Evaluating machine learning methodologies for identification of cancer driver genes

Drivergene.net: A Cytoscape app for the identification of driver nodes of large-scale complex networks and case studies in discovery of drug target genes

There are no tools to identify driver nodes of large-scale networks in approach of competition-based controllability. This study proposed a novel method for this computation of large-scale networks. It implemented the method in a new Cytoscape plug-in app called Drivergene.net. Experiments of the software on large-scale biomolecular networks have shown outstanding speed and computing power. Interestingly, 86.67% of the top 10 driver nodes found on these networks are anticancer drug target genes that reside mostly at the innermost K-cores of the networks. Finally, compared method with those of five other researchers and confirmed that the proposed method outperforms the other methods on identification of anticancer drug target genes. Taken together, Drivergene.net is a reliable tool that efficiently detects not only drug target genes from biomolecular networks but also driver nodes of large-scale complex networks. Drivergene.net with a user manual and example datasets are available https://github.com/tinhpd/Drivergene.git.

An intelligent model for prediction of abiotic stress-responsive microRNAs in plants using statistical moments based features and ensemble approaches

This study proposed an intelligent model for predicting abiotic stress-responsive microRNAs in plants. MicroRNAs (miRNAs) are short RNA molecules regulates the stress in genes. Experimental methods are costly and time-consuming, as compare to in-silico prediction. Addressing this gap, the study seeks to develop an efficient computational model for plant stress response prediction. The two benchmark datasets for MiRNA and Pre-MiRNA dataset have been acquired in this study. Four ensemble approaches such as bagging, boosting, stacking, and blending have been employed. Classifiers such as Random Forest (RF), Extra Trees (ET), Ada Boost (ADB), Light Gradient Boosting Machine (LGBM), and Support Vector Machine (SVM). Stacking and Blending employed all stated classifiers as base learners and Logistic Regression (LR) as Meta Classifier. There have been a total of four types of testing used, including independent set, self-consistency, cross-validation with 5 and 10 folds, and jackknife. This study has utilized evaluation metrics such as accuracy score, specificity, sensitivity, Mathew's correlation coefficient (MCC), and AUC. Our proposed methodology has outperformed existing state of the art study in both datasets based on independent set testing. The SVM-based approach has exhibited accuracy score of 0.659 for the MiRNA dataset, which is better than the previous study. The ET classifier has surpassed the accuracy of Pre-MiRNA dataset as compared to the existing benchmark study, achieving an impressive score of 0.67. The proposed method can be used in future research to predict abiotic stresses in plants.

DEL-Thyroid: deep ensemble learning framework for detection of thyroid cancer progression through genomic mutation

Genes, expressed as sequences of nucleotides, are susceptible to mutations, some of which can lead to cancer. Machine learning and deep learning methods have emerged as vital tools in identifying mutations associated with cancer. Thyroid cancer ranks as the 5th most prevalent cancer in the USA, with thousands diagnosed annually. This paper presents an ensemble learning model leveraging deep learning techniques such as Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), and Bi-directional LSTM (Bi-LSTM) to detect thyroid cancer mutations early. The model is trained on a dataset sourced from asia.ensembl.org and IntOGen.org, consisting of 633 samples with 969 mutations across 41 genes, collected from individuals of various demographics. Feature extraction encompasses techniques including Hahn moments, central moments, raw moments, and various matrix-based methods. Evaluation employs three testing methods: self-consistency test (SCT), independent set test (IST), and 10-fold cross-validation test (10-FCVT). The proposed ensemble learning model demonstrates promising performance, achieving 96% accuracy in the independent set test (IST). Statistical measures such as training accuracy, testing accuracy, recall, sensitivity, specificity, Mathew's Correlation Coefficient (MCC), loss, training accuracy, F1 Score, and Cohen's kappa are utilized for comprehensive evaluation.

