With the advent of Information technology, the Bioinformatics research field
is becoming increasingly attractive to researchers and academicians. The recent
development of various Bioinformatics toolkits has facilitated the rapid
processing and analysis of vast quantities of biological data for human
perception. Most studies focus on locating two connected diseases and making
some observations to construct diverse gene regulatory interaction networks, a
forerunner to general drug design for curing illness. For instance,
Hypopharyngeal cancer is a disease that is associated with EGFR-mutated lung
adenocarcinoma. In this study, we select EGFR-mutated lung adenocarcinoma and
Hypopharyngeal cancer by finding the Lung metastases in hypopharyngeal cancer.
To conduct this study, we collect Mircorarray datasets from GEO (Gene
Expression Omnibus), an online database controlled by NCBI. Differentially
expressed genes, common genes, and hub genes between the selected two diseases
are detected for the succeeding move. Our research findings have suggested
common therapeutic molecules for the selected diseases based on 10 hub genes
with the highest interactions according to the degree topology method and the
maximum clique centrality (MCC). Our suggested therapeutic molecules will be
fruitful for patients with those two diseases simultaneously.

使用生物信息学工具对 EGFR 突变型肺腺癌和咽下癌进行基因调控网络分析，发现了共同治疗分子，为这两种疾病同时患者提供了有益的治疗方法。

探索基因调控互作网络并预测咽喉癌和 EGFR 突变肺腺癌的治疗分子

Exploring Gene Regulatory Interaction Networks and predicting  therapeutic molecules for Hypopharyngeal Cancer and EGFR-mutated lung  adenocarcinoma

The discovery of therapeutic molecules is fundamentally a multi-objective
optimization problem. One formulation of the problem is to identify molecules
that simultaneously exhibit strong binding affinity for a target protein,
minimal off-target interactions, and suitable pharmacokinetic properties.
Inspired by prior work that uses active learning to accelerate the
identification of strong binders, we implement multi-objective Bayesian
optimization to reduce the computational cost of multi-property virtual
screening and apply it to the identification of ligands predicted to be
selective based on docking scores to on- and off-targets. We demonstrate the
superiority of Pareto optimization over scalarization across three case
studies. Further, we use the developed optimization tool to search a virtual
library of over 4M molecules for those predicted to be selective dual
inhibitors of EGFR and IGF1R, acquiring 100% of the molecules that form the
library's Pareto front after exploring only 8% of the library. This workflow
and associated open source software can reduce the screening burden of
molecular design projects and is complementary to research aiming to improve
the accuracy of binding predictions and other molecular properties.

在多目标优化问题中，通过使用多目标贝叶斯优化方法，可以减少多属性虚拟筛选的计算成本，并成功地在三个案例研究中展示了 Pareto 优化的优越性。此外，我们使用开发的优化工具搜索了包含四百万种分子的虚拟库，仅探索了该库的 8% 即获得了该库 Pareto 前沿的 100%。这一工作流程和相关开源软件可以减轻分子设计项目的筛选负担，并且与提高结合预测准确性和其他分子特性相关的研究是互补的。