26 papers · ranked by Valyu relevance
Mano Joseph Mathew, Joyal Mathew, Ripsy Merrin Chacko, Jagadeesh Bayry + 1 more
Computational multiomics methods are based on machine learning methods, and are primarily used for classifying patients into subtypes, discovering novel biomarkers, drug repurposing, and advancing precision medicine. Advances in high-throughput technologies have enabled comprehensive profiling of multiple molecular…
Authors not listed
Mass spectrometry (MS) is a cornerstone technology in modern molecular biology, powering diverse applications across proteomics, metabolomics, lipidomics, glycomics, and beyond. As the field continues to evolve, rapid advancements in instrumentation, acquisition strategies, machine learning, and scalable computing have…
Eléonore Schneegans, Nurun Fancy, Michael Thomas, Nanet Willumsen + 2 more
The Omix pipeline offers an integration and analysis framework for multiomics intended to preprocess, analyse, and visualise multimodal data flexibly to address various research questions. From biomarker discovery and patient stratification to the investigation of complex biological processes, Omix empowers researchers…
Yunqing Luo, Chengjun Zhao, Fei Chen
Multiomics research is a transformative approach in the biological sciences that integrates data from genomics, transcriptomics, proteomics, metabolomics, and other omics technologies to provide a comprehensive understanding of biological systems. This review elucidates the fundamental principles of multiomics…
Yuanting Zheng, Yaqing Liu, Jingcheng Yang, Lianhua Dong + 52 more
Multiomics profiling is a powerful tool to characterize the same samples with complementary features orchestrating the genome, epigenome, transcriptome, proteome, and metabolome. However, the lack of ground truth hampers the objective assessment of and subsequent choice from a plethora of measurement and computational…
Gen Li, Eric F. Lock
With advancements in technology and the decreasing cost of data acquisition, high-throughput omics data have become increasingly prevalent in biomedical research. These data are often collected across multiple omics modalities at different molecular levels, offering a comprehensive perspective on underlying biological…
David Hirst, Morgane Térézol, Laura Cantini, Paul Villoutreix + 2 more
Joint matrix factorization is a popular method for extracting lower dimensional representations of multi-omics data. It disentangles underlying mixtures of biological signals, facilitating efficient sample clustering, disease subtyping, or biomarker identification, for instance. However, when a multi-omics dataset is…
Jeffrey Niu, Carlos Vasquez-Rios, Jiarui Ding
Single-cell multiomics technologies generate paired measurements of different cellular modalities, such as gene expression and chromatin accessibility. However, multiomics technologies are more expensive than their unimodal counterparts, resulting in smaller and fewer available multiomics datasets. Here, we present…
Himel Mallick, Anupreet Porwal, Satabdi Saha, Vladimir Svetnik + 1 more
With the growing commonality of multi-omics datasets, there is now increasing evidence that integrated omics profiles lead to the more efficient discovery of clinically actionable biomarkers that enable better disease outcome prediction and patient stratification. Several methods exist to perform host phenotype…
Yuan Zhou, Pei Geng, Shan Zhang, Feifei Xiao + 3 more
'Li Chen' '' 'Qing Lu'] Title: Abstract With rapidly evolving high-throughput technologies and consistently decreasing costs, collecting multimodal omics data in large-scale studies has become feasible. Although studying multiomics provides a new comprehensive approach in understanding the complex biological mechanisms…
Ana R. Baião, Zhaoxiang Cai, Rebecca C. Poulos, Phillip J. Robinson + 4 more
classical statistical to deep generative approaches Authors: ['Ana R. Baião' 'Zhaoxiang Cai' 'Rebecca C. Poulos' 'Phillip J. Robinson' 'Roger R. Reddel' 'Qing Zhong' 'Susana Vinga' 'Emanuel Gonçalves'] The rapid advancement of high-throughput sequencing and other assay technologies has resulted in the generation of…
Y-h. Taguchi
Multiomics data analysis is the central issue of genomics science. In spite of that, there are not well defined methods that can integrate multomics data sets, which are formatted as matrices with different sizes. In this paper, I propose the usage of tensor decomposition based unsupervised feature extraction as a data…
Sina Tabakhi, Mohammod N. I. Suvon, Pegah Ahadian, Haiping Lu
—With advanced imaging, sequencing, and profiling technologies, multiple omics data become increasingly available and hold promises for many healthcare applications such as cancer diagnosis and treatment. Multimodal learning for integrative multi-omics analysis can help researchers and practitioners gain deep insights…
