Please see the most updated version here . Was there a specific task in mind? Was there a specific gap that needed to be filled? The MOTI VE dataset was created to promote the development of new drug-target interaction (DTI) prediction models based on both, existing relationships between compounds and their protein targets, and the similarity of JUMP Cell Painting morphological features of perturbed cells [2].The MOTI VE dataset was created with the DTI task in mind, and addresses a lack of graph-based biological datasets with empirical node features. Who created this dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)? This dataset was created by the Carpenter-Singh Lab in the Imaging Platform at the Broad Institute of MIT and Harvard, Cambridge, Massachusetts. What support was needed to make this dataset? If there is an associated grant, provide the name of the grantor and the grant name and number, or if it was supported by a company or government agency, give those details.) The authors gratefully acknowledge an internship from the Massachusetts Life Sciences Center (to ES).

All linear models were implemented using PyTorch. We used an Nvidia GTX 1080 Ti GPU for training. IG computes feature attributions by comparing a model's prediction with the prediction We also found that EG led to the best performance for models trained using the DAPr framework. RNA-seq data as follows 1. N is the total number of counts. We also scaled Dasatinib IC50 values to have zero mean and unit variance.

Motivation: In recent years, image-based biological assays have steadily become high-throughput, sparking a need for fast automated methods to extract biologically-meaningful information from hundreds of thousands of images. Taking inspiration from the success of ImageNet, we curate CytoImageNet, a large-scale dataset of openly-sourced and weakly-labeled microscopy images (890K images, 894 classes). Pretraining on CytoImageNet yields features that are competitive to ImageNet features on downstream microscopy classification tasks. We show evidence that CytoImageNet features capture information not available in ImageNet-trained features. The dataset is made available at https://www.kaggle.com/stanleyhua/cytoimagenet.

A key goal of computational personalized medicine is to systematically utilize genomic and other molecular features of samples to predict drug responses for a previously unseen sample. Such predictions are valuable for developing hypotheses for selecting therapies tailored for individual patients. This is especially valuable in oncology, where molecular and genetic heterogeneity of the cells has a major impact on the response. However, the prediction task is extremely challenging, raising the need for methods that can effectively model and predict drug responses. In this study, we propose a novel formulation of multi-task matrix factorization that allows selective data integration for predicting drug responses. To solve the modeling task, we extend the state-of-the-art kernelized Bayesian matrix factorization (KBMF) method with component-wise multiple kernel learning. In addition, our approach exploits the known pathway information in a novel and biologically meaningful fashion to learn the drug response associations. Our method quantitatively outperforms the state of the art on predicting drug responses in two publicly available cancer data sets as well as on a synthetic data set. In addition, we validated our model predictions with lab experiments using an in-house cancer cell line panel. We finally show the practical applicability of the proposed method by utilizing prior knowledge to infer pathway-drug response associations, opening up the opportunity for elucidating drug action mechanisms. We demonstrate that pathway-response associations can be learned by the proposed model for the well known EGFR and MEK inhibitors.