4.6 Article

An Introduction to Machine Learning Approaches for Biomedical Research

Related references

Note: Only part of the references are listed.
Article Medical Informatics

Anomaly Detection Algorithm for Real-World Data and Evidence in Clinical Research: Implementation, Evaluation, and Validation Study

Vendula Churova et al.

Summary: In modern clinical research, ensuring high data quality is crucial. The study presented a machine learning algorithm for detecting anomalous patterns in data, which is universal in nature and capable of detecting anomalous data with a sensitivity exceeding 85%.

JMIR MEDICAL INFORMATICS (2021)

Article Ecology

The evolutionary origin of visual and somatosensory representation in the vertebrate pallium

Shreyas M. Suryanarayana et al.

NATURE ECOLOGY & EVOLUTION (2020)

Review Public, Environmental & Occupational Health

Generative Adversarial Networks and Its Applications in Biomedical Informatics

Lan Lan et al.

FRONTIERS IN PUBLIC HEALTH (2020)

Article Computer Science, Hardware & Architecture

Generative Adversarial Networks

Ian Goodfellow et al.

COMMUNICATIONS OF THE ACM (2020)

Article Computer Science, Artificial Intelligence

InceptionTime: Finding AlexNet for time series classification

Hassan Ismail Fawaz et al.

DATA MINING AND KNOWLEDGE DISCOVERY (2020)

Review Otorhinolaryngology

Unsupervised Learning Techniques for the Investigation of Chronic Rhinosinusitis

Abigail Walker et al.

ANNALS OF OTOLOGY RHINOLOGY AND LARYNGOLOGY (2019)

Article Oncology

Predictive analytics with gradient boosting in clinical medicine

Zhongheng Zhang et al.

ANNALS OF TRANSLATIONAL MEDICINE (2019)

Article Multidisciplinary Sciences

The art of using t-SNE for single-cell transcriptomics

Dmitry Kobak et al.

NATURE COMMUNICATIONS (2019)

Proceedings Paper Engineering, Biomedical

Using Generative Adversarial Networks and Transfer Learning for Breast Cancer Detection by Convolutional Neural Networks

Shuyue Guan et al.

MEDICAL IMAGING 2019: IMAGING INFORMATICS FOR HEALTHCARE, RESEARCH, AND APPLICATIONS (2019)

Article Biochemical Research Methods

Deep Reinforcement Learning and Simulation as a Path Toward Precision Medicine

Brenden K. Petersen et al.

JOURNAL OF COMPUTATIONAL BIOLOGY (2019)

Article Computer Science, Hardware & Architecture

An improved improved Id3 algorithm for medical data classification

Shuo Yang et al.

COMPUTERS & ELECTRICAL ENGINEERING (2018)

Article Computer Science, Artificial Intelligence

Applications of Deep Learning and Reinforcement Learning to Biological Data

Mufti Mahmud et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2018)

Article Computer Science, Artificial Intelligence

Convolution in Convolution for Network in Network

Yanwei Pang et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2018)

Review Computer Science, Artificial Intelligence

A review of clustering techniques and developments

Amit Saxena et al.

NEUROCOMPUTING (2017)

Review Multidisciplinary Sciences

Deep learning

Yann LeCun et al.

NATURE (2015)

Review Multidisciplinary Sciences

Machine learning: Trends, perspectives, and prospects

M. I. Jordan et al.

SCIENCE (2015)

Proceedings Paper Optics

Automatic detection of invasive ductal carcinoma in whole slide images with Convolutional Neural Networks

Angel Cruz-Roa et al.

MEDICAL IMAGING 2014: DIGITAL PATHOLOGY (2014)

Article Computer Science, Artificial Intelligence

Decision trees: a recent overview

S. B. Kotsiantis

ARTIFICIAL INTELLIGENCE REVIEW (2013)

Article Computer Science, Artificial Intelligence

Gradient boosting machines, a tutorial

Alexey Natekin et al.

FRONTIERS IN NEUROROBOTICS (2013)

Article Computer Science, Theory & Methods

Anomaly detection

Varun Chandola et al.

ACM COMPUTING SURVEYS (2009)

Article Biotechnology & Applied Microbiology

What are decision trees?

Carl Kingsford et al.

NATURE BIOTECHNOLOGY (2008)

Article Computer Science, Artificial Intelligence

Conditional anomaly detection

Xiuyao Song et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2007)

Proceedings Paper Computer Science, Artificial Intelligence

ac Clustering categorical data using silhouette coefficient as a relocating measure

S. Aranganayagi et al.

ICCIMA 2007: INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND MULTIMEDIA APPLICATIONS, VOL II, PROCEEDINGS (2007)

Proceedings Paper Computer Science, Artificial Intelligence

Clustering Categorical Data using Silhouette Coefficient as a Relocating Measure

S. Aranganayagi et al.

ICCIMA 2007: INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND MULTIMEDIA APPLICATIONS, VOL III, PROCEEDINGS (2007)