Publications

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Handbook of Genetic Algorithms

This book sets out to explain what genetic algorithms are and how they can be used to solve real-world problems. The first objective is tackled by the editor, Lawrence Davis. Th...

Molecular signatures database (MSigDB) 3.0

Abstract Motivation: Well-annotated gene sets representing the universe of the biological processes are critical for meaningful and insightful interpretation of large-scale geno...

From local explanations to global understanding with explainable AI for trees

Pfam: The protein families database in 2021

Abstract The Pfam database is a widely used resource for classifying protein sequences into families and domains. Since Pfam was last described in this journal, over 350 new fam...

Inference and analysis of cell-cell communication using CellChat

ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks

Recently, channel attention mechanism has demonstrated to offer great potential in improving the performance of deep convolutional neural networks (CNNs). However, most existing...

Atomic Decomposition by Basis Pursuit

The time-frequency and time-scale communities have recently developed a large number of overcomplete waveform dictionaries --- stationary wavelets, wavelet packets, cosine packe...

Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors)

Boosting is one of the most important recent developments in\nclassification methodology. Boosting works by sequentially applying a\nclassification algorithm to reweighted versi...

Use of partial least squares (PLS) in strategic management research: a review of four recent studies

Advances in causal modeling techniques have made it possible for researchers to simultaneously examine theory and measures. However, researchers must use these new techniques ap...

UniProt: the universal protein knowledgebase in 2021

Abstract The aim of the UniProt Knowledgebase is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional inf...

Segment Anything

We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation. Using our efficient model in a data collection loop, we built the largest ...