Predicting genetic mutations from whole slide images is indispensable for
cancer diagnosis. However, existing work training multiple binary
classification models faces two challenges: (a) Training multiple binary
classifiers is inefficient and would inevitably lead to a class imbalance
problem. (b) The biological relationships among genes are overlooked, which
limits the prediction performance. To tackle these challenges, we innovatively
design a Biological-knowledge enhanced PathGenomic multi-label Transformer to
improve genetic mutation prediction performances. BPGT first establishes a
novel gene encoder that constructs gene priors by two carefully designed
modules: (a) A gene graph whose node features are the genes' linguistic
descriptions and the cancer phenotype, with edges modeled by genes' pathway
associations and mutation consistencies. (b) A knowledge association module
that fuses linguistic and biomedical knowledge into gene priors by
transformer-based graph representation learning, capturing the intrinsic
relationships between different genes' mutations. BPGT then designs a label
decoder that finally performs genetic mutation prediction by two tailored
modules: (a) A modality fusion module that firstly fuses the gene priors with
critical regions in WSIs and obtains gene-wise mutation logits. (b) A
comparative multi-label loss that emphasizes the inherent comparisons among
mutation status to enhance the discrimination capabilities. Sufficient
experiments on The Cancer Genome Atlas benchmark demonstrate that BPGT
outperforms the state-of-the-art.

从全幻灯图像中预测基因突变对于癌症诊断至关重要。本研究设计了一种生物知识增强的 PathGenomic 多标签 Transformer，通过构建基因优先级和基因突变预测模块，提高了预测性能，实验证明该方法优于现有最先进技术。

通过生物医学 - 语言知识增强的多标签分类预测基因突变从全切片图像中

Predicting Genetic Mutation from Whole Slide Images via  Biomedical-Linguistic Knowledge Enhanced Multi-label Classification

Machine Learning (ML) alleviates the challenges of high-dimensional data
analysis and improves decision making in critical applications like healthcare.
Effective cancer type from high-dimensional genetic mutation data can be useful
for cancer diagnosis and treatment, if the distinguishable patterns between
cancer types are identified. At the same time, analysis of high-dimensional
data is computationally expensive and is often outsourced to cloud services.
Privacy concerns in outsourced ML, especially in the field of genetics,
motivate the use of encrypted computation, like Homomorphic Encryption (HE).
But restrictive overheads of encrypted computation deter its usage. In this
work, we explore the challenges of privacy preserving cancer detection using a
real-world dataset consisting of more than 2 million genetic information for
several cancer types. Since the data is inherently high-dimensional, we explore
smaller ML models for cancer prediction to enable fast inference in the privacy
preserving domain. We develop a solution for privacy preserving cancer
inference which first leverages the domain knowledge on somatic mutations to
efficiently encode genetic mutations and then uses statistical tests for
feature selection. Our logistic regression model, built using our novel
encoding scheme, achieves 0.98 micro-average area under curve with 13% higher
test accuracy than similar studies. We exhaustively test our model's predictive
capabilities by analyzing the genes used by the model. Furthermore, we propose
a fast matrix multiplication algorithm that can efficiently handle
high-dimensional data. Experimental results show that, even with 40,000
features, our proposed matrix multiplication algorithm can speed up concurrent
inference of multiple individuals by approximately 10x and inference of a
single individual by approximately 550x, in comparison to standard matrix
multiplication.

本文探讨隐私保护癌症检测的挑战，提出了一个利用机器学习和遗传突变等领域知识进行的、保护隐私的癌症预测方案。该方案利用 Homomorphic Encryption 加密方法，并使用统计测试进行特征选择，其中所提出的逻辑回归模型，与其他研究相比，测试时具有更高的准确性。因此，本文提出的解决方案在高效地使用密文计算技术方面具有很大的优势。

采用同态加密的可伸缩隐私保护癌症类型预测

Scalable privacy-preserving cancer type prediction with homomorphic encryption

Extracting genetic information from a full range of sequencing data is
important for understanding diseases. We propose a novel method to effectively
explore the landscape of genetic mutations and aggregate them to predict cancer
type. We used multinomial logistic regression, nonsmooth non-negative matrix
factorization (nsNMF), and support vector machine (SVM) to utilize the full
range of sequencing data, aiming at better aggregating genetic mutations and
improving their power in predicting cancer types. Specifically, we introduced a
classifier to distinguish cancer types using somatic mutations obtained from
whole-exome sequencing data. Mutations were identified from multiple cancers
and scored using SIFT, PP2, and CADD, and grouped at the individual gene level.
The nsNMF was then applied to reduce dimensionality and to obtain coefficient
and basis matrices. A feature matrix was derived from the obtained matrices to
train a classifier for cancer type classification with the SVM model. We have
demonstrated that the classifier was able to distinguish the cancer types with
reasonable accuracy. In five-fold cross-validations using mutation counts as
features, the average prediction accuracy was 77.1% (SEM=0.1%), significantly
outperforming baselines and outperforming models using mutation scores as
features. Using the factor matrices derived from the nsNMF, we identified
multiple genes and pathways that are significantly associated with each cancer
type. This study presents a generic and complete pipeline to study the
associations between somatic mutations and cancers. The discovered genes and
pathways associated with each cancer type can lead to biological insights. The
proposed method can be adapted to other studies for disease classification and
pathway discovery.

该研究提出了一种新的方法，利用多项逻辑回归、非平滑非负矩阵分解 (nsNMF) 和支持向量机 (SVM) 等算法，从全序列数据中提取遗传信息以更好地预测癌症类型。实验表明，该方法在突变计数、突变得分等方面均表现优异，可用于其他疾病分类和通路发现研究。