2,134 research outputs found
Biologically Interpretable, Integrative Deep Learning for Cancer Survival Analysis
Identifying complex biological processes associated to patients\u27 survival time at the cellular and molecular level is critical not only for developing new treatments for patients but also for accurate survival prediction. However, highly nonlinear and high-dimension, low-sample size (HDLSS) data cause computational challenges in survival analysis. We developed a novel family of pathway-based, sparse deep neural networks (PASNet) for cancer survival analysis. PASNet family is a biologically interpretable neural network model where nodes in the network correspond to specific genes and pathways, while capturing nonlinear and hierarchical effects of biological pathways associated with certain clinical outcomes. Furthermore, integration of heterogeneous types of biological data from biospecimen holds promise of improving survival prediction and personalized therapies in cancer. Specifically, the integration of genomic data and histopathological images enhances survival predictions and personalized treatments in cancer study, while providing an in-depth understanding of genetic mechanisms and phenotypic patterns of cancer. Two proposed models will be introduced for integrating multi-omics data and pathological images, respectively. Each model in PASNet family was evaluated by comparing the performance of current cutting-edge models with The Cancer Genome Atlas (TCGA) cancer data. In the extensive experiments, PASNet family outperformed the benchmarking methods, and the outstanding performance was statistically assessed. More importantly, PASNet family showed the capability to interpret a multi-layered biological system. A number of biological literature in GBM supported the biological interpretation of the proposed models. The open-source software of PASNet family in PyTorch is publicly available at https://github.com/DataX-JieHao
Machine Learning and Integrative Analysis of Biomedical Big Data.
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues
Pathway-Based Multi-Omics Data Integration for Breast Cancer Diagnosis and Prognosis.
Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017
Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A review
Cancer remains one of the most challenging diseases to treat in the medical
field. Machine learning has enabled in-depth analysis of rich multi-omics
profiles and medical imaging for cancer diagnosis and prognosis. Despite these
advancements, machine learning models face challenges stemming from limited
labeled sample sizes, the intricate interplay of high-dimensionality data
types, the inherent heterogeneity observed among patients and within tumors,
and concerns about interpretability and consistency with existing biomedical
knowledge. One approach to surmount these challenges is to integrate biomedical
knowledge into data-driven models, which has proven potential to improve the
accuracy, robustness, and interpretability of model results. Here, we review
the state-of-the-art machine learning studies that adopted the fusion of
biomedical knowledge and data, termed knowledge-informed machine learning, for
cancer diagnosis and prognosis. Emphasizing the properties inherent in four
primary data types including clinical, imaging, molecular, and treatment data,
we highlight modeling considerations relevant to these contexts. We provide an
overview of diverse forms of knowledge representation and current strategies of
knowledge integration into machine learning pipelines with concrete examples.
We conclude the review article by discussing future directions to advance
cancer research through knowledge-informed machine learning.Comment: 41 pages, 4 figures, 2 table
A Deep Learning Approach for Multi-Omics Data Integration to Diagnose Early-Onset Colorectal Cancer
Colorectal cancer is one of the most common cancers and is a leading cause of death worldwide. It starts in the colon or the rectum, and they are often grouped together because they have many features in common. It has been noticed that colorectal cancer attacks young-onset patients who are less than 50 years of age in increasing rates lately. Rapid developments in omics technologies have led them to be highly regarded in the field of biomedical research for the early detection of cancer. Omics data revealed how different molecules and clinical features work together in the disease progression. However, Omics data sources are variants in nature and require careful preprocessing to be integrated. A convolutional neural network is a class of deep neural networks, commonly applied to analyze visual imagery. In this thesis, we propose a model that converts one-dimensional vectors of omics into RGB images to be integrated into the hidden layers of the convolutional neural network. The prediction model will allow all different omics to contribute to the decision making based on extracting the hidden interactions among these omics. These subsets of interacted omics can serve as potential biomarkers for young-onset colorectal cancer
AI-Enabled Lung Cancer Prognosis
Lung cancer is the primary cause of cancer-related mortality, claiming
approximately 1.79 million lives globally in 2020, with an estimated 2.21
million new cases diagnosed within the same period. Among these, Non-Small Cell
Lung Cancer (NSCLC) is the predominant subtype, characterized by a notably
bleak prognosis and low overall survival rate of approximately 25% over five
years across all disease stages. However, survival outcomes vary considerably
based on the stage at diagnosis and the therapeutic interventions administered.
