Similarity Network Fusion Method Research Articles

Abstract 7354: Proteogenomic and metabolomic characterization of acute myeloid leukemia

Abstract Acute myeloid leukemia (AML) is a blood malignancy of poor prognosis with marked heterogeneity. To elucidate the underlying mechanisms that drive AML as part of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) effort, we performed comprehensive genomics, transcriptomics, proteomics including multiple post-translational modifications (phosphorylation, acetylation, and glycosylation), metabolomics, and lipidomics characterization of 173 treatment-naïve AML patients. Applying the similarity network fusion method on both transcriptomics and proteomics data, we identified 8 proteogenomic clusters. These clusters recapitulate specific recurrent mutations and established clinical subtypes available within the cohort. We used single-cell RNAseq data as a reference to perform immune component analysis for collected bulk samples. The result reveals that our proteogenomic clustering also captures the variations of AML differentiation hierarchies including CD14+ monocyte-like and GMP-like AML. To assess the complex disease nature of AML, we performed functional analysis for each cluster to reveal interplay between multiple genomic aberrations such as NPM1, FLT3-ITD, DNMT3A mutations, complex chromosomal alterations, and the leukemia cell differentiation. Importantly, the multi-omics analysis performed not only connects previously identified molecular drivers and cell differentiation variations within AML, but also links them with observed cancer metabolomic reprogramming with their drug response implications. Moreover, our study also identified site-specific post-translational modifications previously not known in AML, highlighting the valuable insights and clinical relevance of these newly identified clusters. Citation Format: Alec Chu, Yi Hsiao, Yamei Deng, Chenwei Wang, Jennifer Kyle, Yongchao Dou, James C. Pino, Camilo Posso, Leanne Henry, Lijun Chen, Ginny Xiaohe Li, Tung-Shing Mamie Lih, Yifat Geffen, Fengchao Yu, Li Ding, Gil Omenn, Chandan Kumar, Saravana M. Dhanasekaran, Elie Traer, Jeffrey W. Tyner, Hui Zhang, Tao Liu, Sara Gosline, Bing Zhang, Arul Chinnaiyan, Alexey I. Nesvizhskii, Marcin Cieslik, The CPTAC Consortium. Proteogenomic and metabolomic characterization of acute myeloid leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7354.

Abstract P27: Proteogenomic and metabolomic analysis of acute myeloid leukemia reveals molecular and functional underpinnings of cellular and clinical phenotypes

Abstract Acute myeloid leukemia (AML) is a blood malignancy of poor prognosis with marked heterogeneity. To elucidate the underlying mechanisms that drive AML as part of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) effort, we performed large scale comprehensive genomics, transcriptomics, proteomics including multiple post-translational modifications (phosphorylation, acetylation, and glycosylation), metabolomics, and lipidomics characterization of 173 treatment-naïve AML patients. Applying the similarity network fusion method on both transcriptomics and proteomics data, we identified 8 proteogenomic clusters. These clusters recapitulate specific recurrent mutations, fusions, structural variants, and established clinical subtypes available within the cohort, as well as reveal new cluster-specific phenotypes within other multi-omic datasets. We used single-cell RNAseq data as a reference to perform immune component analysis for collected bulk samples. The result reveals that our proteogenomic clustering also captures the variations of AML differentiation hierarchies including CD14+ monocyte-like and GMP-like AML. To assess the complex disease nature of AML, we performed functional analysis for each cluster to reveal interplay between multiple genomic aberrations such as NPM1, FLT3-ITD, DNMT3A mutations, complex chromosomal alterations, and the leukemia cell differentiation. Additionally, the multi-omics analysis performed not only connects previously identified molecular drivers and cell differentiation variations within AML, but also links them with observed cancer metabolomic reprogramming alongside differences in MTOR signaling, MYC activities, mitochondrial activities, and drug responses. Moreover, our study also identified site-specific post-translational modifications previously not known in AML, highlighting the valuable insights and clinical relevance of these newly identified clusters. Citation Format: Shih-Chun Alec Chu, Yi Hsiao, Yamei Deng, Chenwei Wang, Jennifer Kyle, Yongchao Dou, James Pino, Camilo Posso, Leanne Henry, Ginny Li, Li Ding, Lijun Chen, Mamie Lih, Yifat Geffen, Gilbert Omenn, Chandan Kumar, Saravana Dhanasekaran, Fengchao Yu, Elie Traer, Jeffrey W. Tyner, Hui Zhang, Tao Liu, Sara Gosline, Bing Zhang, Arul Chinnaiyan, Alexey I Nesvizhskii, Marcin Cieslik. Proteogenomic and metabolomic analysis of acute myeloid leukemia reveals molecular and functional underpinnings of cellular and clinical phenotypes [abstract]. In: Proceedings of the Blood Cancer Discovery Symposium; 2024 Mar 4-6; Boston, MA. Philadelphia (PA): AACR; Blood Cancer Discov 2024;5(2_Suppl):Abstract nr P27.

