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Design of Knowledge Bases for Plant Gene Regulatory Networks.

Methods in molecular biology (Clifton, N.J.)
Developing a knowledge base that contains all the information necessary for the researcher studying gene regulation in a particular organism can be accomplished in four stages. This begins with defining the data scope. We describe here the necessary ...

Extracting microRNA-gene relations from biomedical literature using distant supervision.

PloS one
Many biomedical relation extraction approaches are based on supervised machine learning, requiring an annotated corpus. Distant supervision aims at training a classifier by combining a knowledge base with a corpus, reducing the amount of manual effor...

pLoc-mVirus: Predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC.

Gene
Knowledge of subcellular locations of proteins is crucially important for in-depth understanding their functions in a cell. With the explosive growth of protein sequences generated in the postgenomic age, it is highly demanded to develop computationa...

Webcrawling and machine learning as a new approach for the spatial distribution of atmospheric emissions.

PloS one
In this study we apply two methods for data collection that are relatively new in the field of atmospheric science. The two developed methods are designed to collect essential geo-localized information to be used as input data for a high resolution e...

Machine Learning to Predict Binding Affinity.

Methods in molecular biology (Clifton, N.J.)
Recent progress in the development of scientific libraries with machine-learning techniques paved the way for the implementation of integrated computational tools to predict ligand-binding affinity. The prediction of binding affinity uses the atomic ...

Building Machine-Learning Scoring Functions for Structure-Based Prediction of Intermolecular Binding Affinity.

Methods in molecular biology (Clifton, N.J.)
Molecular docking enables large-scale prediction of whether and how small molecules bind to a macromolecular target. Machine-learning scoring functions are particularly well suited to predict the strength of this interaction. Here we describe how to ...

mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides.

International journal of molecular sciences
Anticancer peptides (ACPs) are promising therapeutic agents for targeting and killing cancer cells. The accurate prediction of ACPs from given peptide sequences remains as an open problem in the field of immunoinformatics. Recently, machine learning ...

Prediction of ATP-binding sites in membrane proteins using a two-dimensional convolutional neural network.

Journal of molecular graphics & modelling
Membrane proteins, the most important drug targets, account for around 30% of total proteins encoded by the genome of living organisms. An important role of these proteins is to bind adenosine triphosphate (ATP), facilitating crucial biological proce...

Siamese Neural Networks for User Identity Linkage Through Web Browsing.

IEEE transactions on neural networks and learning systems
Linking online identities of users among countless heterogeneous network services on the Internet can provide an explicit digital representation of users, which can benefit both research and industry. In recent years, user identity linkage (UIL) thro...

QTG-Finder: A Machine-Learning Based Algorithm To Prioritize Causal Genes of Quantitative Trait Loci in Arabidopsis and Rice.

G3 (Bethesda, Md.)
Linkage mapping is one of the most commonly used methods to identify genetic loci that determine a trait. However, the loci identified by linkage mapping may contain hundreds of candidate genes and require a time-consuming and labor-intensive fine ma...