Recent advancements in artificial intelligence (AI) have revolutionized the
landscape of lung cancer prognosis. AI-driven methodologies, including machine
learning and deep learning algorithms, have shown promise in enhancing survival
prediction accuracy by efficiently analyzing complex multi-omics data and
integrating diverse clinical variables. By leveraging AI techniques, clinicians
can harness comprehensive prognostic insights to tailor personalized treatment
strategies, ultimately improving patient outcomes in NSCLC. Overviewing
AI-driven data processing can significantly help bolster the understanding and
provide better directions for using such systems.Comment: This is the author's version of a book chapter entitled: "Cancer
Research: An Interdisciplinary Approach", Springe
Investigating the relevance of major signaling pathways in cancer survival using a biologically meaningful deep learning model
BACKGROUND: Survival analysis is an important part of cancer studies. In addition to the existing Cox proportional hazards model, deep learning models have recently been proposed in survival prediction, which directly integrates multi-omics data of a large number of genes using the fully connected dense deep neural network layers, which are hard to interpret. On the other hand, cancer signaling pathways are important and interpretable concepts that define the signaling cascades regulating cancer development and drug resistance. Thus, it is important to investigate potential associations between patient survival and individual signaling pathways, which can help domain experts to understand deep learning models making specific predictions.
RESULTS: In this exploratory study, we proposed to investigate the relevance and influence of a set of core cancer signaling pathways in the survival analysis of cancer patients. Specifically, we built a simplified and partially biologically meaningful deep neural network, DeepSigSurvNet, for survival prediction. In the model, the gene expression and copy number data of 1967 genes from 46 major signaling pathways were integrated in the model. We applied the model to four types of cancer and investigated the influence of the 46 signaling pathways in the cancers. Interestingly, the interpretable analysis identified the distinct patterns of these signaling pathways, which are helpful in understanding the relevance of signaling pathways in terms of their application to the prediction of cancer patients\u27 survival time. These highly relevant signaling pathways, when combined with other essential signaling pathways inhibitors, can be novel targets for drug and drug combination prediction to improve cancer patients\u27 survival time.
CONCLUSION: The proposed DeepSigSurvNet model can facilitate the understanding of the implications of signaling pathways on cancer patients\u27 survival by integrating multi-omics data and clinical factors
Survival-Related Clustering of Cancer Patients by Integrating Clinical and Biological Datasets
Subtype-based treatments and drug therapies are essential aspects to be considered in cancer patients\u27 clinical trials to provide appropriate personalized therapies. With the advancement of the next-generation sequencing technology, several computational models, integrating genomic and transcriptomic datasets (i.e., multi-omics) in the prediction of subtype-based classification in cancer patients, were emerged. However, integration of the prognostic features from the clinical data, related to survival risks with the multi-omics datasets in the prediction of different subtypes, is limited and an important research area to be explored. In this study, we proposed a data integration pipeline with the prognostic features from the clinical data and multi-omics datasets to predict the survival-risk-based subtypes in Kidney Renal Clear Cell Carcinoma (KIRC) patients from The Cancer Genome Atlas (TCGA) database. Firstly, we applied an unsupervised clustering algorithm on KIRC patients and clustered them into two survival-risk-based subgroups, i.e., subtypes. Then, using the clustering-based subtype labels as class labels for cancer patients, we trained a supervised classification model to determine the class label of un-labeled patients.In our clustering step, we applied multivariate Cox Proportional Hazard (Cox-PH) model to select the survival-related prognostically significant features (p-value \u3c 0.05) from the patients’ multivariate clinical data. Then, we used the Silhouette Coefficient to determine the optimal number (k) of the clusters. In our classification step, we integrated high dimensional multi-omics datasets with three different data modalities (such as gene expression, microRNA expression, and DNA methylation). We utilized a dimension-reduction approach, followed by a univariate Cox-PH for each reduced data modality with patients’ survival status. Then, we selected the survival-related reduced-omics-features in our classification model. In this step, we applied a supervised classification method with 10-fold cross-validation to check our survival-based subtype prediction accuracy. We tested multiple machine learning and deep learning algorithms in different steps of the pipeline for clustering (K-means, K-modes and, Gaussian mixture model), dimension-reduction (Denoising Autoencoder and Principal Component Analysis) and classification (Support Vector Machine and Random Forest) purposes. We proposed an optimized model with the highest survival-specific-subtype classification accuracy as the final model
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