Interpretable deep learning models for translational medicine
用于转化医学的可解释深度学习模型
基本信息
- 批准号:10371139
- 负责人:
- 金额:$ 31.27万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2015
- 资助国家:美国
- 起止时间:2015-04-01 至 2024-03-31
- 项目状态:已结题
- 来源:
- 关键词:AffectAlgorithmsAntineoplastic AgentsBig DataBiologicalCancer PatientCancer cell lineCell modelCell physiologyCellsDataDiseaseEventGene ExpressionGeneticGenetic TranscriptionGrainHumanImmune EvasionImmunotherapyIndividualInformation TheoryInterventionKnowledgeLearningLibrariesLightMalignant NeoplasmsMessenger RNAMethodologyMicroRNAsMiningModelingMolecularMonitorNatureNetwork-basedOrganoidsOutcomePaperPathologicPathway interactionsPatientsPharmacologyPhenotypePhysiologicalPublishingResearchResearch PersonnelSideSignal PathwaySignal TransductionSignaling MoleculeStructureSystemSystems BiologyTechniquesTechnologyThe Cancer Genome AtlasTrainingTranslational ResearchUnited States National Institutes of HealthYeastsbasebiological systemscancer cellcancer therapycell behaviorchemical geneticsdata modelingdeep field surveydeep learningdeep learning algorithmdeep learning modeldesigndrug sensitivityexperiencegenome-wideinnovationinquiry-based learninginsightlearning algorithmlearning strategymachine learning algorithmmachine learning methodnovelpre-clinicalprecision medicineprecision oncologypredicting responsepreventresponsesingle-cell RNA sequencingsuccesstheoriestooltranscription factortranscriptometranscriptomicstranslational applicationstranslational impacttranslational medicinetreatment responsetumortumor microenvironment
项目摘要
Understanding the state of cellular signaling systems provides insights to how cells behave under physiological
and pathological conditions. Cellular signaling systems are organized as hierarchy (cascade) and signals of a
molecular is often compositionally encoded to control cellular processes, such as gene expression. This
project aims to develop advanced deep learning models (DLMs) to simulate cellular signaling systems based
on gene expression data. In last 3 years, the project has made significant progresses, but the challenges
remain. Importantly, contemporary DLMs behave as “black boxes”, in that it is difficult to interpret how signals
are encoded and how to interpret which signal a hidden node represent in a DLM. This black-box nature
prevents researchers from gaining biological insights using DLMs, even though these models can be much
superior in modeling data than other types of models in many tasks, e.g., predicting drug sensitivity of cancer
cells. In this competitive renewal, we propose to develop novel DLMs and innovative inference algorithms to
train “interpretable” DLMs and apply them in translational research. The proposed research is innovative and
of high significance in several perspectives: 1) Our novel DLMs and algorithms take advantage of big data
resulting from systematic chemical/genetic perturbations of cellular signaling machinery, so that we can use
the perturbation condition as side information to reveal how signals are encoded in a DLM. 2) We integrate
principles of causal inference and information theory with deep learning method to make DLMs interpretable.
As results, that researchers can gain mechanistic insights from such models. 3) Innovative application of
interpretable DLMs will advance translational research. For example, we will train interpretable DLMs to model
cellular signaling at the level of single cells and use this information investigate inter-cellular interactions
among cells in tumor microenvironment to shed light on immune evasion mechanisms of cancers. We will also
use information derived from interpretable DLMs to predict cancer cell drug sensitivity. We anticipate that our
study will bring forth significant advances not only in deep learning methodology but also in precision medicine.
了解细胞信号系统的状态有助于了解细胞在生理条件下的行为
项目成果
期刊论文数量(0)
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{{ truncateString('XINGHUA LU', 18)}}的其他基金
Interpretable deep learning models for translational medicine
用于转化医学的可解释深度学习模型
- 批准号:
10579895 - 财政年份:2015
- 资助金额:
$ 31.27万 - 项目类别:
Interpretable deep learning models for translational medicine
用于转化医学的可解释深度学习模型
- 批准号:
10171908 - 财政年份:2015
- 资助金额:
$ 31.27万 - 项目类别:
Deciphering cellular signaling system by deep mining a comprehensive genomic compendium
通过深入挖掘全面的基因组纲要来破译细胞信号系统
- 批准号:
9042426 - 财政年份:2015
- 资助金额:
$ 31.27万 - 项目类别:
Ontology-Driven Methods for Knowledge Acquisition and Knowledge Discovery
本体驱动的知识获取和知识发现方法
- 批准号:
8202896 - 财政年份:2011
- 资助金额:
$ 31.27万 - 项目类别:
Ontology-Driven Methods for Knowledge Acquisition and Knowledge Discovery
本体驱动的知识获取和知识发现方法
- 批准号:
8714053 - 财政年份:2011
- 资助金额:
$ 31.27万 - 项目类别:
Ontology-Driven Methods for Knowledge Acquisition and Knowledge Discovery
本体驱动的知识获取和知识发现方法
- 批准号:
8326650 - 财政年份:2011
- 资助金额:
$ 31.27万 - 项目类别:
MODELING ROLES OF BIOACTIVE LIPIDS IN GENE EXPRESSION SYSTEMS
生物活性脂质在基因表达系统中的作用建模
- 批准号:
7959967 - 财政年份:2009
- 资助金额:
$ 31.27万 - 项目类别:
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