Unsupervised Structure Detection in Biomedical Data

Abstract

A major challenge in computational biology is to find simple representations of high-dimensional data that best reveal the underlying structure. In this work, we present an intuitive and easy-to-implement method based on ranked neighborhood comparisons that detects structure in unsupervised data. The method is based on ordering objects in terms of similarity and on the mutual overlap of nearest neighbors. This basic framework was originally introduced in the field of social network analysis to detect actor communities. We demonstrate that the same ideas can successfully be applied to biomedical data sets in order to reveal complex underlying structure. The algorithm is very efficient and works on distance data directly without requiring a vectorial embedding of data. Comprehensive experiments demonstrate the validity of this approach. Comparisons with state-of-the-art clustering methods show that the presented method outperforms hierarchical methods as well as density based clustering methods and model-based clustering. A further advantage of the method is that it simultaneously provides a visualization of the data. Especially in biomedical applications, the visualization of data can be used as a first pre-processing step when analyzing real world data sets to get an intuition of the underlying data structure. We apply this model to synthetic data as well as to various biomedical data sets which demonstrate the high quality and usefulness of the inferred structure.

Pharmacometrics and Machine Learning Partner to Advance Clinical Data Analysis

“…Machine learning (ML), which lies at the intersection of computer science and statistics, provides such computationally powerful tools for the analysis of large and heterogeneous data sets and has become increasingly popular in different domains in the last decade, including medicine . These methods are capable of analyzing large datasets almost in real time.…”

“…11 Machine learning (ML), which lies at the intersection of computer science and statistics, provides such computationally powerful tools for the analysis of large and heterogeneous data sets and has become increasingly popular in different domains in the last decade, including medicine. [12][13][14][15][16][17] These methods are capable of analyzing large datasets almost in real time. This provides the opportunity to leverage ML in clinical pharmacology, and the combination of ML with PMX might lead to great scientific achievements.…”

6'ya 6 Tahta Üzeri̇nde at Kaplama Problemi̇ni̇ Çözmek İçi̇n Deneti̇msi̇z Maki̇ne Öğrenme Algori̇tmasi

“…Unsupervised machine learning algorithms have a wide range of applications such as image processing [43][44][45], digital signal processing [46,47], biomedical research [48], segmentation [49][50][51][52]. Likewise, in this study, we have applied the unsupervised learning algorithm, namely modularity.…”

Probabilistic clustering of time-evolving distance data

“…As a first step, we partition a total of 195,297 sentences from 3,403 electronic health records (EHR) from 704 MSKCC brain cancer patients into groups of similar vocabulary. This is done by treating sentences as binary vectors with non-zero entries corresponding to vocabulary, and obtaining a similarity measure using ranked neighborhood comparisons [31]. Sentences are clustered using this similarity measure with the Louvain method [8].…”