From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment

Swanson, Kyle; Wu, Eric Hsiao-Kuang; Zhang, Angela; Alizadeh, Ash A.; Zou, James

doi:10.1016/j.cell.2023.01.035

Cited by 135 publications

(85 citation statements)

References 124 publications

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“…In addition, ML is also widely used in evolutionary analysis, 14 genomic data analysis 15,16 and other bioinformatics research. When combined with medicine, ML has demonstrated exceptional capabilities in biomedical image analysis, 17,18 biomedical engineering, 19 medical diagnostics, [20][21][22] drug development, 23,24 healthcare, 25 etc. When working with big and multimodal data, ML often offers superior performance over other tools.…”

Section: Introductionmentioning

confidence: 99%

Machine learning facilitating the rational design of nanozymes

Zhang

Yan

2023

J. Mater. Chem. B

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Section: Introductionmentioning

confidence: 99%

Machine learning facilitating the rational design of nanozymes

Zhang

Yan

2023

J. Mater. Chem. B

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“…In recent years, there has been growing interest in applying machine learning and bioinformatics to research. [13–15] By integrating multilayered biological data, including genomes, transcriptomics, proteomics, and metabolomics, these techniques may enable a more comprehensive and systematic understanding of the molecular mechanisms and pathophysiology underlying HCC. The weighted gene co-expression network analysis (WGCNA) approach can analyze large-scale gene expression profile data, identify genes associated with liver cancer, and extract potential biomarkers and therapeutic targets from them.…”

Section: Introductionmentioning

confidence: 99%

WGCNA combined with machine learning to find potential biomarkers of liver cancer

Lv,

Hou,

Zhang

et al. 2023

Medicine

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The incidence of hepatocellular carcinoma (HCC) has been increasing in recent years. With the development of various detection technologies, machine learning is an effective method to screen disease characteristic genes. In this study, weighted gene co-expression network analysis (WGCNA) and machine learning are combined to find potential biomarkers of liver cancer, which provides a new idea for future prediction, prevention, and personalized treatment. In this study, the “limma” software package was used. P < .05 and log2 |fold-change| > 1 is the standard screening differential genes, and then the module genes obtained by WGCNA analysis are crossed to obtain the key module genes. Gene Ontology and Kyoto Gene and Genome Encyclopedia analysis was performed on key module genes, and 3 machine learning methods including lasso, support vector machine-recursive feature elimination, and RandomForest were used to screen feature genes. Finally, the validation set was used to verify the feature genes, the GeneMANIA (http://www.genemania.org) database was used to perform protein–protein interaction networks analysis on the feature genes, and the SPIED3 database was used to find potential small molecule drugs. In this study, 187 genes associated with HCC were screened by using the “limma” software package and WGCNA. After that, 6 feature genes (AADAT, APOF, GPC3, LPA, MASP1, and NAT2) were selected by RandomForest, Absolute Shrinkage and Selection Operator, and support vector machine-recursive feature elimination machine learning algorithms. These genes are also significantly different on the external dataset and follow the same trend as the training set. Finally, our findings may provide new insights into targets for diagnosis, prevention, and treatment of HCC. AADAT, APOF, GPC3, LPA, MASP1, and NAT2 may be potential genes for the prediction, prevention, and treatment of liver cancer in the future.

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“…37−39 Machine learning algorithms examine large-scale data sets, detecting subtle patterns and abnormalities that help to make precise diagnoses, while reducing human error. 40 Here we presented a novel system called EV-MPDS, which was capable of detecting EV membrane protein without isolation based on the FRET through EV membrane-and specific protein-labeled probes (Figure 1), eliminating the need for time-consuming EV extraction steps. This system was designed for early diagnosis and staging of lung cancer.…”

mentioning

confidence: 99%

“…Additionally, compared to single-target diagnosis, multitarget diagnosis enhances sensitivity and specificity. It focuses on multiple disease-related markers, enabling the detection of a wider range of disease-related alterations and minimizing the risk of false negatives or false positives. , However, successful implementation of multitarget diagnosis necessitates careful marker selection, validation, and integration using appropriate analytical methods and algorithms, in conjunction with machine learning techniques. − Machine learning algorithms examine large-scale data sets, detecting subtle patterns and abnormalities that help to make precise diagnoses, while reducing human error …”

mentioning

confidence: 99%

Accurate and Convenient Lung Cancer Diagnosis through Detection of Extracellular Vesicle Membrane Proteins via Förster Resonance Energy Transfer

Xiao,

Yao,

Liao

et al. 2023

Nano Lett.

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Tumor-derived extracellular vesicles (EVs) are promising to monitor early stage cancer. Unfortunately, isolating and analyzing EVs from a patient's liquid biopsy are challenging. For this, we devised an EV membrane proteins detection system (EV-MPDS) based on Forster resonance energy transfer (FRET) signals between aptamer quantum dots and AIEgen dye, which eliminated the EV extraction and purification to conveniently diagnose lung cancer. In a cohort of 80 clinical samples, this system showed enhanced accuracy (100% versus 65%) and sensitivity (100% versus 55%) in cancer diagnosis as compared to the ELISA detection method. Improved accuracy of early screening (from 96.4% to 100%) was achieved by comprehensively profiling five biomarkers using a machine learning analysis system. FRET-based tumor EV-MPDS is thus an isolation-free, low-volume (1 μL), and highly accurate approach, providing the potential to aid lung cancer diagnosis and early screening.

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From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment

Cited by 135 publications

References 124 publications

Machine learning facilitating the rational design of nanozymes

Machine learning facilitating the rational design of nanozymes

WGCNA combined with machine learning to find potential biomarkers of liver cancer

Accurate and Convenient Lung Cancer Diagnosis through Detection of Extracellular Vesicle Membrane Proteins via Förster Resonance Energy Transfer

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