AIMC Topic: Cell Line, Tumor
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MEDICASCY: A Machine Learning Approach for Predicting Small-Molecule Drug Side Effects, Indications, Efficacy, and Modes of Action.
Molecular pharmaceutics
Apr 13, 2020
To improve the drug discovery yield, a method which is implemented at the beginning of drug discovery that accurately predicts drug side effects, indications, efficacy, and mode of action based solely on the input of the drug's chemical structure is ...
DeepMILO: a deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure.
Genome biology
Mar 26, 2020
Non-coding variants have been shown to be related to disease by alteration of 3D genome structures. We propose a deep learning method, DeepMILO, to predict the effects of variants on CTCF/cohesin-mediated insulator loops. Application of DeepMILO on v...
Prediction Model of Aryl Hydrocarbon Receptor Activation by a Novel QSAR Approach, DeepSnap-Deep Learning.
Molecules (Basel, Switzerland)
Mar 13, 2020
The aryl hydrocarbon receptor (AhR) is a ligand-dependent transcription factor that senses environmental exogenous and endogenous ligands or xenobiotic chemicals. In particular, exposure of the liver to environmental metabolism-disrupting chemicals c...
A Convolutional Neural Network-Based Approach for the Rapid Annotation of Molecularly Diverse Natural Products.
Journal of the American Chemical Society
Feb 21, 2020
This report describes the first application of the novel NMR-based machine learning tool "Small Molecule Accurate Recognition Technology" (SMART 2.0) for mixture analysis and subsequent accelerated discovery and characterization of new natural produc...
iLoF: An intelligent Lab on Fiber Approach for Human Cancer Single-Cell Type Identification.
Scientific reports
Feb 21, 2020
With the advent of personalized medicine, there is a movement to develop "smaller" and "smarter" microdevices that are able to distinguish similar cancer subtypes. Tumor cells display major differences when compared to their natural counterparts, due...
Lipidome-based rapid diagnosis with machine learning for detection of TGF-β signalling activated area in head and neck cancer.
British journal of cancer
Feb 5, 2020
BACKGROUND: Several pro-oncogenic signals, including transforming growth factor beta (TGF-β) signalling from tumour microenvironment, generate intratumoural phenotypic heterogeneity and result in tumour progression and treatment failure. However, the...
RefDNN: a reference drug based neural network for more accurate prediction of anticancer drug resistance.
Scientific reports
Feb 5, 2020
Cancer is one of the most difficult diseases to treat owing to the drug resistance of tumour cells. Recent studies have revealed that drug responses are closely associated with genomic alterations in cancer cells. Numerous state-of-the-art machine le...
Machine learning-based prediction of glioma margin from 5-ALA induced PpIX fluorescence spectroscopy.
Scientific reports
Jan 29, 2020
Gliomas are infiltrative brain tumors with a margin difficult to identify. 5-ALA induced PpIX fluorescence measurements are a clinical standard, but expert-based classification models still lack sensitivity and specificity. Here a fully automatic clu...
Machine Learning and Network Analyses Reveal Disease Subtypes of Pancreatic Cancer and their Molecular Characteristics.
Scientific reports
Jan 27, 2020
Given that the biological processes governing the oncogenesis of pancreatic cancers could present useful therapeutic targets, there is a pressing need to molecularly distinguish between different clinically relevant pancreatic cancer subtypes. To add...
In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics.
Nature communications
Jan 9, 2020
Data-independent acquisition (DIA) is an emerging technology for quantitative proteomic analysis of large cohorts of samples. However, sample-specific spectral libraries built by data-dependent acquisition (DDA) experiments are required prior to DIA ...
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