Data Structure Using C By Udit Agarwal Pdf Free
Gretchen Vansise
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Finding small molecules able to bind to a specific protein target is a critical aspect of drug discovery. In this project, using publicly available data on known small molecule-protein bindings from structured sources such as BindingDB and PubChem, we investigate using recently proposed deep learning representations for chemical structures and protein sequences to make drug-protein binding predictions. We propose an end-to-end model that predicts Drug-Target Interactions by taking common unprocessed representations for drugs and proteins as input. Drugs are represented as graphs, with the constituent atoms being the nodes and the bonds as edges between them. Proteins are represented as a sequence of amino acids. We apply graph convolutions on drugs and temporal convolutions on proteins to learn their fingerprint. We compare the performance networks using hand-engineered features to our end-to-end network.
We explore the problem of transferring knowledge learned from previously collected datasets and trained Deep Learning models to tasks for which there isn't sufficient labeled data, the cost of collecting labeled data is too high or the task is completely different from ones previously encountered. We consider the task of closed-domain Reading Comprehension: Question Answering using the Stanford Question Answering Dataset as the source domain with the NewsQA dataset as our transfer domain.
Visual question answering deals with coming up with an efficient representation of both the text and visual domains in order to perform reasoning. This is a challenging problem because reasoning in real world requires us to understand how different objects interact and behave with each other in the scene. To build systems that can reason, we need to incorporate concepts such as compositionality, physics, world knowledge etc. which is trivial for humans but not for current intelligent systems. We try to explore this task via the specific problem of question answering in the space of plots and figures using the recently released FigureQA dataset. We build on the ideas of task specific architectures such as Relation Networks and task generic architectures like FiLM to improve the state of the art performance on the FigureQA dataset.
In biological literature, genes and proteins are referred to using a wide variety of terminology. Given the huge volume of publications every year and the inherent diversity in the field, biologists currently spend a significant amount of time and effort searching for information about genes and proteins. The Gene Ontology (GO) is a collaborative project focused on combining the information about the genes into one integrated database. The ontology covers three broad domains; namely, cellular components, molecular functions, and biological processes. However, currently, only 5% of gene annotations are manually curated and hence authentic. The remaining 95% of the annotations are electronically inferred and have not been verified manually. Although electronically inferred sources have enabled increasing the GO coverage significantly, research shows that inequality across annotation resources can lead to significant bias in Biomedical research. This project attempts to make GO more comprehensive and trustworthy, by building a classifier that identifies the type of evidence to assign to a GO annotation. This evidence detector can then be applied to the electronically inferred annotation to understand their validity.
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