StackACPred: Prediction of anticancer peptides by integrating optimized multiple feature descriptors with stacked ensemble approach

Arif, Muhammad; Ahmad, Saeed; Ge, Fang; Kabir, Muhammad; Khan, Yaser Daanial; Yu, Dong‐Jun; Thafar, Maha A.

doi:10.1016/j.chemolab.2021.104458

Cited by 43 publications

(22 citation statements)

References 63 publications

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“…Here, the common theme is that a problem is often tackled with different approaches that are then benchmarked against each other. Some of the most common and successful ML models used in peptide prediction are the support vector machines − and random forests (RFs). − Recently, deep learning-based approaches have become popular, − also for SP prediction. , A relevant aspect in predictive models is the description of a peptide or protein used to train them, which can be based on information about its physicochemical properties, ,, its sequence, and/or its structure . Such information can be further encoded and fed to the algorithm in different ways.…”

Section: Introductionmentioning

confidence: 99%

“…Common encoding methods include (pseudo-)amino acid composition 39,40,42−45 and positional matrices. 38,39 However, different models and feature-encoding methods may need to be applied, as each problem requires different and tailored combinations of tools. 4) First, all empty NLRs were identified and discarded; (5,6) second, occupied NLRs were sequentially sorted into 10 bins, based on their green to red signal ratio.…”

Section: ■ Introductionmentioning

confidence: 99%

“…Such information can be further encoded and fed to the algorithm in different ways. Common encoding methods include (pseudo-)amino acid composition ,,− and positional matrices. , However, different models and feature-encoding methods may need to be applied, as each problem requires different and tailored combinations of tools.…”

Section: Introductionmentioning

confidence: 99%

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Signal Peptide Efficiency: From High-Throughput Data to Prediction and Explanation

et al. 2023

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The passage of proteins across biological membranes via the general secretory (Sec) pathway is a universally conserved process with critical functions in cell physiology and important industrial applications. Proteins are directed into the Sec pathway by a signal peptide at their N-terminus. Estimating the impact of physicochemical signal peptide features on protein secretion levels has not been achieved so far, partially due to the extreme sequence variability of signal peptides. To elucidate relevant features of the signal peptide sequence that influence secretion efficiency, an evaluation of ∼12,000 different designed signal peptides was performed using a novel miniaturized high-throughput assay. The results were used to train a machine learning model, and a post-hoc explanation of the model is provided. By describing each signal peptide with a selection of 156 physicochemical features, it is now possible to both quantify feature importance and predict the protein secretion levels directed by each signal peptide. Our analyses allow the detection and explanation of the relevant signal peptide features influencing the efficiency of protein secretion, generating a versatile tool for the de novo design and in silico evaluation of signal peptides.

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Section: Introductionmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

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Signal Peptide Efficiency: From High-Throughput Data to Prediction and Explanation

et al. 2023

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“…All the above statistical functions, used for feature extraction, have specific biological significance. 33 – 44 These functions extract information related to the position and composition of DNA gene sequences. Feature extraction helps in extracting very useful information such as the frequency of each element in DNA gene sequences, position relative to the occurrence, composition of a specific gene, and absolute position of each element of gene sequences.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of deep learning techniques for identification of sarcoma-causing carcinogenic mutations

et al. 2022

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The abnormal growth of human healthy cells is called cancer. One of the major types of cancer is sarcoma, mostly found in human bones and soft tissue cells. It commonly occurs in children. According to a survey of the United States of America, there are more than 17,000 sarcoma patients registered each year which is 15% of all cancer cases. Recognition of cancer at its early stage saves many lives. The proposed study developed a framework for the early detection of human sarcoma cancer using deep learning Recurrent Neural Network (RNN) algorithms. The DNA of a human cell is made up of 25,000 to 30,000 genes. Each gene is represented by sequences of nucleotides. The nucleotides in a sequence of a driver gene can change which is termed as mutations. Some mutations can cause cancer. There are seven types of a gene whose mutation causes sarcoma cancer. The study uses the dataset which has been taken from more than 134 samples and includes 141 mutations in 8 driver genes. On these gene sequences RNN algorithms Long and Short-Term Memory (LSTM), Gated Recurrent Units and Bi-directional LSTM (Bi-LSTM) are used for training. Rigorous testing techniques such as Self-consistency testing, independent set testing, 10-fold cross-validation test are applied for the validation of results. These validation techniques yield several metrics such as Area Under the Curve (AUC), sensitivity, specificity, Mathew's correlation coefficient, loss, and accuracy. The proposed algorithm exhibits an accuracy of 99.6% with an AUC value of 1.00.

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“…GD is an optimization algorithm that is used to minimize the cost function and is very useful for MCC calculation. GD shows the best results in the analysis of data [ 24 , 36 ]. If the cost function of GD is [ 37 ]

then the mathematical equation of GD is [ 38 ]

…”

Section: Methodsmentioning

confidence: 99%

RCCC_Pred: A Novel Method for Sequence-Based Identification of Renal Clear Cell Carcinoma Genes through DNA Mutations and a Blend of Features

et al. 2022

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To save lives from cancer, it is very crucial to diagnose it at its early stages. One solution to early diagnosis lies in the identification of the cancer driver genes and their mutations. Such diagnostics can substantially minimize the mortality rate of this deadly disease. However, concurrently, the identification of cancer driver gene mutation through experimental mechanisms could be an expensive, slow, and laborious job. The advancement of computational strategies that could help in the early prediction of cancer growth effectively and accurately is thus highly needed towards early diagnoses and a decrease in the mortality rates due to this disease. Herein, we aim to predict clear cell renal carcinoma (RCCC) at the level of the genes, using the genomic sequences. The dataset was taken from IntOgen Cancer Mutations Browser and all genes’ standard DNA sequences were taken from the NCBI database. Using cancer-associated information of mutation from INTOGEN, the benchmark dataset was generated by creating the mutations in original sequences. After extensive feature extraction, the dataset was used to train ANN+ Hist Gradient boosting that could perform the classification of RCCC genes, other cancer-associated genes, and non-cancerous/unknown (non-tumor driver) genes. Through an independent dataset test, the accuracy observed was 83%, whereas the 10-fold cross-validation and Jackknife validation yielded 98% and 100% accurate results, respectively. The proposed predictor RCCC_Pred is able to identify RCCC genes with high accuracy and efficiency and can help scientists/researchers easily predict and diagnose cancer at its early stages.

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StackACPred: Prediction of anticancer peptides by integrating optimized multiple feature descriptors with stacked ensemble approach

Cited by 43 publications

References 63 publications

Signal Peptide Efficiency: From High-Throughput Data to Prediction and Explanation

Signal Peptide Efficiency: From High-Throughput Data to Prediction and Explanation

Evaluation of deep learning techniques for identification of sarcoma-causing carcinogenic mutations

RCCC_Pred: A Novel Method for Sequence-Based Identification of Renal Clear Cell Carcinoma Genes through DNA Mutations and a Blend of Features

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