ADRML: anticancer drug response prediction using manifold learning
نویسندگان
چکیده
منابع مشابه
Supervised Manifold Learning for Media Interestingness Prediction
In this paper, we describe the models designed for automatically selecting multimedia data, e.g., image and video segments, which are considered to be interesting for a common viewer. Specifically, we utilize an existing dimensionality reduction method called Neighborhood MinMax Projections (NMMP) to extract the low-dimensional features for predicting the discrete interestingness labels. Meanwh...
متن کاملInteractive Learning Using Manifold Geometry
We present an interactive learning method that enables a user to iteratively refine a regression model. The user examines the output of the model, visualized as the vertical axis of a 2D scatterplot, and provides corrections by repositioning individual data instances to the correct output level. Each repositioned data instance acts as a control point for altering the learned model, using the ge...
متن کاملMachine Learning Based Drug Indication Prediction Using Linked Open Data
In this study, drug and disease features were obtained by querying open linked data to train our classifier for predicting new drug indications, and the predictive performance of the classifier for different validation schemes was evaluated. We collected the drug and disease data from Bio2RDF, an open source project that uses semantic web technologies to link data from multiple sources. A binar...
متن کاملDrug-target interaction prediction using ensemble learning and dimensionality reduction.
Experimental prediction of drug-target interactions is expensive, time-consuming and tedious. Fortunately, computational methods help narrow down the search space for interaction candidates to be further examined via wet-lab techniques. Nowadays, the number of attributes/features for drugs and targets, as well as the amount of their interactions, are increasing, making these computational metho...
متن کاملDrug response prediction by ensemble learning and drug-induced gene expression signatures
Motivation: Chemotherapeutic response of cancer cells to a given compound is one of the most fundamental information one requires to design anti-cancer drugs. Recent advances in producing large drug screens against cancer cell lines provided an opportunity to apply machine learning methods for this purpose. In addition to cytotoxicity databases, considerable amount of drug-induced gene expressi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Scientific Reports
سال: 2020
ISSN: 2045-2322
DOI: 10.1038/s41598-020-71257-7