Multiple-instance learning of somatic mutations for the classification of tumour type and the prediction of microsatellite status

Mutation-Attention (MuAt): deep representation learning of somatic mutations for tumour typing and subtyping

(2023) Prima Sanjaya et al.   Genome Medicine

Genomics enters the deep learning era

(2022) Etienne Routhier et al.   PeerJ

Data-efficient and weakly supervised computational pathology on whole-slide images

(2021) Ming Y. Lu et al.   Nature Biomedical Engineering

A multi-resolution model for histopathology image classification and localization with multiple instance learning

(2021) Jiayun Li et al.   COMPUTERS IN BIOLOGY AND MEDICINE

AI-based pathology predicts origins for cancers of unknown primary

(2021) Ming Y. Lu et al.   NATURE

Biologically informed deep neural network for prostate cancer discovery

(2021) Haitham A. Elmarakeby et al.   NATURE

Cancer Type Classification in Liquid Biopsies Based on Sparse Mutational Profiles Enabled through Data Augmentation and Integration

(2021) Alexandra Danyi et al.   Life-Basel

The repertoire of mutational signatures in human cancer

(2020) Ludmil B. Alexandrov et al.   NATURE

A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns

(2020) Wei Jiao et al.   Nature Communications

Deep neural network classification based on somatic mutations potentially predicts clinical benefit of immune checkpoint blockade in lung adenocarcinoma

(2020) Jie Peng et al.   OncoImmunology

Passenger mutations accurately classify human tumors

(2019) Marina Salvadores et al.   PLoS Computational Biology

PyRanges: efficient comparison of genomic intervals in Python

(2019) Endre Bakken Stovner et al.   BIOINFORMATICS

Logomaker: beautiful sequence logos in Python

(2019) Ammar Tareen et al.   BIOINFORMATICS

A pan-cancer somatic mutation embedding using autoencoders

(2019) Martin Palazzo et al.   BMC BIOINFORMATICS

Comparative Molecular Analysis of Gastrointestinal Adenocarcinomas

(2018) Yang Liu et al.   CANCER CELL

A Comprehensive Pan-Cancer Molecular Study of Gynecologic and Breast Cancers

(2018) Ashton C. Berger et al.   CANCER CELL

Opportunities and obstacles for deep learning in biology and medicine

(2018) Travers Ching et al.   Journal of the Royal Society Interface

The curse(s) of dimensionality

(2018) Naomi Altman et al.   NATURE METHODS

Multiple instance learning: A survey of problem characteristics and applications

(2018) Marc-André Carbonneau et al.   PATTERN RECOGNITION

Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines

(2018) Kyle Ellrott et al.   Cell Systems

MSIpred: a python package for tumor microsatellite instability classification from tumor mutation annotation data using a support vector machine

(2018) Chen Wang et al.   Scientific Reports

Performance evaluation for rapid detection of pan-cancer microsatellite instability with MANTIS

(2016) Esko A. Kautto et al.   Oncotarget

Predicting effects of noncoding variants with deep learning–based sequence model

(2015) Jian Zhou et al.   NATURE METHODS

Multiple instance classification: Review, taxonomy and comparative study

(2013) Jaume Amores   ARTIFICIAL INTELLIGENCE

Integrated genomic characterization of endometrial carcinoma

(2013) Gad Getz et al.   NATURE

Signatures of mutational processes in human cancer

(2013) Ludmil B. Alexandrov et al.   NATURE

Cancer of unknown primary site

(2012) Nicholas Pavlidis et al.   LANCET

Comprehensive molecular characterization of human colon and rectal cancer

(2012)   NATURE