AIMC Topic: Neoplasm Proteins

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Personalised Medicine for Colorectal Cancer Using Mechanism-Based Machine Learning Models.

International journal of molecular sciences Sep 15, 2021
Gaining insight into the mechanisms of signal transduction networks (STNs) by using critical features from patient-specific mathematical models can improve patient stratification and help to identify potential drug targets. To achieve this, these mod...
Humans Machine Learning Precision Medicine Colorectal Neoplasms Progression-Free Survival Linear Models Kaplan-Meier Estimate Wnt Signaling Pathway Gene Expression Regulation, Neoplastic Proteomics Neoplasm Proteins Models, Biological
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The structure-based cancer-related single amino acid variation prediction.

Scientific reports Jun 30, 2021
Single amino acid variation (SAV) is an amino acid substitution of the protein sequence that can potentially influence the entire protein structure or function, as well as its binding affinity. Protein destabilization is related to diseases, includin...
Neoplasm Proteins Models, Biological Amino Acid Substitution Humans Neoplasms Support Vector Machine
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Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance.

Nature communications May 11, 2021
Resistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitivity is not currently implemented clinically due to insufficient accuracy of existing machine learnin...
Machine Learning Neoplasms Transcriptome Cell Line, Tumor Humans Neoplasm Proteins Radiation, Ionizing Genome, Human Metabolic Networks and Pathways Radiation Tolerance Survival Analysis Atlases as Topic Gene Expression Regulation, Neoplastic Datasets as Topic Treatment Outcome Databases, Genetic
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Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey.

International journal of molecular sciences Apr 22, 2021
Artificial Intelligence is providing astonishing results, with medicine being one of its favourite playgrounds. Machine Learning and, in particular, Deep Neural Networks are behind this revolution. Among the most challenging targets of interest in me...
Machine Learning Precision Medicine Neoplasms Antineoplastic Agents Neoplasm Proteins Molecular Targeted Therapy Humans Neural Networks, Computer
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Machine learning-based investigation of the cancer protein secretory pathway.

PLoS computational biology Apr 5, 2021
Deregulation of the protein secretory pathway (PSP) is linked to many hallmarks of cancer, such as promoting tissue invasion and modulating cell-cell signaling. The collection of secreted proteins processed by the PSP, known as the secretome, is ofte...
Neoplasms Cell Line, Tumor Neoplasm Proteins Mutation Machine Learning Algorithms Biomarkers, Tumor Cell Transformation, Neoplastic Genes, p53 Secretory Pathway Humans
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Systematic characterization of mutations altering protein degradation in human cancers.

Molecular cell Feb 9, 2021
The ubiquitin-proteasome system (UPS) is the primary route for selective protein degradation in human cells. The UPS is an attractive target for novel cancer therapies, but the precise UPS genes and substrates important for cancer growth are incomple...
HEK293 Cells Proteolysis Models, Genetic Neoplasm Proteins Mutation Humans Neoplasms Deep Learning Cell Line, Tumor
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Predicting potential residues associated with lung cancer using deep neural network.

Mutation research Dec 11, 2020
Lung cancer is a prominent type of cancer, which leads to high mortality rate worldwide. The major lung cancers lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC) occur mainly due to somatic driver mutations in proteins and screening of su...
Lung Neoplasms Models, Genetic Neoplasm Proteins Adenocarcinoma of Lung Humans Deep Learning
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Identifying transcriptomic correlates of histology using deep learning.

PloS one Nov 25, 2020
Linking phenotypes to specific gene expression profiles is an extremely important problem in biology, which has been approached mainly by correlation methods or, more fundamentally, by studying the effects of gene perturbations. However, genome-wide ...
Genome Deep Learning Humans Histology Gene Expression Regulation, Neoplastic Neoplasms Neoplasm Proteins Transcriptome Tissue Distribution
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Synergistic drug combinations and machine learning for drug repurposing in chordoma.

Scientific reports Jul 31, 2020
Chordoma is a devastating rare cancer that affects one in a million people. With a mean-survival of just 6 years and no approved medicines, the primary treatments are surgery and radiation. In order to speed new medicines to chordoma patients, a drug...
Humans Machine Learning Cell Line, Tumor Antineoplastic Combined Chemotherapy Protocols Neoplasm Proteins Pyridines Drug Repositioning Morpholines Chordoma Piperazines Pyrimidines Benzamides
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Applications of radiomics and machine learning for radiotherapy of malignant brain tumors.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] May 11, 2020
BACKGROUND: Magnetic resonance imaging (MRI) and amino acid positron-emission tomography (PET) of the brain contain a vast amount of structural and functional information that can be analyzed by machine learning algorithms and radiomics for the use o...
Radiosurgery Promoter Regions, Genetic Isocitrate Dehydrogenase Imaging Genomics DNA Repair Enzymes Tumor Suppressor Proteins Neuroimaging DNA Methylation Progression-Free Survival Computational Biology Humans Diagnosis, Differential Positron-Emission Tomography Sensitivity and Specificity Glioma Radiation Oncology Neoplasm Proteins Brain Neoplasms Magnetic Resonance Imaging Neoplasm Grading Glioblastoma Radiotherapy Planning, Computer-Assisted Deep Learning Neoplasm Recurrence, Local DNA Modification Methylases Image Processing, Computer-Assisted
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