scholarly journals MLACP: machine-learning-based prediction of anticancer peptides

Oncotarget ◽  
2017 ◽  
Vol 8 (44) ◽  
pp. 77121-77136 ◽  
Author(s):  
Balachandran Manavalan ◽  
Shaherin Basith ◽  
Tae Hwan Shin ◽  
Sun Choi ◽  
Myeong Ok Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Anticancer Peptides

Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery

2019 ◽  
Vol 19 (1) ◽  
pp. 4-16 ◽  
Author(s):  
Qihui Wu ◽  
Hanzhong Ke ◽  
Dongli Li ◽  
Qi Wang ◽  
Jiansong Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Large Scale ◽  
Recent Progress ◽  
High Specificity ◽  
Learning Approaches ◽  
Anticancer Peptides ◽  
The Past ◽  
Traditional Approaches ◽  
Large Scale Screening

Over the past decades, peptide as a therapeutic candidate has received increasing attention in drug discovery, especially for antimicrobial peptides (AMPs), anticancer peptides (ACPs) and antiinflammatory peptides (AIPs). It is considered that the peptides can regulate various complex diseases which are previously untouchable. In recent years, the critical problem of antimicrobial resistance drives the pharmaceutical industry to look for new therapeutic agents. Compared to organic small drugs, peptide- based therapy exhibits high specificity and minimal toxicity. Thus, peptides are widely recruited in the design and discovery of new potent drugs. Currently, large-scale screening of peptide activity with traditional approaches is costly, time-consuming and labor-intensive. Hence, in silico methods, mainly machine learning approaches, for their accuracy and effectiveness, have been introduced to predict the peptide activity. In this review, we document the recent progress in machine learning-based prediction of peptides which will be of great benefit to the discovery of potential active AMPs, ACPs and AIPs.

scholarly journals Front Cover: Designing Anticancer Peptides by Constructive Machine Learning (ChemMedChem 13/2018)

ChemMedChem ◽  
2018 ◽  
Vol 13 (13) ◽  
pp. 1260-1260
Author(s):  
Francesca Grisoni ◽  
Claudia S. Neuhaus ◽  
Gisela Gabernet ◽  
Alex T. Müller ◽  
Jan A. Hiss ◽  
...  
Keyword(s):  
Machine Learning ◽  
Front Cover ◽  
Anticancer Peptides

Designing Anticancer Peptides by Constructive Machine Learning

ChemMedChem ◽  
2018 ◽  
Vol 13 (13) ◽  
pp. 1300-1302 ◽  
Author(s):  
Francesca Grisoni ◽  
Claudia S. Neuhaus ◽  
Gisela Gabernet ◽  
Alex T. Müller ◽  
Jan A. Hiss ◽  
...  
Keyword(s):  
Machine Learning ◽  
Anticancer Peptides

Comprehensive Review and Comparison for Anticancer Peptides Identification Models

2020 ◽  
Vol 21 ◽  
Author(s):  
Xiao Song ◽  
Yuanying Zhuang ◽  
Yihua Lan ◽  
Yinglai Lin ◽  
Xiaoping Min
Keyword(s):  
Machine Learning ◽  
Pathogenic Bacteria ◽  
Prediction Models ◽  
Support Vector ◽  
Comprehensive Review ◽  
Anticancer Peptides ◽  
Prediction Tools ◽  
Potential Applications ◽  
Normal Human ◽  
Comparative Results

: Anticancer peptides (ACPs) eliminate pathogenic bacteria and kill tumor cells, showing no hemolysis and no damages to normal human cells. This unique ability explores the possibility of ACPs as therapeutic delivery and its potential applications in clinical therapy. Identifying ACPs is one of the most fundamental and central problems in new antitumor drug research. During the past decades, a number of machine learning-based prediction tools have been developed to solve this important task. However, the predictions produced by various tools are difficult to quantify and compare. Therefore, in this article, we provide a comprehensive review of existing machine learning methods for ACPs prediction and fair comparison of the predictors. To evaluate current prediction tools, we conducted a comparative study and analyzed the existing ACPs predictor from 10 public literatures. The comparative results obtained suggest that Support Vector Machine-based model with features combination provided significant improvement in the overall performance, when compared to the other machine learning method-based prediction models.

scholarly journals ACP-DA: Improving the Prediction of Anticancer Peptides Using Data Augmentation

2021 ◽  
Vol 12 ◽  
Author(s):  
Xian-gan Chen ◽  
Wen Zhang ◽  
Xiaofei Yang ◽  
Chenhong Li ◽  
Hengling Chen
Keyword(s):  
Machine Learning ◽  
Cancer Treatment ◽  
Computational Methods ◽  
Data Augmentation ◽  
Feature Learning ◽  
Feature Space ◽  
Anticancer Peptides ◽  
Machine Learning Model ◽  
Using Data ◽  
Promising Perspective

