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DNA-Binding Proteins

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Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients.

Nature cancer
Cell-line screens create expansive datasets for learning predictive markers of drug response, but these models do not readily translate to the clinic with its diverse contexts and limited data. In the present study, we apply a recently developed tech...

DeepD2V: A Novel Deep Learning-Based Framework for Predicting Transcription Factor Binding Sites from Combined DNA Sequence.

International journal of molecular sciences
Predicting in vivo protein-DNA binding sites is a challenging but pressing task in a variety of fields like drug design and development. Most promoters contain a number of transcription factor (TF) binding sites, but only a small minority has been id...

DeepDISE: DNA Binding Site Prediction Using a Deep Learning Method.

International journal of molecular sciences
It is essential for future research to develop a new, reliable prediction method of DNA binding sites because DNA binding sites on DNA-binding proteins provide critical clues about protein function and drug discovery. However, the current prediction ...

A sequence-based multiple kernel model for identifying DNA-binding proteins.

BMC bioinformatics
BACKGROUND: DNA-Binding Proteins (DBP) plays a pivotal role in biological system. A mounting number of researchers are studying the mechanism and detection methods. To detect DBP, the tradition experimental method is time-consuming and resource-consu...

Application of deep learning to understand resilience to Alzheimer's disease pathology.

Brain pathology (Zurich, Switzerland)
People who have Alzheimer's disease neuropathologic change (ADNC) typically associated with dementia but not the associated cognitive decline can be considered to be "resilient" to the effects of ADNC. We have previously reported lower neocortical le...

DBP-GAPred: An intelligent method for prediction of DNA-binding proteins types by enhanced evolutionary profile features with ensemble learning.

Journal of bioinformatics and computational biology
DNA-binding proteins (DBPs) perform an influential role in diverse biological activities like DNA replication, slicing, repair, and transcription. Some DBPs are indispensable for understanding many types of human cancers (i.e. lung, breast, and liver...

RF-SVM: Identification of DNA-binding proteins based on comprehensive feature representation methods and support vector machine.

Proteins
Protein-DNA interactions play an important role in biological progress, such as DNA replication, repair, and modification processes. In order to have a better understanding of its functions, the one of the most important steps is the identification o...

FTWSVM-SR: DNA-Binding Proteins Identification via Fuzzy Twin Support Vector Machines on Self-Representation.

Interdisciplinary sciences, computational life sciences
Due to the high cost of DNA-binding proteins (DBPs) detection, many machine learning algorithms (ML) have been utilized to large-scale process and detect DBPs. The previous methods took no count of the processing of noise samples. In this study, a fu...

DeepDISOBind: accurate prediction of RNA-, DNA- and protein-binding intrinsically disordered residues with deep multi-task learning.

Briefings in bioinformatics
Proteins with intrinsically disordered regions (IDRs) are common among eukaryotes. Many IDRs interact with nucleic acids and proteins. Annotation of these interactions is supported by computational predictors, but to date, only one tool that predicts...

iDRBP-EL: Identifying DNA- and RNA- Binding Proteins Based on Hierarchical Ensemble Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Identification of DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs) from the primary sequences is essential for further exploring protein-nucleic acid interactions. Previous studies have shown that machine-learning-based methods can efficie...