While most deep learning approaches are developed for single images, in real‐world applications, images are often obtained as a series to inform decision‐making. Due to hardware (memory) and software (algorithm) limitations, few methods have been developed to integrate multiple images so far. Herein, an approach that seamlessly integrates deep learning and traditional machine learning models is presented, to study multiple images and score joint damages in rheumatoid arthritis. This method allows the quantification of joining space narrowing to approach the clinical upper limit. Beyond predictive performance, the multilevel interconnections across joints and damage types into the machine learning model are integrated and the crossregulation map of joint damages in rheumatoid arthritis is revealed. An interactive preprint version of the article can be found at
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https://doi.org/10.22541/au.165828097.71839600/v1 . -
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Abstract A promising alternative to comprehensively performing genomics experiments is to, instead, perform a subset of experiments and use computational methods to impute the remainder. However, identifying the best imputation methods and what measures meaningfully evaluate performance are open questions. We address these questions by comprehensively analyzing 23 methods from the ENCODE Imputation Challenge. We find that imputation evaluations are challenging and confounded by distributional shifts from differences in data collection and processing over time, the amount of available data, and redundancy among performance measures. Our analyses suggest simple steps for overcoming these issues and promising directions for more robust research.
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Abstract Motivation Finding driver genes that are responsible for the aberrant proliferation rate of cancer cells is informative for both cancer research and the development of targeted drugs. The established experimental and computational methods are labor-intensive. To make algorithms feasible in real clinical settings, methods that can predict driver genes using less experimental data are urgently needed.
Results We designed an effective feature selection method and used Support Vector Machines (SVM) to predict the essentiality of the potential driver genes in cancer cell lines with only 10 genes as features. The accuracy of our predictions was the highest in the Broad-DREAM Gene Essentiality Prediction Challenge. We also found a set of genes whose essentiality could be predicted much more accurately than others, which we called Accurately Predicted (AP) genes. Our method can serve as a new way of assessing the essentiality of genes in cancer cells.
Availability and implementation The raw data that support the findings of this study are available at Synapse. https://www.synapse.org/#! Synapse: syn2384331/wiki/62825. Source code is available at GitHub. https://github.com/GuanLab/DREAM-Gene-Essentiality-Challenge.
Supplementary information Supplementary data are available at Bioinformatics online.
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Abstract Motivation Reversible protein phosphorylation is an essential post-translational modification regulating protein functions and signaling pathways in many cellular processes. Aberrant activation of signaling pathways often contributes to cancer development and progression. The mass spectrometry-based phosphoproteomics technique is a powerful tool to investigate the site-level phosphorylation of the proteome in a global fashion, paving the way for understanding the regulatory mechanisms underlying cancers. However, this approach is time-consuming and requires expensive instruments, specialized expertise and a large amount of starting material. An alternative in silico approach is predicting the phosphoproteomic profiles of cancer patients from the available proteomic, transcriptomic and genomic data.
Results Here, we present a winning algorithm in the 2017 NCI-CPTAC DREAM Proteogenomics Challenge for predicting phosphorylation levels of the proteome across cancer patients. We integrate four components into our algorithm, including (i) baseline correlations between protein and phosphoprotein abundances, (ii) universal protein–protein interactions, (iii) shareable regulatory information across cancer tissues and (iv) associations among multi-phosphorylation sites of the same protein. When tested on a large held-out testing dataset of 108 breast and 62 ovarian cancer samples, our method ranked first in both cancer tissues, demonstrating its robustness and generalization ability.
Availability and implementation Our code and reproducible results are freely available on GitHub: https://github.com/GuanLab/phosphoproteome_prediction.
Supplementary information Supplementary data are available at Bioinformatics online.
