25 papers · ranked by Valyu relevance
Farhana R. Pinu, David J. Beale, Amy M. Paten, Konstantinos Kouremenos + 3 more
'Konstantinos Kouremenos' 'Sanjay Swarup' 'Horst J. Schirra' 'David Wishart'] The use of multiple omics techniques (i.e., genomics, transcriptomics, proteomics, and metabolomics) is becoming increasingly popular in all facets of life science. Omics techniques provide a more holistic molecular perspective of studied…
Michal Krassowski, Vivek Das, Sangram K. Sahu, Biswapriya B. Misra
Multi-omics, variously called integrated omics, pan-omics, and trans-omics, aims to combine two or more omics data sets to aid in data analysis, visualization and interpretation to determine the mechanism of a biological process. Multi-omics efforts have taken center stage in biomedical research leading to the…
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…
Danila Bredikhin, Ilia Kats, Oliver Stegle
Advances in multi-omics technologies have led to an explosion of multimodal datasets to address questions ranging from basic biology to translation. While these rich data provide major opportunities for discovery, they also come with data management and analysis challenges, thus motivating the development of tailored…
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…
Heming Zhang, Dekang Cao, Zirui Chen, Xiuyuan Zhang + 7 more
Multi-omic data, i.e., genomics, epigenomics, transcriptomics, proteomics, characterize cellular complex signaling systems from multi-level and multi-view and provide a holistic view of complex cellular signaling pathways. However, it remains challenging to integrate and interpret multi-omics data. Graph neural network…
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…
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…
David Gomez-Cabrero, Sonia Tarazona, Isabel Ferreirós-Vidal, Ricardo N. Ramirez + 26 more
Multi-omics approaches use a diversity of high-throughput technologies to profile the different molecular layers of living cells. Ideally, the integration of this information should result in comprehensive systems models of cellular physiology and regulation. However, most multi-omics projects still include a limited…
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…
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…
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…
Laura Cantini, Pooya Zakeri, Celine Hernandez, Aurelien Naldi + 3 more
High-dimensional multi-omics data are now standard in biology. They can greatly enhance our understanding of biological systems when effectively integrated. To achieve this multi-omics data integration, Joint Dimensionality Reduction (jDR) methods are among the most efficient approaches. However, several jDR methods…
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…
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…
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…
Ahmad Naim Hussein, Mukesh Prasad, Ali Braytee
Advancements in high-throughput technologies have led to a shift from traditional hypothesis-driven methodologies to data-driven approaches. Multi-omics refers to the integrative analysis of data derived from multiple 'omes', such as genomics, proteomics, transcriptomics, metabolomics, and microbiomics. This approach…
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…
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…
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…
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Ensuring the trustworthiness of machine learning (ML) models in high-stake applications is crucial. One such application is predicting anti-cancer drug sensitivity, where ML models are built with the final goal of integrating them into treatment recommendation systems for personalized medicine. Here, we propose a…
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
Integrating machine learning (ML) into drug discovery has ushered in a new era of innovation, dramatically enhancing the efficiency and precision of identifying and developing new therapeutics. This review provides a comprehensive analysis of the current applications of machine learning in drug discovery, focusing on…
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One Health seeks to integrate and balance the health of humans, animals, and environmental systems. These three spheres are intricately interconnected through microbiomes, which are universally present and exchange microbes and genes, influencing not only human and animal health but also key environmental…
Authors not listed
Scientific discovery relies on innovative software as much as experimental methods, especially in proteomics, where computational tools are essential for mass spectrometer setup, data analysis, and interpretation. Since the introduction of SEQUEST, proteomics software has grown into a complex ecosystem of algorithms…