ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides (Q62729907)

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30 March 2020

title

ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides (English)

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30 March 2020

1

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30 March 2020

Jiangning Song

4

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30 March 2020

author name string

Chen Zhou

2

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30 March 2020

Huangrong Chen

3

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Ran Su

5

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language of work or name

English

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publication date

1 December 2018

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30 March 2020

volume

34

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issue

23

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page(s)

4007-4016

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

30 March 2020

cites work

1 reference

12 December 2020

Support vector machine classification and validation of cancer tissue samples using microarray expression data

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12 December 2020

Minimum redundancy feature selection from microarray gene expression data

1 reference

12 December 2020

Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy

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12 December 2020

Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences

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12 December 2020

Cationic antimicrobial peptides as novel cytotoxic agents for cancer treatment

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12 December 2020

Peptide-based drug design: here and now

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12 December 2020

Cancer statistics, 2010

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12 December 2020

Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008

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12 December 2020

Promises of apoptosis-inducing peptides in cancer therapeutics

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12 December 2020

Update of PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence

1 reference

12 December 2020

The use of therapeutic peptides to target and to kill cancer cells

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12 December 2020

Predicting anticancer peptides with Chou's pseudo amino acid composition and investigating their mutagenicity via Ames test

1 reference

12 December 2020

Cancer drug resistance: an evolving paradigm

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12 December 2020

From antimicrobial to anticancer peptides. A review

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12 December 2020

In silico models for designing and discovering novel anticancer peptides.

1 reference

12 December 2020

PhosphoSVM: prediction of phosphorylation sites by integrating various protein sequence attributes with a support vector machine.

1 reference

12 December 2020

CancerPPD: a database of anticancer peptides and proteins.

1 reference

12 December 2020

iACP: a sequence-based tool for identifying anticancer peptides

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12 December 2020

Fast prediction of protein methylation sites using a sequence-based feature selection technique.

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12 December 2020

CPPred-RF: A Sequence-based Predictor for Identifying Cell-Penetrating Peptides and Their Uptake Efficiency

1 reference

12 December 2020

Identifying N6-methyladenosine sites using multi-interval nucleotide pair position specificity and support vector machine.

1 reference

12 December 2020

SkipCPP-Pred: an improved and promising sequence-based predictor for predicting cell-penetrating peptides

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12 December 2020

10.1093/BIOINFORMATICS/BTY451

Identifiers

DOI

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journals/bioinformatics/WeiZCSS18

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DBLP publication ID

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DBLP Dataset 2021-01-02

28 January 2021

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