Benchmark and application of unsupervised classification approaches for univariate data

Unsupervised Segmentation-Based Machine Learning as an Advanced Analysis Tool for Single Molecule Break Junction Data

(2020) Nathan D. Bamberger et al.   Journal of Physical Chemistry C

A reference-free clustering method for the analysis of molecular break-junction measurements

(2019) Damien Cabosart et al.   APPLIED PHYSICS LETTERS

Applications of machine learning in drug discovery and development

(2019) Jessica Vamathevan et al.   NATURE REVIEWS DRUG DISCOVERY

Unsupervised word embeddings capture latent knowledge from materials science literature

(2019) Vahe Tshitoyan et al.   NATURE

Robust graphene-based molecular devices

(2019) Maria El Abbassi et al.   Nature Nanotechnology

Efficiently measuring a quantum device using machine learning

(2019) D. T. Lennon et al.   npj Quantum Information

Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

(2019) K. T. Schütt et al.   Nature Communications

UMAP reveals cryptic population structure and phenotype heterogeneity in large genomic cohorts

(2019) Alex Diaz-Papkovich et al.   PLoS Genetics

Machine Learning in Nanoscience: Big Data at Small Scales

(2019) Keith A. Brown et al.   NANO LETTERS

Quantum Transport through a Single Conjugated Rigid Molecule, a Mechanical Break Junction Study

(2018) Riccardo Frisenda et al.   ACCOUNTS OF CHEMICAL RESEARCH

Classification of conductance traces with recurrent neural networks

(2018) Kasper P. Lauritzen et al.   JOURNAL OF CHEMICAL PHYSICS

Fast Data Sorting with Modified Principal Component Analysis to Distinguish Unique Single Molecular Break Junction Trajectories

(2018) J. M. Hamill et al.   PHYSICAL REVIEW LETTERS

Dimensionality reduction for visualizing single-cell data using UMAP

(2018) Etienne Becht et al.   NATURE BIOTECHNOLOGY

Uncovering hierarchical data structure in single molecule transport

(2017) Ben H. Wu et al.   JOURNAL OF CHEMICAL PHYSICS

AiiDA: automated interactive infrastructure and database for computational science

(2016) Giovanni Pizzi et al.   COMPUTATIONAL MATERIALS SCIENCE

Understanding the interaction between energetic ions and freestanding graphene towards practical 2D perforation

(2016) Jakob Buchheim et al.   Nanoscale

Unsupervised vector-based classification of single-molecule charge transport data

(2016) Mario Lemmer et al.   Nature Communications

Deep learning in neural networks: An overview

(2015) Jürgen Schmidhuber   NEURAL NETWORKS

Machine learning applications in cancer prognosis and prediction

(2015) Konstantina Kourou et al.   Computational and Structural Biotechnology Journal

NbClust: AnRPackage for Determining the Relevant Number of Clusters in a Data Set

(2015) Malika Charrad et al.   Journal of Statistical Software

Large negative differential conductance in single-molecule break junctions

(2014) Mickael L. Perrin et al.   Nature Nanotechnology

Big Data Deep Learning: Challenges and Perspectives

(2014) Xue-Wen Chen et al.   IEEE Access

The high-throughput highway to computational materials design

(2013) Stefano Curtarolo et al.   NATURE MATERIALS

Agglomerative clustering via maximum incremental path integral

(2013) Wei Zhang et al.   PATTERN RECOGNITION

Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups

(2012) Geoffrey Hinton et al.   IEEE SIGNAL PROCESSING MAGAZINE

An extensive comparative study of cluster validity indices

(2012) Olatz Arbelaitz et al.   PATTERN RECOGNITION

A survey of hierarchical classification across different application domains

(2010) Carlos N. Silla et al.   DATA MINING AND KNOWLEDGE DISCOVERY