A novel efficient drug repurposing framework through drug-disease association data integration using convolutional neural networks

Drug repurposing is an exciting field of research toward recognizing a new FDA-approved drug target for the treatment of a specific disease. It has received extensive attention regarding the tedious, time-consuming, and highly expensive procedure with a high risk of failure of new drug discovery. Data-driven approaches are an important class of methods that have been introduced for identifying a candidate drug against a target disease. In the present study, a model is proposed illustrating the integration of drug-disease association data for drug repurposing using a deep neural network. The model, so-called IDDI-DNN, primarily constructs similarity matrices for drug-related properties (three matrices), disease-related properties (two matrices), and drug-disease associations (one matrix). Then, these matrices are integrated into a unique matrix through a two-step procedure benefiting from the similarity network fusion method. The model uses a constructed matrix for the prediction of novel and unknown drug-disease associations through a convolutional neural network. The proposed model was evaluated comparatively using two different datasets including the gold standard dataset and DNdataset. Comparing the results of evaluations indicates that IDDI-DNN outperforms other state-of-the-art methods concerning prediction accuracy.

Predicting Drug-Disease Associations Through Similarity Network Fusion and Multi-View Feature Projection Representation.

Predicting drug-disease associations (DDAs) through computational methods has become a prevalent trend in drug development because of their high efficiency and low cost. Existing methods usually focus on constructing heterogeneous networks by collecting multiple data resources to improve prediction ability. However, potential association possibilities of numerous unconfirmed drug-related or disease-related pairs are not sufficiently considered. In this article, we propose a novel computational model to predict new DDAs. First, a heterogeneous network is constructed, including four types of nodes (drugs, targets, cell lines, diseases) and three types of edges (associations, association scores, similarities). Second, an updating and merging-based similarity network fusion method, termed UM-SF, is presented to fuse various similarity networks with diverse weights. Finally, an intermediate layer-mediated multi-view feature projection representation method, termed IM-FP, is proposed to calculate the predicted DDA scores. This method uses multiple association scores to construct multi-view drug features, then projects them into disease space through the intermediate layer, where an intermediate layer similarity constraint is designed to learn the projection matrices. Results of comparative experiments reveal the effectiveness of our innovations. Comparisons with other state-of-the-art models by the 10-fold cross-validation experiment indicate our model's advantage on AUROC and AUPR metrics. Moreover, our proposed model successfully predicted 107 novel high-ranked DDAs.

