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"Guan, Yuanfang"
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DeepSleep convolutional neural network allows accurate and fast detection of sleep arousal
Sleep arousals are transient periods of wakefulness punctuated into sleep. Excessive sleep arousals are associated with symptoms such as sympathetic activation, non-restorative sleep, and daytime sleepiness. Currently, sleep arousals are mainly annotated by human experts through looking at 30-second epochs (recorded pages) manually, which requires considerable time and effort. Here we present a deep learning approach for automatically segmenting sleep arousal regions based on polysomnographic recordings. Leveraging a specific architecture that ‘translates’ input polysomnographic signals to sleep arousal labels, this algorithm ranked first in the “You Snooze, You Win” PhysioNet Challenge. We created an augmentation strategy by randomly swapping similar physiological channels, which notably improved the prediction accuracy. Our algorithm enables fast and accurate delineation of sleep arousal events at the speed of 10 seconds per sleep recording. This computational tool would greatly empower the scoring process in clinical settings and accelerate studies on the impact of arousals.Li and Guan present a deep learning approach for automatically segmenting sleep arousal regions based on polysomnographic recordings. The algorithm, which won an open competition, enables fast and accurate delineation of sleep arousal events and would be useful in the scoring process in clinical studies.
Journal Article
FastClone is a probabilistic tool for deconvoluting tumor heterogeneity in bulk-sequencing samples
Dissecting tumor heterogeneity is a key to understanding the complex mechanisms underlying drug resistance in cancers. The rich literature of pioneering studies on tumor heterogeneity analysis spurred a recent community-wide benchmark study that compares diverse modeling algorithms. Here we present FastClone, a top-performing algorithm in accuracy in this benchmark. FastClone improves over existing methods by allowing the deconvolution of subclones that have independent copy number variation events within the same chromosome regions. We characterize the behavior of FastClone in identifying subclones using stage III colon cancer primary tumor samples as well as simulated data. It achieves approximately 100-fold acceleration in computation for both simulated and patient data. The efficacy of FastClone will allow its application to large-scale data and clinical data, and facilitate personalized medicine in cancers.
Multiple algorithms exist for predicting heterogeneity and clonal architecture from the bulk sequencing of tumor tissue. Here, the authors report on an algorithm, FastClone, which was developed from a DREAM challenge and show that FastClone can accurately predict clonality in simulated data and data from colon cancer.
Journal Article
Deep learning prediction of patient response time course from early data via neural-pharmacokinetic/pharmacodynamic modelling
Longitudinal analyses of patient response time courses following doses of therapeutics are currently performed using pharmacokinetic/pharmacodynamic (PK/PD) methodologies, which require considerable human experience and expertise in the modelling of dynamical systems. By utilizing recent advancements in deep learning, we show that the governing differential equations can be learned directly from longitudinal patient data. In particular, we propose a novel neural-PK/PD framework that combines key pharmacological principles with neural ordinary differential equations. We applied it to an analysis of drug concentration and platelet response from a clinical dataset consisting of over 600 patients. We show that the neural-PK/PD model improves on a state-of-the-art model with respect to metrics for temporal prediction. Furthermore, by incorporating key PK/PD concepts into its architecture, the model can generalize and enable the simulations of patient responses to untested dosing regimens. These results demonstrate the potential of neural-PK/PD for automated predictive analytics of patient response time course.
The response of the body to drugs follows complex dynamical processes that can be difficult to predict. Lu and colleagues combine a neural network approach with pharmacokinetic/pharmacodynamic modelling to learn these complex dynamics.
Journal Article
Asymmetric predictive relationships across histone modifications
Decoding the epigenomic landscapes in diverse tissues and cell types is fundamental to understanding molecular mechanisms underlying many essential cellular processes and human diseases. Recent advances in artificial intelligence provide new methods and strategies for imputing unknown epigenomes based on existing data, yet how to reveal the predictive relationships among epigenetic marks remains largely unexplored. Here we present a machine learning approach for epigenomic imputation and interpretation. Through dissection of the spatial contributions from six histone marks, we reveal the prevalent and asymmetric cross-prediction relationships among these marks. Meanwhile, our approach achieved high predictive performance on held-out prospective epigenomes and outperformed the state of the art. To facilitate future research, we further applied this approach to impute a total of 527 and 2,455 unavailable genome-wide histone modification signal tracks for the ENCODE3 and Roadmap datasets, respectively.
