Dietary Restriction (DR) is one of the most popular anti-ageing
interventions, prompting exhaustive research into genes associated with its
mechanisms. Recently, Machine Learning (ML) has been explored to identify
potential DR-related genes among ageing-related genes, aiming to minimize
costly wet lab experiments needed to expand our knowledge on DR. However, to
train a model from positive (DR-related) and negative (non-DR-related)
examples, existing ML methods naively label genes without known DR relation as
negative examples, assuming that lack of DR-related annotation for a gene
represents evidence of absence of DR-relatedness, rather than absence of
evidence; this hinders the reliability of the negative examples (non-DR-related
genes) and the method's ability to identify novel DR-related genes. This work
introduces a novel gene prioritization method based on the two-step
Positive-Unlabelled (PU) Learning paradigm: using a similarity-based,
KNN-inspired approach, our method first selects reliable negative examples
among the genes without known DR associations. Then, these reliable negatives
and all known positives are used to train a classifier that effectively
differentiates DR-related and non-DR-related genes, which is finally employed
to generate a more reliable ranking of promising genes for novel
DR-relatedness. Our method significantly outperforms the existing
state-of-the-art non-PU approach for DR-relatedness prediction in three
relevant performance metrics. In addition, curation of existing literature
finds support for the top-ranked candidate DR-related genes identified by our
model.

引入了一种基于两步正 - 未标记学习范式的新型基因优先排序方法，该方法首先使用基于相似性的类 KNN 方法从没有已知 DR 关系的基因中选择可靠的负例，然后使用这些可靠的负例和所有已知的正例来训练一个分类器，该分类器能够有效区分 DR 相关和非 DR 相关的基因，从而生成更可靠的有关新颖 DR 相关性的有前途的基因排名。这种方法在三个相关性能指标上明显优于现有的非正 - 未标记方法用于 DR 相关性预测。此外，对现有文献的整理发现，我们模型确定的排名靠前的候选 DR 相关基因得到了支持。

正标记无标记学习用于在与衰老相关基因中鉴定新的潜在膳食限制相关基因

Positive-Unlabelled Learning for Identifying New Candidate Dietary  Restriction-related Genes among Ageing-related Genes

Existing language models excel at writing from scratch, but many real-world
scenarios require rewriting an existing document to fit a set of constraints.
Although sentence-level rewriting has been fairly well-studied, little work has
addressed the challenge of rewriting an entire document coherently. In this
work, we introduce the task of document-level targeted content transfer and
address it in the recipe domain, with a recipe as the document and a dietary
restriction (such as vegan or dairy-free) as the targeted constraint. We
propose a novel model for this task based on the generative pre-trained
language model (GPT-2) and train on a large number of roughly-aligned recipe
pairs (this https URL).
Both automatic and human evaluations show that our model out-performs existing
methods by generating coherent and diverse rewrites that obey the constraint
while remaining close to the original document. Finally, we analyze our model's
rewrites to assess progress toward the goal of making language generation more
attuned to constraints that are substantive rather than stylistic.

本文介绍了将文档级别的有针对性的内容转移应用于配方领域中，通过生成式预训练语言模型 (GPT-2) 模型来实现，在保持原始文档的情况下，生成了遵守限制且连贯多样的改写，并评估了改写效果。同时，倡导语言生成更具有实质性的限制性。