Using Machine Learning for Real-Time Analysis of High-Throughput Biological Data

Abstract

ML and high-throughput biological data have revolutionised bioinformatics, helping us comprehend complex biological processes. Strong computers can analyse and interpret massive amounts of RNA sequencing (RNA-Seq) and mass spectrometry data, which is rising quickly. Real-time machine learning retrieves biological data from massive databases. Statistics cannot handle noisy, high-dimensional, non-linear biological data like ML. 

Most transcriptome profiling methods use RNA-Seq, which produces millions of sequences per sample. High-dimensional data pattern discovery, organisation, and forecasting need complex processing. Supervised and unsupervised machine learning can detect gene regulatory networks, track gene expression, and predict cell responses. Mistakes and biases in RNA-Seq sequencing hinder gene expression analysis. Random forests, SVMs, and deep learning architectures have created strong gene expression profile representations to solve these difficulties. More accuracy and reliability. 

For proteomics research, mass spectrometry delivers complex, dynamic data. Deep and reinforcement learning are common mass spectrometry data analysis methods. It studies protein, peptide, and post-translational modifications. ML finds protein isoforms, biomarkers, and interactions. ML feature extraction accelerates processing. 

ML on high-throughput biological data remains tough despite progress. Tag-free training data hinders supervised learning. On little datasets, ML algorithms may overfit or fail to generalise. Researchers improve models using semi-supervised, unsupervised, and transfer learning without large annotated datasets. Strong ML models are challenging to understand since biological data is connected. XAI represents biological knowledge ML model selection. 

Real-time biological data analysis requires fast computers. Personalised medicine and large-scale epidemiology need real-time high-throughput data processing. Real-time bioinformatics requires improved ML models for streaming data, parallel computation, and cloud computing. HPC and cloud systems evaluate huge biological data live. 

AI may find biomarkers, disease subtypes, and therapy targets. These findings may impact medication and tailored medicine development. The ML may find biological correlations statistics overlook. ML algorithms can learn biology and disease from transcriptomics, proteomics, and genetics. Precision medicine predicts and cures complex diseases utilising multi-omics data. 

But machine learning and high-throughput biological data processing have restrictions. Batch effects, variable data, and experimental platforms reduce reproducibility. Test ML after standardising data pretreatment and normalisation. Machine learning in biomedical research raises ethical and legal concerns. Patient data must be safe in healthcare. 

This work analyses high-throughput biological data using machine learning. The issues and solutions of ML algorithms for RNA-Seq and mass spectrometry data processing are presented. Bioinformatics ML. Tool shows complex biological processes. Finally, it discusses how multi-omics data, deep learning, explainable AI, and real-time data analysis will transform bioinformatics machine learning.