The future of digital health with federated learning.

Data-driven machine learning (ML) has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems. Existing medical data is not fully exploited by ML primarily because it sits in data silos and privacy concerns restrict access to this data. However, without access to sufficient data, ML will be prevented from reaching its full potential and, ultimately, from making the transition from research to clinical practice. This paper considers key factors contributing to this issue, explores how federated learning (FL) may provide a solution for the future of digital health and highlights the challenges and considerations that need to be addressed.

Generic accelerated sequence alignment in SeqAn using vectorization and multi-threading.

Motivation: Pairwise sequence alignment is undoubtedly a central tool in many bioinformatics analyses. In this paper, we present a generically accelerated module for pairwise sequence alignments applicable for a broad range of applications. In our module, we unified the standard dynamic programming kernel used for pairwise sequence alignments and extended it with a generalized inter-sequence vectorization layout, such that many alignments can be computed simultaneously by exploiting SIMD (single instruction multiple data) instructions of modern processors. We then extended the module by adding two layers of thread-level parallelization, where we (a) distribute many independent alignments on multiple threads and (b) inherently parallelize a single alignment computation using a work stealing approach producing a dynamic wavefront progressing along the minor diagonal. Results: We evaluated our alignment vectorization and parallelization on different processors, including the newest Intel® Xeon® (Skylake) and Intel® Xeon PhiTM (KNL) processors, and use cases. The instruction set AVX512-BW (Byte and Word), available on Skylake processors, can genuinely improve the performance of vectorized alignments. We could run single alignments 1600 times faster on the Xeon PhiTM and 1400 times faster on the Xeon® than executing them with our previous sequential alignment module. Availability and implementation: The module is programmed in C++ using the SeqAn (Reinert et al., 2017) library and distributed with version 2.4 under the BSD license. We support SSE4, AVX2, AVX512 instructions and included UME: SIMD, a SIMD-instruction wrapper library, to extend our module for further instruction sets. We thoroughly test all alignment components with all major C++ compilers on various platforms. Supplementary information: Supplementary data are available at Bioinformatics online.