Cancer is an umbrella term that includes a wide spectrum of disease severity, from those that are malignant, metastatic, and aggressive to benign lesions with very low potential for progression or death. The ability to prognosticate patient outcomes would facilitate management of various malignancies: patients whose cancer is likely to advance quickly would receive necessary treatment that is commensurate with the predicted biology of the disease. Former prognostic models based on clinical variables (age, gender, cancer stage, tumor grade, etc.), though helpful, cannot account for genetic differences, molecular etiology, tumor heterogeneity, and important host biological mechanisms. Therefore, recent prognostic models have shifted toward the integration of complementary information available in both molecular data and clinical variables to better predict patient outcomes: vital status (overall survival), metastasis (metastasis-free survival), and recurrence (progression-free survival). In this article, we review 20 survival prediction approaches that integrate multi-omics and clinical data to predict patient outcomes. We discuss their strategies for modeling survival time (continuous and discrete), the incorporation of molecular measurements and clinical variables into risk models (clinical and multi-omics data), how to cope with censored patient records, the effectiveness of data integration techniques, prediction methodologies, model validation, and assessment metrics. The goal is to inform life scientists of available resources, and to provide a complete review of important building blocks in survival prediction. At the same time, we thoroughly describe the pros and cons of each methodology, and discuss in depth the outstanding challenges that need to be addressed in future method development.

Molecular subtyping is fundamental in cancer research and clinical management of cancer, guiding treatment planning, monitoring therapeutic response, and informing prognosis. Early methods were designed specifically for gene expression data due to the lack of other molecular data types. Thanks to breakthroughs in high-throughput technologies, recent subtyping tools have shifted their focus to integrating multi-omics profiles to uncover novel subtypes that better reflect genetic variation, molecular pathogenesis, tumor heterogeneity, and host response biological mechanisms. However, these integrative approaches have not been able to fully exploit the complementary potentials of diverse molecular data types. They often rely on specific omics types with large common sample size and fail to incorporate important biological knowledge in their models. Here, we introduce Disease subtyping using Spectral clustering and Community detection from Consensus networks (DSCC), a method designed to identify meaningful disease subtypes from a wide range of molecular data, including gene expression, miRNA expression, DNA methylation, copy number variation, somatic mutations, protein abundance, and metabolite levels. We demonstrate the superiority of DSCC over state-of-the-art cancer subtyping methods using 43 cancer datasets with more than 11,000 patients. Furthermore, the incorporation of DSCC-derived subtype information as a covariate in prognostic models improves survival prediction accuracy and robustness. The DSCC source code, data, and scripts for reproducing all results in this study are available at https://github.com/tinnlab/DSCC.

This manuscript describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' (https://github.com/NIGMS/NIGMS-Sandbox). The module delivers learning materials on Cloud-based Consensus Pathway Analysis in an interactive format that uses appropriate cloud resources for data access and analyses. Pathway analysis is important because it allows us to gain insights into biological mechanisms underlying conditions. But the availability of many pathway analysis methods, the requirement of coding skills, and the focus of current tools on only a few species all make it very difficult for biomedical researchers to self-learn and perform pathway analysis efficiently. Furthermore, there is a lack of tools that allow researchers to compare analysis results obtained from different experiments and different analysis methods to find consensus results. To address these challenges, we have designed a cloud-based, self-learning module that provides consensus results among established, state-of-the-art pathway analysis techniques to provide students and researchers with necessary training and example materials. The training module consists of five Jupyter Notebooks that provide complete tutorials for the following tasks: (i) process expression data, (ii) perform differential analysis, visualize and compare the results obtained from four differential analysis methods (limma, t-test, edgeR, DESeq2), (iii) process three pathway databases (GO, KEGG and Reactome), (iv) perform pathway analysis using eight methods (ORA, CAMERA, KS test, Wilcoxon test, FGSEA, GSA, SAFE and PADOG) and (v) combine results of multiple analyses. We also provide examples, source code, explanations and instructional videos for trainees to complete each Jupyter Notebook. The module supports the analysis for many model (e.g. human, mouse, fruit fly, zebra fish) and non-model species. The module is publicly available at https://github.com/NIGMS/Consensus-Pathway-Analysis-in-the-Cloud. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Nanophotonic is an emerging technology considered as one of the key solutions for future generation on-chip interconnects. Indeed, this technology provides high bandwidth for data transfers and can be a very interesting alternative to bypass the bottleneck induced by classical NoC. However, their implementation in fully integrated 3D circuits remains uncertain due to the high power consumption of on-chip lasers. However, if a specific bit error rate is targeted, digital processing can be added in the electrical domain to reduce the laser power and keep the same communication reliability. This paper addresses this problem and proposesto transmit encoded data on the optical interconnect, which allows for a reduction of the laser power consumption, thus increasing nanophotonics interconnects energy efficiency. The results presented in this paper show that using simple Hamming coder and decoder permits to reduce the laser power by nearly 50% without loss in communication data rate and with a negligible hardware overhead.

Optical Network-on-Chip (ONoC) is a promising communication medium for large-scale Multiprocessor System on Chip (MPSoC). ONoC outperforms classical electrical NoC in terms of throughput and latency. The medium can support multiple transactions at the same time on different wavelengths by using Wavelength Division Multiplexing (WDM). Moreover multiple wavelengths can be used as high-bandwidth channel to reduce transmission time. However, multiple signals sharing simultaneously a waveguide can lead to inter-channel crosstalk noise. This problem impacts the Signal to Noise Ratio (SNR) of the optical signal, which leads to an increase in the Bit Error Rate (BER) at the receiver side. In this paper we first formulate the crosstalk noise and execution time models and then propose a Wavelength Allocation (WA) method in a ring-based WDM ONoC allowing to search for performance and energy trade-offs, based on the application constraints. As result, most promising WA solutions are highlighted for a defined application mapping onto 16-core WDM ONoC.