The Connectivity Map (CMap) is a large publicly available database of
cellular transcriptomic responses to chemical and genetic perturbations built
using a standardized acquisition protocol known as the L1000 technique.
Databases such as CMap provide an exciting opportunity to enrich drug discovery
efforts, providing a 'known' phenotypic landscape to explore and enabling the
development of state of the art techniques for enhanced information extraction
and better informed decisions. Whilst multiple methods for measuring phenotypic
similarity and interrogating profiles have been developed, the field is
severely lacking standardized benchmarks using appropriate data splitting for
training and unbiased evaluation of machine learning methods. To address this,
we have developed 'Leak Proof CMap' and exemplified its application to a set of
common transcriptomic and generic phenotypic similarity methods along with an
exemplar triplet loss-based method. Benchmarking in three critical performance
areas (compactness, distinctness, and uniqueness) is conducted using carefully
crafted data splits ensuring no similar cell lines or treatments with shared or
closely matching responses or mechanisms of action are present in training,
validation, or test sets. This enables testing of models with unseen samples
akin to exploring treatments with novel modes of action in novel patient
derived cell lines. With a carefully crafted benchmark and data splitting
regime in place, the tooling now exists to create performant phenotypic
similarity methods for use in personalized medicine (novel cell lines) and to
better augment high throughput phenotypic screening technologies with the L1000
transcriptomic technology.

使用标准化采集协议 L1000 技术建立的连通图（CMap）是一个大型的公开可用的细胞转录组对化学和基因干扰反应的数据库，文中开发了 “泄漏证据 CMap” 来对表型相似性方法和三重损失方法进行基准测试，以便用于个性化医学和高通量表型筛选技术的增强。

CMap 固定漏洞：一个用于训练和评估细胞系无关 L1000 相似度方法的框架

Leak Proof CMap; a framework for training and evaluation of cell line  agnostic L1000 similarity methods

In this chapter we take a closer look at the distribution of symbolic
regression models generated by genetic programming in the search space. The
motivation for this work is to improve the search for well-fitting symbolic
regression models by using information about the similarity of models that can
be precomputed independently from the target function. For our analysis, we use
a restricted grammar for uni-variate symbolic regression models and generate
all possible models up to a fixed length limit. We identify unique models and
cluster them based on phenotypic as well as genotypic similarity. We find that
phenotypic similarity leads to well-defined clusters while genotypic similarity
does not produce a clear clustering. By mapping solution candidates visited by
GP to the enumerated search space we find that GP initially explores the whole
search space and later converges to the subspace of highest quality expressions
in a run for a simple benchmark problem.

本论文通过使用一定限制语法的编程方法，对符号回归模型空间的分布及模型之间的相似度进行了分析，研究表明凭借表型相似度，可以得出较为明确的模型分类，为符号回归模型的生成提供重要的信息。