In anti-cancer drug development, a major scientific challenge is
disentangling the complex relationships between high-dimensional genomics data
from patient tumor samples, the corresponding tumor's organ of origin, the drug
targets associated with given treatments and the resulting treatment response.
Furthermore, to realize the aspirations of precision medicine in identifying
and adjusting treatments for patients depending on the therapeutic response,
there is a need for building tumor dynamic models that can integrate both
longitudinal tumor size as well as multimodal, high-content data. In this work,
we take a step towards enhancing personalized tumor dynamic predictions by
proposing a heterogeneous graph encoder that utilizes a bipartite Graph
Convolutional Neural network (GCN) combined with Neural Ordinary Differential
Equations (Neural-ODEs). We applied the methodology to a large collection of
patient-derived xenograft (PDX) data, spanning a wide variety of treatments (as
well as their combinations) on tumors that originated from a number of
different organs. We first show that the methodology is able to discover a
tumor dynamic model that significantly improves upon an empirical model which
is in current use. Additionally, we show that the graph encoder is able to
effectively utilize multimodal data to enhance tumor predictions. Our findings
indicate that the methodology holds significant promise and offers potential
applications in pre-clinical settings.

使用异质图编码器结合神经普通微分方程的方法，在个性化肿瘤动态预测方面取得了显著改进，表明该方法在临床前研究中具有潜在应用。

图神经网络和神经微分方程在肿瘤动态预测中的融合

Integration of Graph Neural Network and Neural-ODEs for Tumor Dynamic  Prediction

Given the increasing volume and quality of genomics data, extracting new
insights requires interpretable machine-learning models. This work presents
Genomic Interpreter: a novel architecture for genomic assay prediction. This
model outperforms the state-of-the-art models for genomic assay prediction
tasks. Our model can identify hierarchical dependencies in genomic sites. This
is achieved through the integration of 1D-Swin, a novel Transformer-based block
designed by us for modelling long-range hierarchical data. Evaluated on a
dataset containing 38,171 DNA segments of 17K base pairs, Genomic Interpreter
demonstrates superior performance in chromatin accessibility and gene
expression prediction and unmasks the underlying `syntax' of gene regulation.

本研究利用建立新的架构模型 Genomic Interpreter 对基因组测序数据进行预测，通过整合 1D-Swin 对数据的层级依赖性进行建模，成功识别染色质可及性和基因表达的预测，揭示了基因调控的潜在语法。

基因组解释器：一种具有 1D 平移窗口变换器的层次基因组深度神经网络

Genomic Interpreter: A Hierarchical Genomic Deep Neural Network with 1D  Shifted Window Transformer

Genomics data such as RNA gene expression, methylation and micro RNA
expression are valuable sources of information for various clinical predictive
tasks. For example, predicting survival outcomes, cancer histology type and
other patients' related information is possible using not only clinical data
but molecular data as well. Moreover, using these data sources together, for
example in multitask learning, can boost the performance. However, in practice,
there are many missing data points which leads to significantly lower patient
numbers when analysing full cases, which in our setting refers to all
modalities being present.
In this paper we investigate how imputing data with missing values using deep
learning coupled with multitask learning can help to reach state-of-the-art
performance results using combined genomics modalities, RNA, micro RNA and
methylation. We propose a generalised deep imputation method to impute values
where a patient has all modalities present except one. Interestingly enough,
deep imputation alone outperforms multitask learning alone for the
classification and regression tasks across most combinations of modalities. In
contrast, when using all modalities for survival prediction we observe that
multitask learning alone outperforms deep imputation alone with statistical
significance (adjusted p-value 0.03). Thus, both approaches are complementary
when optimising performance for downstream predictive tasks.

本文介绍了使用深度学习对缺失值进行插值的方法，以促进基因组数据的应用，混合使用 RNA、microRNA 和甲基化可以提高性能，但是多任务学习仅在生存预测中表现出优越性。

将深度插值与多任务学习相结合，用于基因组数据的下游任务

Coupling Deep Imputation with Multitask Learning for Downstream Tasks on Genomics Data

We consider regression under the "extremely small $n$ large $p$" condition,
where the number of samples $n$ is so small compared to the dimensionality $p$
that predictors cannot be estimated without prior knowledge. This setup occurs
in personalized medicine, for instance, when predicting treatment outcomes for
an individual patient based on noisy high-dimensional genomics data. A
remaining source of information is expert knowledge, which has received
relatively little attention in recent years. We formulate the inference problem
of asking expert feedback on features on a budget, propose an elicitation
strategy for a simple "small $n$" setting, and derive conditions under which
the elicitation strategy is optimal. Experiments on simulated experts, both on
synthetic and genomics data, demonstrate that the proposed strategy can
drastically improve prediction accuracy.

针对极小的 $n$ 大 $p$ 条件，我们考虑回归问题，其中样本数量 $n$ 相对于维度 $p$ 很小，且不能在没有先前知识的情况下预测特征。 我们提出了一个简单的小 $n$ 环境下的证据策略，并推导出策略最优的条件，我们在模拟专家的实验中发现，所提出的策略可以极大地提高预测准确性。