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Found 10 papers, 4 papers with code
All You Need is Color: Image based Spatial Gene Expression Prediction using Neural Stain Learning
"Is it possible to predict expression levels of different genes at a given spatial location in the routine histology image of a tumor section by modeling its stain absorption characteristics?"
ALBRT: Cellular Composition Prediction in Routine Histology Images
Cellular composition prediction, i. e., predicting the presence and counts of different types of cells in the tumor microenvironment from a digitized image of a Hematoxylin and Eosin (H&E) stained tissue section can be used for various tasks in computational pathology such as the analysis of cellular topology and interactions, subtype prediction, survival analysis, etc.
Learning Neural Activations
An artificial neuron is modelled as a weighted summation followed by an activation function which determines its output.
Generalized Learning with Rejection for Classification and Regression Problems
We have demonstrated the applicability and effectiveness of the method on synthetically generated data as well as benchmark datasets from UCI machine learning repository for both classification and regression problems.
An embarrassingly simple approach to neural multiple instance classification
Multiple Instance Learning (MIL) is a weak supervision learning paradigm that allows modeling of machine learning problems in which labels are available only for groups of examples called bags.
A Generalized Meta-loss function for regression and classification using privileged information
Learning using privileged information (LUPI) is a powerful heterogenous feature space machine learning framework that allows a machine learning model to learn from highly informative or privileged features which are available during training only to generate test predictions using input space features which are available both during training and testing.
ISLAND: In-Silico Prediction of Proteins Binding Affinity Using Sequence Descriptors
Determination of binding affinity of proteins in the formation of protein complexes requires sophisticated, expensive and time-consuming experimentation which can be replaced with computational methods.
Training large margin host-pathogen protein-protein interaction predictors
We have also developed a web server for our HPI predictor called HoPItor (Host Pathogen Interaction predicTOR) that can predict interactions between human and viral proteins.
pyLEMMINGS: Large Margin Multiple Instance Classification and Ranking for Bioinformatics Applications
Motivation: A major challenge in the development of machine learning based methods in computational biology is that data may not be accurately labeled due to the time and resources required for experimentally annotating properties of proteins and DNA sequences.
