AIMC Topic: Antineoplastic Agents

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A comparative analysis of saponin-enriched fraction from (Moench) Garcke, (Gaertn) and (Santapau and Fernandes): an hemolytic and cytotoxicity evaluation.

Animal biotechnology Jun 17, 2020
To explore the newer saponin resources, toxicity of saponin-enriched fraction (SEF) extracted from (SV) was evaluated for first time and compared with toxicity of SEF extracted from (SM) and (CV). All extracted SEF from diverse resources were cha...
Sapindus Animals Saponins Antineoplastic Agents Sheep Plant Extracts Silene
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Multi-input deep learning architecture for predicting breast tumor response to chemotherapy using quantitative MR images.

International journal of computer assisted radiology and surgery Jun 16, 2020
PURPOSE: Neoadjuvant chemotherapy (NAC) aims to minimize the tumor size before surgery. Predicting response to NAC could reduce toxicity and delays to effective intervention. Computational analysis of dynamic contrast-enhanced magnetic resonance imag...
Middle Aged Antineoplastic Agents Diagnosis, Computer-Assisted Deep Learning Retrospective Studies Female Adult Breast Neoplasms Contrast Media Magnetic Resonance Imaging Area Under Curve Chemotherapy, Adjuvant Humans Neural Networks, Computer ROC Curve Image Processing, Computer-Assisted Treatment Outcome Neoadjuvant Therapy
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Beyond the limitation of targeted therapy: Improve the application of targeted drugs combining genomic data with machine learning.

Pharmacological research May 28, 2020
Precision oncology involves effectively selecting drugs for cancer patients and planning an effective treatment regimen. However, for Molecular targeted drug, using genomic state of the drug target to select drugs has limitations. Many patients who c...
Databases, Genetic Models, Statistical Drug Screening Assays, Antitumor Molecular Targeted Therapy Genomics Decision Support Techniques Gene Expression Regulation, Neoplastic Precision Medicine Signal Transduction Clinical Decision-Making Humans Biomarkers, Tumor Machine Learning Neoplasms Antineoplastic Agents Pharmacogenetics Cell Line, Tumor
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Robot technology identifies a Parkinsonian therapeutics repurpose to target stem cells of glioblastoma.

CNS oncology May 28, 2020
Glioblastoma is a heterogeneous lethal disease, regulated by a stem-cell hierarchy and the neurotransmitter microenvironment. The identification of chemotherapies targeting individual cancer stem cells is a clinical need. A robotic workstation was ...
High-Throughput Screening Assays Tumor Cells, Cultured Neurotransmitter Agents Humans Brain Neoplasms Antineoplastic Agents Neoplastic Stem Cells Parkinson Disease Drug Repositioning Robotics Glioblastoma
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The emerging roles of artificial intelligence in cancer drug development and precision therapy.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie May 20, 2020
Artificial intelligence (AI) has strong logical reasoning ability and independent learning ability, which can simulate the thinking process of the human brain. AI technologies such as machine learning can profoundly optimize the existing mode of anti...
Humans Artificial Intelligence Precision Medicine Clinical Decision-Making Antigens, Neoplasm Signal Transduction Drug Development Drug Resistance, Neoplasm Animals Neoplasms Antineoplastic Agents Immunotherapy Molecular Targeted Therapy Radiotherapy, Computer-Assisted
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Discovering the hidden messages within cell trajectories using a deep learning approach for in vitro evaluation of cancer drug treatments.

Scientific reports May 6, 2020
We describe a novel method to achieve a universal, massive, and fully automated analysis of cell motility behaviours, starting from time-lapse microscopy images. The approach was inspired by the recent successes in application of machine learning for...
Computational Biology Drug Screening Assays, Antitumor Molecular Imaging Bioengineering Cell Tracking Humans Machine Learning Time-Lapse Imaging Algorithms Reproducibility of Results Antineoplastic Agents
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Convolutional Neural Network Can Recognize Drug Resistance of Single Cancer Cells.

International journal of molecular sciences Apr 30, 2020
It is known that single or isolated tumor cells enter cancer patients' circulatory systems. These circulating tumor cells (CTCs) are thought to be an effective tool for diagnosing cancer malignancy. However, handling CTC samples and evaluating CTC se...
Humans Neural Networks, Computer Machine Learning Cells, Cultured Precision Medicine Neoplasms Single-Cell Analysis Antineoplastic Agents Drug Resistance, Neoplasm Deep Learning Cell Line, Tumor
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SAEROF: an ensemble approach for large-scale drug-disease association prediction by incorporating rotation forest and sparse autoencoder deep neural network.

Scientific reports Mar 18, 2020
Drug-disease association is an important piece of information which participates in all stages of drug repositioning. Although the number of drug-disease associations identified by high-throughput technologies is increasing, the experimental methods ...
Support Vector Machine Obesity Anti-Obesity Agents Area Under Curve Humans Databases, Factual ROC Curve Neural Networks, Computer Computer Simulation Lung Neoplasms Machine Learning Stomach Neoplasms Computational Biology Algorithms Antineoplastic Agents
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Machine Learning Analysis of Individual Tumor Lesions in Four Metastatic Colorectal Cancer Clinical Studies: Linking Tumor Heterogeneity to Overall Survival.

The AAPS journal Mar 16, 2020
Total tumor size (TS) metrics used in TS models in oncology do not consider tumor heterogeneity, which could help to better predict drug efficacy. We analyzed individual target lesions (iTLs) of patients with metastatic colorectal carcinoma (mCRC) to...
Carcinoma Humans Clinical Studies as Topic Machine Learning Neoplasm Metastasis Colorectal Neoplasms Proportional Hazards Models Antineoplastic Agents
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Machine learning prediction of oncology drug targets based on protein and network properties.

BMC bioinformatics Mar 14, 2020
BACKGROUND: The selection and prioritization of drug targets is a central problem in drug discovery. Computational approaches can leverage the growing number of large-scale human genomics and proteomics data to make in-silico target identification, r...
Proteins Area Under Curve Drug Discovery Antineoplastic Agents Protein Interaction Maps Genomics Humans Machine Learning Computer Simulation
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