Biology subjects

Janizek, J. D.

Publications and source records attributed to Janizek, J. D..

5 recordsLinked to original sources

bioRxiv2022

Principled Feature Attribution for Unsupervised Gene Expression Analysis

As interest in unsupervised deep learning models for the analysis of gene expression data has grown, an increasing number of methods have been developed to make these deep learning models more interpretable. These methods can be separated into two groups: (1) post hoc analyses of black box models through feature attribution methods and (2) approaches to build inherently interpretable models through biologically-constrained architectures. In this work, we argue that these approaches are not mutually exclusive, but can in fact be usefully combined. We propose a novel unsupervised pathway attribution method, which better identifies major sources of transcriptomic variation than prior methods when combined with biologically-constrained neural network models. We demonstrate how principled feature attributions aid in the analysis of a variety of single cell datasets. Finally, we apply our approach to a large dataset of post-mortem brain samples from patients with Alzheimers disease, and show that it identifies Mitochondrial Respiratory Complex I as an important factor in this disease.

bioinformatics↗

bioRxiv2021

Uncovering expression signatures of synergistic drug response using an ensemble of explainable AI models

Complex machine learning models are poised to revolutionize the treatment of diseases like acute myeloid leukemia (AML) by helping physicians choose optimal combinations of anti-cancer drugs based on molecular features. While accurate predictions are important, it is equally important to be able to learn about the underlying molecular basis of anti-cancer drug synergy. Explainable AI (XAI) offers a promising new route for data-driven cancer pharmacology, combining highly accurate models with interpretable insights into model decisions. Due to the highly correlated, high-dimensional nature of cancer transcriptomic data, however, we find that existing XAI approaches are suboptimal when applied naively to large transcriptomic datasets. We show how a novel approach based on model ensembling helps to increase the quality of explanations. We then use our method to demonstrate that a hematopoietic differentiation signature underlies synergy for a variety of anti-AML drug combinations.

cancer biology↗

bioRxiv2020

Adversarial Deconfounding Autoencoder for Learning Robust Gene Expression Embeddings

MotivationIncreasing number of gene expression profiles has enabled the use of complex models, such as deep unsupervised neural networks, to extract a latent space from these profiles. However, expression profiles, especially when collected in large numbers, inherently contain variations introduced by technical artifacts (e.g., batch effects) and uninteresting biological variables (e.g., age) in addition to the true signals of interest. These sources of variations, called confounders, produce embeddings that fail to transfer to different domains, i.e., an embedding learned from one dataset with a specific confounder distribution does not generalize to different distributions. To remedy this problem, we attempt to disentangle confounders from true signals to generate biologically informative embeddings. ResultsIn this paper, we introduce the AD-AE (Adversarial Deconfounding AutoEncoder) approach to deconfounding gene expression latent spaces. The AD-AE model consists of two neural networks: (i) an autoencoder to generate an embedding that can reconstruct original measurements, and (ii) an adversary trained to predict the confounder from that embedding. We jointly train the networks to generate embeddings that can encode as much information as possible without encoding any confounding signal. By applying AD-AE to two distinct gene expression datasets, we show that our model can (1) generate embeddings that do not encode confounder information, (2) conserve the biological signals present in the original space, and (3) generalize successfully across different confounder domains. We demonstrate that AD-AE outperforms standard autoencoder and other deconfounding approaches. AvailabilityOur code and data are available at https://gitlab.cs.washington.edu/abdincer/ad-ae. Contactabdincer@cs.washington.edu; suinlee@cs.washington.edu

bioinformatics↗

bioRxiv2018

Explainable machine learning prediction of synergistic drug combinations for precision cancer medicine

Although combination therapy has been a mainstay of cancer treatment for decades, it remains challenging, both to identify novel effective combinations of drugs and to determine the optimal combination for a particular patients tumor. While there have been several recent efforts to test drug combinations in vitro, examining the immense space of possible combinations is far from being feasible. Thus, it is crucial to develop datadriven techniques to computationally identify the optimal drug combination for a patient. We introduce TreeCombo, an extreme gradient boosted tree-based approach to predict synergy of novel drug combinations, using chemical and physical properties of drugs and gene expression levels of cell lines as features. We find that TreeCombo significantly outperforms three other state-of-theart approaches, including the recently developed DeepSynergy, which uses the same set of features to predict synergy using deep neural networks. Moreover, we found that the predictions from our approach were interpretable, with genes having well-established links to cancer serving as important features for prediction of drug synergy.

cancer biology↗

bioRxiv2018

Accurate functional classification of thousands of BRCA1 variants with saturation genome editing

Variants of uncertain significance (VUS) fundamentally limit the utility of genetic information in a clinical setting. The challenge of VUS is epitomized by BRCA1, a tumor suppressor gene integral to DNA repair and genomic stability. Germline BRCA1 loss-of-function (LOF) variants predispose women to early-onset breast and ovarian cancers. Although BRCA1 has been sequenced in millions of women, the risk associated with most newly observed variants cannot be definitively assigned. Data sharing attenuates this problem but it is unlikely to solve it, as most newly observed variants are exceedingly rare. In lieu of genetic evidence, experimental approaches can be used to functionally characterize VUS. However, to date, functional studies of BRCA1 VUS have been conducted in a post hoc, piecemeal fashion. Here we employ saturation genome editing to assay 96.5% of all possible single nucleotide variants (SNVs) in 13 exons that encode functionally critical domains of BRCA1. Our assay measures cellular fitness in a haploid human cell line whose survival is dependent on intact BRCA1 function. The resulting function scores for nearly 4,000 SNVs are bimodally distributed and almost perfectly concordant with established assessments of pathogenicity. Sequence-function maps enhanced by parallel measurements of variant effects on mRNA levels reveal mechanisms by which loss-of-function SNVs arise. Hundreds of missense SNVs critical for protein function are identified, as well as dozens of exonic and intronic SNVs that compromise BRCA1 function by disrupting splicing or transcript stability. We predict that these function scores will be directly useful for the clinical interpretation of cancer risk based on BRCA1 sequencing. Furthermore, we propose that this paradigm can be extended to overcome the challenge of VUS in other genes in which genetic variation is clinically actionable.

genomics↗