- Center of Computational Science, University of Miami, Miami, FL 33146, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, USA
| | - Ziding Zhang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
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