iACP-MultiCNN: Multi-channel CNN based anticancer peptides identification

Aziz, Abu Zahid Bin; Hasan, Md. Al Mehedi; Ahmad, Shamim; Mamun, Md. Al; Shin, Jungpil; Hossain, Rahat

doi:10.1016/j.ab.2022.114707

Cited by 9 publications

(6 citation statements)

References 41 publications

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“…In Aziz et al (2022) , a novel multi-channel CNN is proposed for the identification of anticancer peptides (ACPs) from protein sequences. The data from state-of-the-art methodologies is collected and subjected to binary encoding for data preprocessing.…”

Section: Related Workmentioning

confidence: 99%

An efficient consolidation of word embedding and deep learning techniques for classifying anticancer peptides: FastText+BiLSTM

Karakaya,

Kilimci

2024

PeerJ Computer Science

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Anticancer peptides (ACPs) are a group of peptides that exhibit antineoplastic properties. The utilization of ACPs in cancer prevention can present a viable substitute for conventional cancer therapeutics, as they possess a higher degree of selectivity and safety. Recent scientific advancements generate an interest in peptide-based therapies which offer the advantage of efficiently treating intended cells without negatively impacting normal cells. However, as the number of peptide sequences continues to increase rapidly, developing a reliable and precise prediction model becomes a challenging task. In this work, our motivation is to advance an efficient model for categorizing anticancer peptides employing the consolidation of word embedding and deep learning models. First, Word2Vec, GloVe, FastText, One-Hot-Encoding approaches are evaluated as embedding techniques for the purpose of extracting peptide sequences. Then, the output of embedding models are fed into deep learning approaches CNN, LSTM, BiLSTM. To demonstrate the contribution of proposed framework, extensive experiments are carried on widely-used datasets in the literature, ACPs250 and independent. Experiment results show the usage of proposed model enhances classification accuracy when compared to the state-of-the-art studies. The proposed combination, FastText+BiLSTM, exhibits 92.50% of accuracy for ACPs250 dataset, and 96.15% of accuracy for the Independent dataset, thence determining new state-of-the-art.

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Section: Related Workmentioning

confidence: 99%

An efficient consolidation of word embedding and deep learning techniques for classifying anticancer peptides: FastText+BiLSTM

Karakaya,

Kilimci

2024

PeerJ Computer Science

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“…Residue-level feature (AAC, Pse-AAC, and autocorrelation) and peptide-level features (BPF + evolutionary based features and protein encoding physiochemical features) were used with the sequential convolution module followed by two parallel paths of residual modules and the Bi-LSTM module to predict ACPs in the ME-ACP method . The CNN-based iACP-Multichannel method improved the prediction of ACPs, where peptides are represented using one hot embedding (OHE) . Finally, the CNN-based embedding model (meta-convolution model (MLACP 2.0)) is upgraded MLACP, which defines peptides using AAC, blossom matrix, DPC, k-spaced AAC, z -scale features, CTD, g -gap DPC, EAAC, QSO, and Seq2Vec to predict ACPs …”

Section: Ai Models For Acpsmentioning

confidence: 99%

Advancing Peptide-Based Cancer Therapy with AI: In-Depth Analysis of State-of-the-Art AI Models

Bhattarai,

Tayara,

Chong

2024

J. Chem. Inf. Model.

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Anticancer peptides (ACPs) play a vital role in selectively targeting and eliminating cancer cells. Evaluating and comparing predictions from various machine learning (ML) and deep learning (DL) techniques is challenging but crucial for anticancer drug research. We conducted a comprehensive analysis of 15 ML and 10 DL models, including the models released after 2022, and found that support vector machines (SVMs) with feature combination and selection significantly enhance overall performance. DL models, especially convolutional neural networks (CNNs) with light gradient boosting machine (LGBM) based feature selection approaches, demonstrate improved characterization. Assessment using a new test data set (ACP10) identifies ACPred, MLACP 2.0, AI4ACP, mACPred, and AntiCP2.0_AAC as successive optimal predictors, showcasing robust performance. Our review underscores current prediction tool limitations and advocates for an omnidirectional ACP prediction framework to propel ongoing research.

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“…Machine learning (ML)-based algorithms are becoming increasingly prominent in various healthcare sectors [17]- [22], serving purposes such as supporting clinical diagnosis, classifying disease stages, and predicting clinical outcomes [23], [24]. ML-based methods have emerged as robust approaches for identifying complex data patterns, automating data analysis, and making inferences/classifications related to diseases, including PD [25], [26].…”

Section: Introductionmentioning

confidence: 99%

Classification of Hand-Movement Disabilities in Parkinson’s Disease Using a Motion-Capture Device and Machine Learning

Shin,

Matsumoto,

Maniruzzaman

et al. 2024

IEEE Access

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P ARKINSON'S disease (PD) is a neurological disorder caused by degeneration of dopaminergic neurons in the midbrain. PD patients mainly suffer from motor symptoms, which significantly impact their daily lives. The diagnostic criteria for PD include the presence of muscle rigidity, tremor, and postural reflex disturbances. The Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is the standard tool for evaluating PD symptoms, part III of which is dedicated to motor symptoms. That part involves a comprehensive set of specific physical examinations, and physicians assign semi quantitative scores from 0 to 4. However, this approach faces notable challenges, including the requirement for movement-disorder experts proficient in using MDS-UPDRS and the presence of substantial inter rater variability even among experts. Overcoming these challenges requires a quantitative and objective

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iACP-MultiCNN: Multi-channel CNN based anticancer peptides identification

Cited by 9 publications

References 41 publications

An efficient consolidation of word embedding and deep learning techniques for classifying anticancer peptides: FastText+BiLSTM

An efficient consolidation of word embedding and deep learning techniques for classifying anticancer peptides: FastText+BiLSTM

Advancing Peptide-Based Cancer Therapy with AI: In-Depth Analysis of State-of-the-Art AI Models

Classification of Hand-Movement Disabilities in Parkinson’s Disease Using a Motion-Capture Device and Machine Learning

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