Knowledge of the wide array of epigenomic signals provides biological insight into the state of a give cell type, but it is infeasible to experimentally characterize all possible types of epigenomic signal in the multitude of cell types in the human body. The authors present Ocelot, a machine learning approach for imputing cell-type-specific epigenomic signals along the genome.
Journal Article
Joint learning improves protein abundance prediction in cancers
Background
The classic central dogma in biology is the information flow from DNA to mRNA to protein, yet complicated regulatory mechanisms underlying protein translation often lead to weak correlations between mRNA and protein abundances. This is particularly the case in cancer samples and when evaluating the same gene across multiple samples.
Results
Here, we report a method for predicting proteome from transcriptome, using a training dataset provided by NCI-CPTAC and TCGA, consisting of transcriptome and proteome data from 77 breast and 105 ovarian cancer samples. First, we establish a generic model capturing the correlation between mRNA and protein abundance of a single gene. Second, we build a gene-specific model capturing the interdependencies among multiple genes in a regulatory network. Third, we create a cross-tissue model by joint learning the information of shared regulatory networks and pathways across cancer tissues. Our method ranked first in the NCI-CPTAC DREAM Proteogenomics Challenge, and the predictive performance is close to the accuracy of experimental replicates. Key functional pathways and network modules controlling the proteomic abundance in cancers were revealed, in particular metabolism-related genes.
Conclusions
We present a method to predict proteome from transcriptome, leveraging data from different cancer tissues to build a trans-tissue model, and suggest how to integrate information from multiple cancers to provide a foundation for further research.
Journal Article
ImageDoubler: image-based doublet identification in single-cell sequencing
Single-cell sequencing provides detailed insights into individual cell behaviors within complex systems based on the assumption that each cell is uniquely isolated. However, doublets—where two or more cells are sequenced together—disrupt this assumption and can lead to potential data misinterpretations. Traditional doublet detection methods primarily rely on simulated genomic data, which may be less effective in homogeneous cell populations and can introduce biases from experimental processes. Therefore, we introduce ImageDoubler in this study, an innovative image-based model that identifies doublets and missing samples leveraging the Fluidigm single-cell sequencing image data. Our approach showcases a notable doublet detection efficacy, achieving a rate up to 93.87% and registering a minimum improvement of 33.1% in F1 scores compared to existing genomic-based methods. This advancement highlights the potential of using imaging to glean insight into developing doublet detection algorithms and exposes the limitations inherent in current genomic-based techniques.
The accuracy of single-cell sequencing can be hampered by the occurrence of doublets, where two or more cells are sequenced together, thus leading to data misinterpretation. Here, the authors present ImageDoubler, an image-based algorithm for detecting doublets and missing samples in single-cell sequencing.
Journal Article
Heterogeneous digital biomarker integration out-performs patient self-reports in predicting Parkinson’s disease
Parkinson’s disease (PD) is one of the first diseases where digital biomarkers demonstrated excellent performance in differentiating disease from healthy individuals. However, no study has systematically compared and leveraged multiple types of digital biomarkers to predict PD. Particularly, machine learning works on the fine-motor skills of PD are limited. Here, we developed deep learning methods that achieved an AUC (Area Under the receiver operator characteristic Curve) of 0.933 in identifying PD patients on 6418 individuals using 75048 tapping accelerometer and position records. Performance of tapping is superior to gait/rest and voice-based models obtained from the same benchmark population. Assembling the three models achieved a higher AUC of 0.944. Notably, the models not only correlated strongly to, but also performed better than patient self-reported symptom scores in diagnosing PD. This study demonstrates the complementary predictive power of tapping, gait/rest and voice data and establishes integrative deep learning-based models for identifying PD.
Deng et al. develop deep learning methods that identify Parkinson’s Disease (PD) patients using public accelerometer and position data with higher accuracy than when using gait/rest and voice-based models. Their study demonstrates the complementary predictive power of tapping, gait/rest and voice data and establishes integrative deep learning-based models for identifying PD.
Journal Article