Genome data are crucial in modern medicine, offering significant potential
for diagnosis and treatment. Thanks to technological advancements, many
millions of healthy and diseased genomes have already been sequenced; however,
obtaining the most suitable data for a specific study, and specifically for
validation studies, remains challenging with respect to scale and access.
Therefore, in silico genomics sequence generators have been proposed as a
possible solution. However, the current generators produce inferior data using
mostly shallow (stochastic) connections, detected with limited computational
complexity in the training data. This means they do not take the appropriate
biological relations and constraints, that originally caused the observed
connections, into consideration. To address this issue, we propose
cancer-inspired genomics mapper model (CGMM), that combines genetic algorithm
(GA) and deep learning (DL) methods to tackle this challenge. CGMM mimics
processes that generate genetic variations and mutations to transform readily
available control genomes into genomes with the desired phenotypes. We
demonstrate that CGMM can generate synthetic genomes of selected phenotypes
such as ancestry and cancer that are indistinguishable from real genomes of
such phenotypes, based on unsupervised clustering. Our results show that CGMM
outperforms four current state-of-the-art genomics generators on two different
tasks, suggesting that CGMM will be suitable for a wide range of purposes in
genomic medicine, especially for much-needed validation studies.

文章提出了一种结合遗传算法和深度学习的癌症灵感的基因组绘图模型 (CGMM)，用于生成合成基因组以进行研究验证，并证明其在两项不同任务中优于当前四种最先进的基因组生成器，适用于基因组医学的诊断和治疗。

基于癌症启发的基因组编图模型生成具有所需基因组特征的合成 DNA 序列

Cancer-inspired Genomics Mapper Model for the Generation of Synthetic  DNA Sequences with Desired Genomics Signatures

Natural language generation (NLG) has received increasing attention, which
has highlighted evaluation as a central methodological concern. Since human
evaluations for these systems are costly, automatic metrics have broad appeal
in NLG. Research in language generation often finds situations where it is
appropriate to apply existing metrics or propose new ones. The application of
these metrics are entirely dependent on validation studies - studies that
determine a metric's correlation to human judgment. However, there are many
details and considerations in conducting strong validation studies. This
document is intended for those validating existing metrics or proposing new
ones in the broad context of NLG: we 1) begin with a write-up of best practices
in validation studies, 2) outline how to adopt these practices, 3) conduct
analyses in the WMT'17 metrics shared task\footnote{Our jupyter notebook
containing the analyses is available at https://github.com}, and 4)
highlight promising approaches to NLG metrics 5) conclude with our opinions on
the future of this area.

本文主要探讨自然语言生成领域中测评方法中的自动指标的应用和验证，提出了验证研究的最佳实践，并在 WMT'17 度量共享任务中进行了分析，同时也突出了未来的发展方向。