4.7 Review

Machine learning and deep learning methods that use omics data for metastasis prediction

Related references

Note: Only part of the references are listed.
Article Biochemistry & Molecular Biology

MetaCancer: A deep learning-based pan-cancer metastasis prediction model developed using multi-omics data

Somayah Albaradei et al.

Summary: Researchers presented a deep learning model, MetaCancer, which integrates RNA sequencing, microRNA sequencing, and DNA methylation data to improve cancer metastasis prediction performance. The integration of multiple data as features outperformed using only mRNA data, and mRNA-related features made a greater contribution in distinguishing primary tumors from metastatic tumors.

COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL (2021)

Review Medicine, Research & Experimental

Machine learning in clinical decision making

Lorenz Adlung et al.

Summary: Machine learning is being increasingly integrated into clinical practice, but critical considerations such as validation, evaluation, avoiding over-dependence, and responsible sharing of code and data need to be addressed. Challenges also include benchmarking, dissemination of knowledge, and transparent assessment of pipelines for better integration into the medical realm.
Letter Biotechnology & Applied Microbiology

Assessing the impact of generative AI on medicinal chemistry

W. Patrick Walters et al.

NATURE BIOTECHNOLOGY (2020)

Article Biotechnology & Applied Microbiology

Deep learning in omics: a survey and guideline

Zhiqiang Zhang et al.

BRIEFINGS IN FUNCTIONAL GENOMICS (2019)

Review Genetics & Heredity

Circulating MicroRNAs in Cancer: Potential and Challenge

Mengying Cui et al.

FRONTIERS IN GENETICS (2019)

Article Biochemistry & Molecular Biology

A cross-cancer metastasis signature in the microRNA-mRNA axis of paired tissue samples

Samuel C. Lee et al.

MOLECULAR BIOLOGY REPORTS (2019)

Article Oncology

Are 90% of deaths from cancer caused by metastases?

Hanna Dillekas et al.

CANCER MEDICINE (2019)

Article Medicine, General & Internal

Key challenges for delivering clinical impact with artificial intelligence

Christopher J. Kelly et al.

BMC MEDICINE (2019)

Article Multidisciplinary Sciences

Prediction and Analysis of Skin Cancer Progression using Genomics Profiles of Patients

Sherry Bhalla et al.

SCIENTIFIC REPORTS (2019)

Article Genetics & Heredity

A primer on deep learning in genomics

James Zou et al.

NATURE GENETICS (2019)

Article Public, Environmental & Occupational Health

Prediction of survival and metastasis in breast cancer patients using machine learning classifiers

Leili Tapak et al.

CLINICAL EPIDEMIOLOGY AND GLOBAL HEALTH (2019)

Review Biochemistry & Molecular Biology

Defining Driver DNA Methylation Changes in Human Cancer

Gerd P. Pfeifer

INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES (2018)

Article Biotechnology & Applied Microbiology

Deep learning in biomedicine

Michael Wainberg et al.

NATURE BIOTECHNOLOGY (2018)

Review Oncology

MicroRNAs and metastasis: small RNAs play big roles

Jongchan Kim et al.

CANCER AND METASTASIS REVIEWS (2018)

Article Oncology

Using machine learning to parse breast pathology reports

Adam Yala et al.

BREAST CANCER RESEARCH AND TREATMENT (2017)

Review Oncology

Epithelial-mesenchymal transition in tumor metastasis

Kay T. Yeung et al.

MOLECULAR ONCOLOGY (2017)

Article Medicine, Research & Experimental

A support vector machine classifier for the prediction of osteosarcoma metastasis with high accuracy

Yunfei He et al.

INTERNATIONAL JOURNAL OF MOLECULAR MEDICINE (2017)

Article Medicine, General & Internal

Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer

Babak Ehteshami Bejnordi et al.

JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2017)

Article Biotechnology & Applied Microbiology

Sparse feature selection for classification and prediction of metastasis in endometrial cancer

Mehmet Eren Ahsen et al.

BMC GENOMICS (2017)

Article Biochemical Research Methods

Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model

Lujia Chen et al.