Szilard Voros, Michael R. Barnes, David Watson, Wess Boatwright + 4 more
Evidence has shown that lipoprotein(a) (Lp[a]) is an independent, causal, genetic risk factor for cardiovascular disease (CVD) that promotes the progression of high-risk, vulnerable atherosclerotic plaque phenotypes. Systems biology integrates multiomics datasets to study linear and nonlinear relationships to enhance…
Liangrui Pan, Dazheng Liu, Zhichao Feng, Wenjuan Liu + 1 more
'Shaoliang Peng'] Abstract—Due to the high heterogeneity and clinical characteristics of cancer, there are significant differences in multi-omic data and clinical characteristics among different cancer subtypes. Therefore, accurate classification of cancer subtypes can help doctors choose the most appropriate treatment…
Shan Cong, Zhiling Sang, Hongwei Liu, Haoran Luo + 4 more
Classification Authors: ['Shan Cong' 'Zhiling Sang' 'Hongwei Liu' 'Haoran Luo' 'Xin Wang' 'Hong Liang' 'Jie Hao' 'Xiaohui Yao'] Abstract—The distinct characteristics of multiomics data, including complex interactions within and across biological layers and disease heterogeneity (e.g., heterogeneity in etiology and…
Sina Tabakhi, Charlotte Vandermeulen, Ian Sudbery, Haiping Lu
integration Authors: ['Sina Tabakhi' 'Charlotte Vandermeulen' 'Ian Sudbery' 'Haiping Lu'] The increase in high-dimensional multiomics data demands advanced integration models to capture the complexity of human diseases. Graph-based deep learning integration models, despite their promise, struggle with small patient…
Daisy Yi Ding, Xiaotao Shen, M Snyder, Robert Tibshirani
Multiomics data fusion integrates diverse data modalities, ranging from transcriptomics to proteomics, to gain a comprehensive understanding of biological systems and enhance predictions on outcomes of interest related to disease phenotypes and treatment responses. Cooperative learning, a recently proposed method…
Dariusz Boroń, Nikola Zmarzły, Magdalena Wierzbik-Strońska, Joanna Rosińczuk + 4 more
'Joanna Rosińczuk' 'Paweł Mieszczański' 'Beniamin Oskar Grabarek' 'Yashwanth Subbannayya' 'Sanjiban Chakrabarty'] Endometrial cancer is the most common gynecological cancers in developed countries. Many of the mechanisms involved in its initiation and progression remain unclear. Analysis providing comprehensive data on…
Minzhang Zheng, Carlo Piermarocchi, George I. Mias
Longitudinal deep multi-omics profiling, which combines biomolecular, physiological, environmental and clinical measures data, shows great promise for precision health. However, integrating and understanding the complexity of such data remains a big challenge. Here we propose a bottom-up framework starting from…
Minzhang Zheng, Carlo Piermarocchi, George I. Mias
Longitudinal deep multiomics profiling, which combines biomolecular, physiological, environmental and clinical measures data, shows great promise for precision health. However, integrating and understanding the complexity of such data remains a big challenge. Here we utilize an individual-focused bottom-up approach…
Authors not listed
Untargeted metabolomics is a powerful approach for exploring the chemical diversity and dynamics of biological systems. However, the types of questions that can be addressed depend not only on experimental design but also on the data processing and analysis workflows employed, many of which require advanced…
Authors not listed
Desorption Electrospray Ionization Mass Spectrometry Imaging (DESI-MSI) is a powerful technique for molecular analysis of surfaces; however, its application of single cell studies has not been previously published. In the current work, a commercial DESI setup (DESI XS) coupled to a mass spectrometer was used to analyze…
Authors not listed
Molecular mechanisms governing initiation steps of the assembly of thousands of endogenous multi-protein complexes (EMCs) remain incompletely understood. Here, multiple lines of observations are reported reflecting the biological functions-aligned initiation sequence of hybrid assembly pathways (HAPs) of EMCs. HAPs…
Authors not listed
Microfractionation is a prominent alternative for the discovery and characterization of bioactive natural products in complex extracts (e.g., plant, fungal, or microbial); however, the method faces limitations as it requires arduous scale-up for complete characterization. Furthermore, the integration of…
Jose L. Medina-Franco, Edgar López-López, Johny R. Rodríguez-Pérez, Héctor F. Cortés-Hernández + 1 more
In Chemoinformatics, as in many other computational-related disciplines, it is a common practice to identify the “single best” approach or methodology, for instance, identify the best fingerprint representation, the best single virtual screening approach or protocol, the optimal representation of the chemical space…