From data to diagnosis: how machine learning is revolutionizing biomarker discovery in idiopathic inflammatory myopathies

Abstract

Idiopathic inflammatory myopathies (IIMs) are a heterogeneous group of muscle disorders including adult and juvenile dermatomyositis, polymyositis, immune-mediated necrotising myopathy and sporadic inclusion body myositis, all of which present with variable symptoms and disease progression. The identification of effective biomarkers for IIMs has been challenging due to the heterogeneity between IIMs and within IIM subgroups, but recent advances in machine learning (ML) techniques have shown promises in identifying novel biomarkers. This paper reviews recent studies on potential biomarkers for IIM and evaluates their clinical utility. We also explore how data analytic tools and ML algorithms have been used to identify biomarkers, highlighting their potential to advance our understanding and diagnosis of IIM and improve patient outcomes. Overall, ML techniques have great potential to revolutionize biomarker discovery in IIMs and lead to more effective diagnosis and treatment.

Keywords: biomarkers; idiopathic inflammatory myopathies; machine learning; myositis-specific autoantibodies.

Figure 1

Interactive starburst plot showing machine learning categories, subsets, and algorithms. The interactive starburst plot displays the main categories of machine learning, including supervised and unsupervised learning, and their corresponding subsets and algorithms. Users can explore the plot to gain insights into the various machine learning techniques and their applications. This is an interactive plot: follow link: https://chart-studio.plotly.com/∼Emilymc/3.embed GAN: generative adversarial networks, RNN: recurrent neural networks, CNN: convolutional neural networks, AE: autoencoders, BIRCH: Balanced Iterative Reducing and Clustering using Hierarchies, OPICS: Ordering Points To Identify the Clustering Structure, SARSA: State-Action-Reward-State-Action.

Figure 2

The relationship between artificial intelligence, machine learning, deep learning and data science. The diagram highlights how these fields build on each other to provide advanced solutions for data-driven problems. Figure created with Biorender.com

Figure 3

Number of publications using ML algorithms in IIM research. Bar graph showing the number of publications investigating the use of ML in the field of IIM between 2014 and between January and October 2023^*. The data presented herein have been derived from PubMed and are reflective of publications available as of 4 October 2023. These publications were identified using specific search criteria, employing the terms ‘Inflammatory myopathies and Machine Learning’. Figure created with Biorender.com

Figure 4

Pipeline for the main steps in the FlowSOM analysis. (A) Data Preparation and quality control checks (i) The fcs-files are read, (ii) compensated, (iii) QC checked and (iv) concatenated. (B) FlowSOM model training and evaluation of model quality. (v) The model is trained and visualization is shown as a minimum-spanning tree, which is composed of multiple inter-connecting nodes. (vi) Each node comprises a start chart of different colours representing an immune marker. (vii) Example of a start chart with mean immune marker values. (C) Analysis of FlowSOM model using other visualization tools such as (viii) clustering analysis via t-SNE map, (ix) heatmaps or (x) differential analysis which can be used to infer biological conclusions about the data. Figure created with Biorender.com

Figure 5

Building blocks of typical CNN from an image. Convolutional layer: (A) set of filters are learned during training and applied to the input image to extract features at different spatial locations. Each filter convolves over the input image to produce a feature map. Pooling layer: The pooling layer is used to down-sample the output of the convolutional layer, reducing the spatial dimensions of the feature maps while retaining the important features. Fully connected layer: The fully connected layer is used to produce the final output of the network. It takes the flattened output from the previous layer and applies a set of weights to produce a vector of outputs.Figure created with Biorender.com