Robust 3D brain segmentation in magnetic resonance image with weighted feature fusion

Accurate tissue segmentation in brain magnetic resonance image is of paramount importance for brain reconstruction and analysis. Supervoxels analysis enables relatively robust segmentation of brain tissues, by assigning the supervoxels to tissue clusters according to the inherent information of multiple features captured from the supervoxels. In this popular framework, difficulties are encountered in clustering imposed by the insufficient selection, deficient utilisation of features, and rough supervoxel boundaries due to the influence of noise. To address these aforementioned challenges, the authors propose a weighted feature fusion clustering segmentation method for dividing supervoxels into three tissues on the basis of a robust feature fused similarity matrix. To comprehensively capture the similarities of features among the supervoxels, we construct three complementary similarity matrices on the basis of multiple features from three aspects, that is, appearance, shape, and space location. Next, the authors propose a weighted similarity network fusion method to discriminatively fuse the three similarity matrices into one single matrix. This novel fusion process cannot only extract the common and complementary information of features, but also automatically adjust the weight of similarities according to its reliability. Finally, we achieve initial segmentation results by performing spectral clustering on the fused similarity matrix. To optimise the segmented boundaries affected by noise in the initial segmentation results, the authors construct a voxel-wise diffusion energy function by considering the intensity information of neighbouring voxels. Experiments on two public brain magnetic resonance image datasets demonstrate the robustness of the proposed approach.

A fusion framework to extract typical treatment patterns from electronic medical records.

Electronic Medical Records (EMRs) contain temporal and heterogeneous doctor order information that can be used for treatment pattern discovery. Our objective is to identify "right patient", "right drug", "right dose", "right route", and "right time" from doctor order information. We propose a fusion framework to extract typical treatment patterns based on multi-view similarity Network Fusion (SNF) method. The multi-view SNF method involves three similarity measures: content-view similarity, sequence-view similarity and duration-view similarity. An EMR dataset and two metrics were utilized to evaluate the performance and to extract typical treatment patterns. Experimental results on a real-world EMR dataset show that the multi-view similarity network fusion method outperforms all the single-view similarity measures and also outperforms the existing similarity measure methods. Furthermore, we extract and visualize typical treatment patterns by clustering analysis. The extracted typical treatment patterns by combining doctor order content, sequence, and duration views can provide data-driven guidelines for artificial intelligence in medicine and help clinicians make better decisions in clinical practice.

Exploring Gyral Patterns of Infant Cortical Folding based on Multi-view Curvature Information.

The human cortical folding is intriguingly complex in its variability and regularity across individuals. Exploring the principal patterns of cortical folding is of great importance for neuroimaging research. The term-born neonates with minimum exposure to the complicated environments are the ideal candidates to mine the postnatal origins of principal cortical folding patterns. In this work, we propose a novel framework to study the gyral patterns of neonatal cortical folding. Specifically, first, we leverage multi-view curvature-derived features to comprehensively characterize the complex and multi-scale nature of cortical folding. Second, for each feature, we build a dissimilarity matrix for measuring the difference of cortical folding between any pair of subjects. Then, we convert these dissimilarity matrices as similarity matrices, and nonlinearly fuse them into a single matrix via a similarity network fusion method. Finally, we apply a hierarchical affinity propagation clustering approach to group subjects into several clusters based on the fused similarity matrix. The proposed framework is generic and can be applied to any cortical region, or even the whole cortical surface. Experiments are carried out on a large dataset with 600+ term-born neonates to mine the principal folding patterns of three representative gyral regions.

Similarity fusion scheme for cover song identification

To take advantage of the complementarity of different features in representing the common facets shared among cover versions, the similarity network fusion strategy in biological field is adopted to fuse the cochlear pitch class profile (PCP), beat-synchronous chroma and harmonic PCP feature-based similarity networks for cover song identification. For a music collection, first, the similarity network based on each feature and corresponding similarity measure is generated; then, the similarity network fusion method is used to fuse these similarity networks to create the fused similarity network; finally, the similarity scores in fused similarity network are used to train a classifier, which can then be used to identify whether the corresponding tracks are reference/cover or reference/non-cover pair according to the input similarity value. Experimental results demonstrated that the proposed scheme not only realised general classification with high accuracy, but also outperformed the state-of-the-art schemes in high-score evaluation and defining cover versions community.