4.6 Article

Anticancer Peptide Prediction via Multi-Kernel CNN and Attention Model

Related references

Note: Only part of the references are listed.
Review Biochemistry & Molecular Biology

Comprehensive Review and Comparison of Anticancer Peptides Identification Models

Xiao Song et al.

Summary: This study provides a comprehensive evaluation of machine learning methods for ACPs prediction and a fair comparison of existing predictors. The Support Vector Machine-based model with features combination showed significant improvement in overall performance compared to other machine learning methods.

CURRENT PROTEIN & PEPTIDE SCIENCE (2021)

Article Biochemical Research Methods

AntiCP 2.0: an updated model for predicting anticancer peptides

Piyush Agrawal et al.

Summary: The study developed a computational model for predicting and designing anticancer peptides (ACPs), revealing residue composition preference, positional preference, and motif features of ACPs. Machine learning models were utilized and trained on different datasets, with the best models implemented on the webserver AntiCP 2.0 for free access.

BRIEFINGS IN BIOINFORMATICS (2021)

Review Microbiology

Rediscovery of antimicrobial peptides as therapeutic agents

Minkyung Ryu et al.

Summary: AMPs are promising alternatives to antibiotics, exhibiting not only antimicrobial activity but also antifungal, antiviral, anticancer, antioxidant, and insecticidal effects. Due to their target specificity and complexity of action mechanisms, many AMPs are relatively safe, with lower chances of toxic side effects and resistance in microorganisms.

JOURNAL OF MICROBIOLOGY (2021)

Article Biochemical Research Methods

Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework

Leyi Wei et al.

Summary: The study introduced a novel machine learning approach called Stack-ORI to identify replication origin sites (ORIs) in four different eukaryotic species. Results showed that Stack-ORI outperformed baseline models on both training and independent datasets, consistently achieving better performance across all cell-specific models. The novel approach also provided necessary explanations for model success, highlighting the most important feature encoding schemes significant for predicting cell-specific ORIs.

BRIEFINGS IN BIOINFORMATICS (2021)

Article Biochemical Research Methods

Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools

Ran Su et al.

BRIEFINGS IN BIOINFORMATICS (2020)

Article Biochemical Research Methods

ACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptides

Bing Rao et al.

BRIEFINGS IN BIOINFORMATICS (2020)

Article Biochemical Research Methods

Deep-Resp-Forest: A deep forest model to predict anti-cancer drug response

Ran Su et al.

METHODS (2019)

Article Biochemical Research Methods

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

Leyi Wei et al.

JOURNAL OF PROTEOME RESEARCH (2017)

Article Computer Science, Artificial Intelligence

Convolutional neural networks for hyperspectral image classification

Shiqi Yu et al.

NEUROCOMPUTING (2017)

Article Biotechnology & Applied Microbiology

PSBinder: A Web Service for Predicting Polystyrene Surface-Binding Peptides

Ning Li et al.

BIOMED RESEARCH INTERNATIONAL (2017)

Article Computer Science, Artificial Intelligence

Parallel vision for perception and understanding of complex scenes: methods, framework, and perspectives

Kunfeng Wang et al.

ARTIFICIAL INTELLIGENCE REVIEW (2017)

Article Reproductive Biology

Gonadotoxic effects of busulfan in two strains of mice

Karina Gutierrez et al.

REPRODUCTIVE TOXICOLOGY (2016)

Article Biochemistry & Molecular Biology

ACPP: A Web Server for Prediction and Design of Anti-cancer Peptides

Saravanan Vijayakumar et al.

INTERNATIONAL JOURNAL OF PEPTIDE RESEARCH AND THERAPEUTICS (2015)

Article Biochemistry & Molecular Biology

CancerPPD: a database of anticancer peptides and proteins

Atul Tyagi et al.

NUCLEIC ACIDS RESEARCH (2015)

Article Public, Environmental & Occupational Health

Hesperidin protects brain and sciatic nerve tissues against cisplatin-induced oxidative, histological and electromyographical side effects in rats

Suat Kamisli et al.

TOXICOLOGY AND INDUSTRIAL HEALTH (2015)

Article Reproductive Biology

The Effect of Busulfan on Body Weight, Testis Weight and MDA Enzymes in Male Rats

Nasibeh Hosseini Ahar et al.

INTERNATIONAL JOURNAL OF WOMENS HEALTH AND REPRODUCTION SCIENCES (2014)

Article Computer Science, Artificial Intelligence

Learning Hierarchical Features for Scene Labeling

Clement Farabet et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2013)

Article Multidisciplinary Sciences

In Silico Models for Designing and Discovering Novel Anticancer Peptides

Atul Tyagi et al.

SCIENTIFIC REPORTS (2013)

Review Microbiology

From antimicrobial to anticancer peptides. A review

Diana Gaspar et al.

FRONTIERS IN MICROBIOLOGY (2013)

Article Biochemistry & Molecular Biology

Cucurbit[7]uril encapsulated cisplatin overcomes cisplatin resistance via a pharmacokinetic effect

Jane A. Plumb et al.

METALLOMICS (2012)

Review Biochemistry & Molecular Biology

Promises of Apoptosis-Inducing Peptides in Cancer Therapeutics

David Barras et al.

CURRENT PHARMACEUTICAL BIOTECHNOLOGY (2011)

Article Biochemical Research Methods

CD-HIT Suite: a web server for clustering and comparing biological sequences

Ying Huang et al.

BIOINFORMATICS (2010)

Review Oncology

The clinical development of the bryostatins

A Clamp et al.

ANTI-CANCER DRUGS (2002)