(Q44322341)

1 December 2017

title

Prediction of interaction between small molecule and enzyme using AdaBoost (English)

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1 December 2017

9

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1 December 2017

Bing Niu

1

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1 December 2017

Yuhuan Jin

2

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1 December 2017

Lin Lu

3

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1 December 2017

Kaiyan Fen

4

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1 December 2017

Lei Gu

5

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1 December 2017

Zhisong He

6

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1 December 2017

Wencong Lu

7

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1 December 2017

Yixue Li

8

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1 December 2017

publication date

14 February 2009

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1 December 2017

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1 December 2017

volume

13

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1 December 2017

issue

3

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1 December 2017

page(s)

313-320

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https://scigraph.springernature.com/pub.10.1007/s11030-009-9116-1

1 December 2017

exact match

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cites work

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Crossref

https://api.crossref.org/works/10.1007%2FS11030-009-9116-1

From genomics to chemical genomics: new developments in KEGG

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Predicting protein structural class by functional domain composition

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Support vector machines for predicting the specificity of GalNAc-transferase.

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Artificial neural network model for predicting protein subcellular location.

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Support vector machines for prediction of protein subcellular location by incorporating quasi-sequence-order effect.

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12 December 2020

Using Chou's pseudo amino acid composition based on approximate entropy and an ensemble of AdaBoost classifiers to predict protein subnuclear location

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The classification and origins of protein folding patterns

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12 December 2020

The European Bioinformatics Institute's data resources: towards systems biology

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12 December 2020

STITCH: interaction networks of chemicals and proteins

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12 December 2020

Support vector machines for prediction of protein domain structural class.

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12 December 2020

Predicting protein structural class with AdaBoost Learner.

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12 December 2020

New approach to pharmacophore mapping and QSAR analysis using inductive logic programming. Application to thermolysin inhibitors and glycogen phosphorylase B inhibitors.

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12 December 2020

Artificial neural network method for predicting protein secondary structure content.

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Study of the feasibility of distinguishing cigarettes of different brands using an Adaboost algorithm and near-infrared spectroscopy.

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12 December 2020

Support Vector Machines for predicting HIV protease cleavage sites in protein.

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12 December 2020

Principles governing amino acid composition of integral membrane proteins: application to topology prediction

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12 December 2020

Predicting subcellular localization with AdaBoost Learner.

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12 December 2020

Support Vector Machines for Predicting Membrane Protein Types by Using Functional Domain Composition

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12 December 2020

PromoterExplorer: an effective promoter identification method based on the AdaBoost algorithm.

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12 December 2020

Using AdaBoost for the prediction of subcellular location of prokaryotic and eukaryotic proteins

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Database resources of the National Center for Biotechnology Information

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12 December 2020

Prediction of membrane protein types by incorporating amphipathic effects

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Recognition of a protein fold in the context of the Structural Classification of Proteins (SCOP) classification

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12 December 2020

Using LogitBoost classifier to predict protein structural classes.

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12 December 2020

Knowledge-based analysis of microarray gene expression data by using support vector machines

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12 December 2020

Seventy-five percent accuracy in protein secondary structure prediction

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12 December 2020

Prediction of membrane protein types and subcellular locations.

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12 December 2020

Automated classification of lung bronchovascular anatomy in CT using AdaBoost.

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Classification and prediction of beta-turn types.

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MetaCyc: a multiorganism database of metabolic pathways and enzymes

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A novel approach to predicting protein structural classes in a (20-1)-D amino acid composition space

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Support vector machines for the classification and prediction of ?-turn types

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12 December 2020

10.1007/S11030-009-9116-1

Identifiers

DOI

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1 December 2017

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