Anticancer peptides (ACPs) have provided a promising perspective for cancer treatment, and the prediction of ACPs is very important for the discovery of new cancer treatment drugs. It is time consuming and expensive to use experimental methods to identify ACPs, so computational methods for ACP identification are urgently needed. There have been many effective computational methods, especially machine learning-based methods, proposed for such predictions. Most of the current machine learning methods try to find suitable features or design effective feature learning techniques to accurately represent ACPs. However, the performance of these methods can be further improved for cases with insufficient numbers of samples. In this article, we propose an ACP prediction model called ACP-DA (Data Augmentation), which uses data augmentation for insufficient samples to improve the prediction performance. In our method, to better exploit the information of peptide sequences, peptide sequences are represented by integrating binary profile features and AAindex features, and then the samples in the training set are augmented in the feature space. After data augmentation, the samples are used to train the machine learning model, which is used to predict ACPs. The performance of ACP-DA exceeds that of existing methods, and ACP-DA achieves better performance in the prediction of ACPs compared with a method without data augmentation. The proposed method is available at http://github.com/chenxgscuec/ACPDA.

Evolution of Machine Learning Algorithms in the Prediction and Design of Anticancer Peptides

2020 ◽  
Vol 21 (12) ◽  
pp. 1242-1250 ◽  
Author(s):  
Shaherin Basith ◽  
Balachandran Manavalan ◽  
Tae Hwan Shin ◽  
Da Yeon Lee ◽  
Gwang Lee
Keyword(s):  
Machine Learning ◽  
Anticancer Agents ◽  
Model Building ◽  
Sequence Data ◽  
Machine Learning Algorithms ◽  
Great Promise ◽  
Future Directions ◽  
Design And Synthesis ◽  
Anticancer Peptides ◽  
Protein Sequence Data

: Peptides act as promising anticancer agents due to their ease of synthesis and modifications, enhanced tumor penetration, and less systemic toxicity. However, only limited success has been achieved so far, as experimental design and synthesis of anticancer peptides (ACPs) are prohibitively costly and time-consuming. Furthermore, the sequential increase in the protein sequence data via highthroughput sequencing makes it difficult to identify ACPs only through experimentation, which often involves months or years of speculation and failure. All these limitations could be overcome by applying machine learning (ML) approaches, which is a field of artificial intelligence that automates analytical model building for rapid and accurate outcome predictions. Recently, ML approaches hold great promise in the rapid discovery of ACPs, which could be witnessed by the growing number of MLbased anticancer prediction tools. In this review, we aim to provide a comprehensive view on the existing ML approaches for ACP predictions. Initially, we will briefly discuss the currently available ACP databases. This is followed by the main text, where state-of-the-art ML approaches working principles and their performances based on the ML algorithms are reviewed. Lastly, we discuss the limitations and future directions of the ML methods in the prediction of ACPs.

scholarly journals AntiCP 2.0: an updated model for predicting anticancer peptides

2020 ◽  
Author(s):  
Piyush Agrawal ◽  
Dhruv Bhagat ◽  
Manish Mahalwal ◽  
Neelam Sharma ◽  
Gajendra P S Raghava
Keyword(s):  
Machine Learning ◽  
Training Dataset ◽  
Validation Dataset ◽  
Composition Analysis ◽  
Operating Characteristics ◽  
Motif Analysis ◽  
Anticancer Peptides ◽  
C Terminus ◽  
Therapeutic Peptides ◽  
Model Training

Abstract Increasing use of therapeutic peptides for treating cancer has received considerable attention of the scientific community in the recent years. The present study describes the in silico model developed for predicting and designing anticancer peptides (ACPs). ACPs residue composition analysis show the preference of A, F, K, L and W. Positional preference analysis revealed that residues A, F and K are favored at N-terminus and residues L and K are preferred at C-terminus. Motif analysis revealed the presence of motifs like LAKLA, AKLAK, FAKL and LAKL in ACPs. Machine learning models were developed using various input features and implementing different machine learning classifiers on two datasets main and alternate dataset. In the case of main dataset, dipeptide composition based ETree classifier model achieved maximum Matthews correlation coefficient (MCC) of 0.51 and 0.83 area under receiver operating characteristics (AUROC) on the training dataset. In the case of alternate dataset, amino acid composition based ETree classifier performed best and achieved the highest MCC of 0.80 and AUROC of 0.97 on the training dataset. Five-fold cross-validation technique was implemented for model training and testing, and their performance was also evaluated on the validation dataset. Best models were implemented in the webserver AntiCP 2.0, which is freely available at https://webs.iiitd.edu.in/raghava/anticp2/. The webserver is compatible with multiple screens such as iPhone, iPad, laptop and android phones. The standalone version of the software is available at GitHub; docker-based container also developed.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining
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