Results 61 to 70 of about 79,155 (276)

A fair experimental comparison of neural network architectures for latent representations of multi-omics for drug response prediction

BMC Bioinformatics, 2023
Background Recent years have seen a surge of novel neural network architectures for the integration of multi-omics data for prediction. Most of the architectures include either encoders alone or encoders and decoders, i.e., autoencoders of various sorts,
Tony Hauptmann, Stefan Kramer
doaj   +1 more source

Multi-omics analysis: Paving the path toward achieving precision medicine in cancer treatment and immuno-oncology

Frontiers in Molecular Biosciences, 2022
The acceleration of large-scale sequencing and the progress in high-throughput computational analyses, defined as omics, was a hallmark for the comprehension of the biological processes in human health and diseases.
Virgile Raufaste-Cazavieille, Raoul Santiago, Raoul Santiago, Arnaud Droit   +3 more
doaj   +1 more source

Patient‐specific pharmacogenomics demonstrates xCT as predictive therapeutic target in colon cancer with possible implications in tumor connectivity

Molecular Oncology, EarlyView.
This study integrates transcriptomic profiling of matched tumor and healthy tissues from 32 colorectal cancer patients with functional validation in patient‐derived organoids, revealing dysregulated metabolic programs driven by overexpressed xCT (SLC7A11) and SLC3A2, identifying an oncogenic cystine/glutamate transporter signature linked to ...
Marco Strecker, Keren Zohar, Martin Böttcher, Thomas Wartmann, Henry Freudenstein, Maximilian Doelling, Mihailo Andric, Wenjie Shi, Or Kakhlon, Katrin Hippe, Beatrix Jahnke, Dimitrios Mougiakakos, Franziska Baenke, Daniel Stange, Roland S. Croner, Michal Linial, Ulf D. Kahlert   +16 more
wiley   +1 more source

Unsupervised neural network for single cell Multi-omics INTegration (UMINT): an application to health and disease

Frontiers in Molecular Biosciences, 2023
Multi-omics studies have enabled us to understand the mechanistic drivers behind complex disease states and progressions, thereby providing novel and actionable biological insights into health status. However, integrating data from multiple modalities is
Chayan Maitra, Dibyendu B. Seal, Vivek Das, Rajat K. De   +3 more
doaj   +1 more source

Integrated Multi-Omics Maps of Lower-Grade Gliomas

Cancers, 2022
Multi-omics high-throughput technologies produce data sets which are not restricted to only one but consist of multiple omics modalities, often as patient-matched tumour specimens. The integrative analysis of these omics modalities is essential to obtain a holistic view on the otherwise fragmented information hidden in this data.
Hans Binder, Maria Schmidt, Lydia Hopp, Suren Davitavyan, Arsen Arakelyan, Henry Loeffler-Wirth   +5 more
openaire   +3 more sources

Developing evidence‐based, cost‐effective P4 cancer medicine for driving innovation in prevention, therapeutics, patient care and reducing healthcare inequalities

Molecular Oncology, EarlyView.
The cancer problem is increasing globally with projections up to the year 2050 showing unfavourable outcomes in terms of incidence and cancer‐related deaths. The main challenges are prevention, improved therapeutics resulting in increased cure rates and enhanced health‐related quality of life.
Ulrik Ringborg, Joachim von Braun, Julio Celis, Anton Berns, Michael Baumann, Tit Albreht, Nancy Abou‐Zeid, Vanderlei Bagnato, Christian Brandts, Chien‐Jen Chen, Massimiliano di Pietro, Manjit Dosanjh, Thomas Dubois, Alexander Eggermont, Angelika Eggert, Ingemar Ernberg, Sara Faithfull, Johannes Förner, Stefan Fröhling, Manuel Heitor, Leroy Hood, Wei Jiang, Bengt Jönsson, Ravi Kannan, Maria Leptin, Su Li, Peter Lindgren, Douglas Lowy, Jun Ma, Alex Markham, Péter Nagy, Simon Oberst, M. Iqbal Parker, Danielle Rodin, Kevin Ryan, Joachim Schüz, Richard Sullivan, Josep Tabernero, Peter Turkson, Oliver Várhelyi, Harold Varmus, Chijie Wang, Elisabete Weiderpass, Nils Wilking   +43 more
wiley   +1 more source

MoAGL-SA: a multi-omics adaptive integration method with graph learning and self attention for cancer subtype classification

BMC Bioinformatics
Background The integration of multi-omics data through deep learning has greatly improved cancer subtype classification, particularly in feature learning and multi-omics data integration.
Lei Cheng, Qian Huang, Zhengqun Zhu, Yanan Li, Shuguang Ge, Longzhen Zhang, Ping Gong   +6 more
doaj   +1 more source

Unconventional machine learning of genome-wide human cancer data

, 2020
Recent advances in high-throughput genomic technologies coupled with exponential increases in computer processing and memory have allowed us to interrogate the complex aberrant molecular underpinnings of human disease from a genome-wide perspective ...
Bajaj, Sweta R., Chittenden, Thomas W., Cilfone, Nicholas, Gamel, Omar E., Gujja, Sharvari, Gulcher, Jeffrey R., Li, Richard Y., Lidar, Daniel A.   +7 more
core   +1 more source

Mind-life continuity: a qualitative study of conscious experience [PDF]

, 2017
There are two fundamental models to understanding the phenomenon of natural life. One is thecomputational model, which is based on the symbolic thinking paradigm. The other is the biologicalorganism model.
Hipólito, Inês, Martins, J.
core   +1 more source

Phenotypic and genotypic characterization of single circulating tumor cells in the follow‐up of high‐grade serous ovarian cancer

Molecular Oncology, EarlyView.
Single circulating tumor cells (sCTCs) from high‐grade serous ovarian cancer patients were enriched, imaged, and genomically profiled using WGA and NGS at different time points during treatment. sCTCs revealed enrichment of alterations in Chromosomes 2, 7, and 12 as well as persistent or emerging oncogenic CNAs, supporting sCTC identity.
Carolin Salmon, Rui P. L. Neves, Nikolas H. Stoecklein, Sven‐Thorsten Liffers, Jens Siveke, Jan D. Kuhlmann, Pauline Wimberger, Paul Buderath, Rainer Kimmig, Sabine Kasimir‐Bauer   +9 more
wiley   +1 more source