27 papers · ranked by Valyu relevance
Chongyang Chen, Jing Wang, Donghui Pan, Xinyu Wang + 6 more
Multi-omics usually refers to the crossover application of multiple high-throughput screening technologies represented by genomics, transcriptomics, single-cell transcriptomics, proteomics and metabolomics, spatial transcriptomics, and so on, which play a great role in promoting the study of human diseases. Most of the…
Hakim Benkirane, Yoann Pradat, Stefan Michiels, Paul-Henry Cournède
Recent advances in high-throughput sequencing technologies have enabled the extraction of multiple features that depict patient samples at diverse and complementary molecular levels. The generation of such data has led to new challenges in computational biology regarding the integration of highdimensional and…
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…
Cristian D. Gutierrez Reyes, Gerardo Alejo-Jacuinde, Benjamin Perez Sanchez, Jesus Chavez Reyes + 7 more
'Benjamin Perez Sanchez' 'Jesus Chavez Reyes' 'Sherifdeen Onigbinde' 'Damir Mogut' 'Irma Hernández-Jasso' 'Denisse Calderón-Vallejo' 'J. Luis Quintanar' 'Yehia Mechref' 'Madhav Bhatia'] Traditional methodologies often fall short in addressing the complexity of biological systems. In this regard, system biology omics…
Srinivasan Mani, Seema R. Lalani, Mohan Pammi
Precision medicine is a transformative healthcare model that utilizes an understanding of a person’s genome, environment, lifestyle, and interplay to deliver customized healthcare. Precision medicine has the potential to improve the health and productivity of the population, enhance patient trust and satisfaction in…
Tien-Thanh Bui, Rui Xie, Wei Zhang
The rapid accumulation of multi-omics data presents a valuable opportunity to advance our understanding of complex diseases and biological systems, driving the development of integrative computational methods. However, the complexity of biological processes—spanning multiple molecular layers and involving intricate…
Desheng Wu, Qiulian Fang
—Cancer survival prediction is important for developing personalized treatments and inducing disease-causing mechanisms. Multi-omics data integration is attracting widespread interest in cancer research for providing information for understanding cancer progression at multiple genetic levels. Many works, however, are…
Tiberiu Totu, Mattia Tomasoni, Hella Anna Bolck, Marija Buljan
Omics profiling has proven of great use for unbiased and comprehensive identification of key features that define biological phenotypes and underlie medical conditions. While each omics profile assists characterization of specific molecular components relevant for the studied phenotype, their joint evaluation can offer…
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…
Quan Zhao, Jiawen Du, Muqing Zhou, Xu-Wen Wang + 3 more
Advances in high-throughput sequencing technologies have revolutionized biomedical research by enabling the generation of diverse and extensive omics data, including genomic, transcriptomic, and epigenetic profiles, which are crucial for understanding complex diseases (, , , , , ). Integrating these multi-omics…
Yonatan Itai, Nimrod Rappoport, Ron Shamir
Integrative analysis of multi-omic datasets has proven to be extremely valuable in cancer research and precision medicine. However, obtaining multimodal data from the same samples is often difficult. Integrating multiple datasets of different omics remains a challenge, with only a few available algorithms developed to…
Ziming Jiang, Haoxuan Zhang, Yibo Gao, Yingli Sun
Multi-omics strategies, integrating genomics, transcriptomics, proteomics, and metabolomics, have revolutionized biomarker discovery and enabled novel applications in personalized oncology. Despite rapid technological developments, a comprehensive synthesis addressing integration strategies, analytical workflows, and…
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…
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…
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…
Rohit K. Tripathy, Zachary Frohock, Hong Wang, Gregory A. Cary + 3 more
The rapid growth of multi-omics datasets, in addition to the wealth of existing biological prior knowledge, necessitates the development of effective methods for their integration. Such methods are essential for building predictive models and identifying disease-related molecular markers. We propose a framework for…
Tim Downing, Nicos Angelopoulos
The continuing advances of omic technologies mean that it is now more tangible to measure the numerous features collectively reflecting the molecular properties of a sample. When multiple omic methods are used, statistical and computational approaches can exploit these large, connected profiles. Multi-omics is the…
Heming Zhang, S. Peter Goedegebuure, Li Ding, David DeNardo + 4 more
Multi-omic data-driven studies, characterizing complex disease signaling system from multiple levels, are at the forefront of precision medicine and healthcare. The integration and interpretation of multi-omic data are essential for identifying molecular targets and deciphering core signaling pathways of complex…
Annette Spooner, Mohammad Karimi Moridani, Azadeh Safarchi, Salim Maher + 3 more
carcinoma (HCC) using machine learning Authors: ['Annette Spooner' 'Mohammad Karimi Moridani' 'Azadeh Safarchi' 'Salim Maher' 'Fatemeh Vafaee' 'Amany Zekry' 'Arcot Sowmya'] The complementary information found in different modalities of patient data can aid in more accurate modelling of a patient's disease state and a…
Lei Xin, Caiyun Huang, Hao Li, Shihong Huang + 7 more
Challenges and Breakthroughs Authors: ['Lei Xin' 'Caiyun Huang' 'Hao Li' 'Shihong Huang' 'Yuling Feng' 'Zhenglun Kong' 'Zicheng Liu' 'Siyuan Li' 'Chang Yu' 'Fei Shen' 'Hao Tang'] With the rapid development of high-throughput sequencing platforms, an increasing number of omics technologies, such as genomics…
Katie Fan
Metabolic engineering in plants has emerged as a powerful approach to address global challenges in agriculture, nutrition, and sustainability. This comprehensive review explores cutting-edge strategies for manipulating primary and secondary metabolic pathways in plants, utilizing advanced genetic modification tools to…
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…
Denise Slenter, M. Kutmon, Chris T. Evelo, Egon L. Willighagen
Metabolomics data analysis for phenotype identification commonly reveals only a small set of biochemical markers, often containing overlapping metabolites for individual phenotypes. Differentiation between distinctive sample groups requires understanding the underlying causes of metabolic changes. However, combining…
David Buterez, Jon Paul Janet, Steven J. Kiddle, Pietro Liò
High-throughput screening (HTS), as one of the key techniques in drug discovery, is frequently used to identify promising drug candidates in a largely automated and cost-effective way. One of the necessary conditions for successful HTS campaigns is a large and diverse compound library, enabling hundreds of thousands of…
Eftychia Eva Kontou, Axel Walter, Oliver Alka, Julianus Pfeuffer + 5 more
Metabolomics experiments generate highly complex datasets, which are time and work-intensive, sometimes even error-prone if inspected manually. Therefore, new methods for automated, fast, reproducible, and accurate data processing and dereplication are required. Here, we present UmetaFlow, a computational workflow for…
Authors not listed
MALDI MS analysis of liquid biopsy combined with ML enables non-invasive disease screening and monitoring. Presented an open-source R-based workflow covering all steps from raw data preprocessing to predictive model evaluation. The pipeline is customizable, transparent, and validated on clinical plasma samples from…