BMC BIOINFORMATICS (2016)

Article Computer Science, Interdisciplinary Applications

Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation

Holger R. Roth et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2016)

Article Health Care Sciences & Services

Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View

Wei Luo et al.

JOURNAL OF MEDICAL INTERNET RESEARCH (2016)

Article Multidisciplinary Sciences

Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis

Geert Litjens et al.

SCIENTIFIC REPORTS (2016)

Review Oncology

Targeting metastasis

Patricia S. Steeg

NATURE REVIEWS CANCER (2016)

Review Oncology

The challenge of targeting metastasis

Isaiah J. Fidler et al.

CANCER AND METASTASIS REVIEWS (2015)

Review Cell Biology

The role of EMT and MET in cancer dissemination

Jacqueline Banyard et al.

CONNECTIVE TISSUE RESEARCH (2015)

Article Gastroenterology & Hepatology

Epigenetic Alterations in Colorectal Cancer: Emerging Biomarkers

Yoshinaga Okugawa et al.

GASTROENTEROLOGY (2015)

Review Multidisciplinary Sciences

Deep learning

Yann LeCun et al.

NATURE (2015)

Editorial Material Biotechnology & Applied Microbiology

Deep learning for regulatory genomics

Yongjin Park et al.

NATURE BIOTECHNOLOGY (2015)

Article Multidisciplinary Sciences

Modeling the Transitions between Collective and Solitary Migration Phenotypes in Cancer Metastasis

Bin Huang et al.

SCIENTIFIC REPORTS (2015)

Review Pharmacology & Pharmacy

Cancer metastases: challenges and opportunities

Xiangming Guan

ACTA PHARMACEUTICA SINICA B (2015)

Review Biochemistry & Molecular Biology

Cancer Invasion: Patterns and Mechanisms

N. V. Krakhmal et al.

ACTA NATURAE (2015)

Review Biochemistry & Molecular Biology

Invading one step at a time: the role of invadopodia in tumor metastasis

H. Paz et al.

ONCOGENE (2014)

Review Pharmacology & Pharmacy

Targeting tumor cell motility as a strategy against invasion and metastasis

Alan Wells et al.

TRENDS IN PHARMACOLOGICAL SCIENCES (2013)

Review Oncology

MicroRNA and cancer

Martin D. Jansson et al.

MOLECULAR ONCOLOGY (2012)

Review Biochemistry & Molecular Biology

Tumor Metastasis: Molecular Insights and Evolving Paradigms

Scott Valastyan et al.

Review Oncology

'Omic approaches to preventing or managing metastatic breast cancer

Obi L. Griffith et al.

BREAST CANCER RESEARCH (2011)

Review Biochemistry & Molecular Biology

Targeting anoikis resistance in prostate cancer metastasis

Shinichi Sakamoto et al.

MOLECULAR ASPECTS OF MEDICINE (2010)

Article Computer Science, Artificial Intelligence

An experimental comparison of performance measures for classification

C. Ferri et al.

PATTERN RECOGNITION LETTERS (2009)

Article Biotechnology & Applied Microbiology

BioGPS: an extensible and customizable portal for querying and organizing gene annotation resources

Chunlei Wu et al.

GENOME BIOLOGY (2009)

Review Medicine, General & Internal

Metastasis: recent discoveries and novel treatment strategies

Suzanne A. Eccles et al.

LANCET (2007)

Article Multidisciplinary Sciences

Reducing the dimensionality of data with neural networks

G. E. Hinton et al.

SCIENCE (2006)

Article Computer Science, Artificial Intelligence

A fast learning algorithm for deep belief nets

Geoffrey E. Hinton et al.

NEURAL COMPUTATION (2006)

Article Computer Science, Artificial Intelligence

One-shot learning of object categories

FF Li et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2006)

Article Biochemistry & Molecular Biology

A case study on choosing normalization methods and test statistics for two-channel microarray data

Y Xie et al.

COMPARATIVE AND FUNCTIONAL GENOMICS (2004)

Article Mathematical & Computational Biology

Exploration, normalization, and summaries of high density oligonucleotide array probe level data

RA Irizarry et al.

BIOSTATISTICS (2003)