TOP1 and R-loops facilitate transcriptional DSBs at hypertranscribed cancer driver genes

DNA double-stranded breaks (DSBs) pose a significant threat to genomic integrity, and their generation during essential cellular processes like transcription remains poorly understood. In this study, we employ several techniques to map DSBs, R-loops, and topoisomerase 1 cleavage complex (TOP1cc) to comprehensively investigate the interplay between transcription, DSBs, topoisomerase 1 (TOP1), and R-loops. Our findings reveal the presence of DSBs at highly expressed genes enriched with TOP1 and R-loops. Remarkably, transcription-associated DSBs at these loci are significantly reduced upon depletion of R-loops and TOP1, uncovering the pivotal roles of TOP1 and R-loops in transcriptional DSB formation. By elucidating the intricate interplay between TOP1cc trapping, R-loops, and DSBs, our study provides insights into the mechanisms underlying transcription-associated genomic instability. Moreover, we establish a link between transcriptional DSBs and early molecular changes driving cancer development, highlighting the distinct etiology and molecular characteristics of driver mutations compared to passenger mutations.

Gsw-fi: a GLM model incorporating shrinkage and double-weighted strategies for identifying cancer driver genes with functional impact

BACKGROUND

Cancer, a disease with high morbidity and mortality rates, poses a significant threat to human health. Driver genes, which harbor mutations accountable for the initiation and progression of tumors, play a crucial role in cancer development. Identifying driver genes stands as a paramount objective in cancer research and precision medicine.

RESULTS

In the present work, we propose a method for identifying driver genes using a Generalized Linear Regression Model (GLM) with Shrinkage and double-Weighted strategies based on Functional Impact, which is named GSW-FI. Firstly, an estimating model is proposed for assessing the background functional impacts of genes based on GLM, utilizing gene features as predictors. Secondly, the shrinkage and double-weighted strategies as two revising approaches are integrated to ensure the rationality of the identified driver genes. Lastly, a statistical method of hypothesis testing is designed to identify driver genes by leveraging the estimated background function impacts. Experimental results conducted on 31 The Cancer Genome Altas datasets demonstrate that GSW-FI outperforms ten other prediction methods in terms of the overlap fraction with well-known databases and consensus predictions among different methods.

CONCLUSIONS

GSW-FI presents a novel approach that efficiently identifies driver genes with functional impact mutations using computational methods, thereby advancing the development of precision medicine for cancer.

m1A-Ensem: accurate identification of 1-methyladenosine sites through ensemble models

BACKGROUND

1-methyladenosine (m1A) is a variant of methyladenosine that holds a methyl substituent in the 1st position having a prominent role in RNA stability and human metabolites.

OBJECTIVE

Traditional approaches, such as mass spectrometry and site-directed mutagenesis, proved to be time-consuming and complicated.

METHODOLOGY

The present research focused on the identification of m1A sites within RNA sequences using novel feature development mechanisms. The obtained features were used to train the ensemble models, including blending, boosting, and bagging. Independent testing and k-fold cross validation were then performed on the trained ensemble models.

RESULTS

The proposed model outperformed the preexisting predictors and revealed optimized scores based on major accuracy metrics.

CONCLUSION

For research purpose, a user-friendly webserver of the proposed model can be accessed through https://taseersuleman-m1a-ensem1.streamlit.app/ .

An ensemble-based deep learning model for detection of mutation causing cutaneous melanoma

When the mutation affects the melanocytes of the body, a condition called melanoma results which is one of the deadliest skin cancers. Early detection of cutaneous melanoma is vital for raising the chances of survival. Melanoma can be due to inherited defective genes or due to environmental factors such as excessive sun exposure. The accuracy of the state-of-the-art computer-aided diagnosis systems is unsatisfactory. Moreover, the major drawback of medical imaging is the shortage of labeled data. Generalized classifiers are required to diagnose melanoma to avoid overfitting the dataset. To address these issues, blending ensemble-based deep learning (BEDLM-CMS) model is proposed to detect mutation of cutaneous melanoma by integrating long short-term memory (LSTM), Bi-directional LSTM (BLSTM) and gated recurrent unit (GRU) architectures. The dataset used in the proposed study contains 2608 human samples and 6778 mutations in total along with 75 types of genes. The most prominent genes that function as biomarkers for early diagnosis and prognosis are utilized. Multiple extraction techniques are used in this study to extract the most-prominent features. Afterwards, we applied different DL models optimized through grid search technique to diagnose melanoma. The validity of the results is confirmed using several techniques, including tenfold cross validation (10-FCVT), independent set (IST), and self-consistency (SCT). For validation of the results multiple metrics are used which include accuracy, specificity, sensitivity, and Matthews's correlation coefficient. BEDLM gives the highest accuracy of 97% in the independent set test whereas in self-consistency test and tenfold cross validation test it gives 94% and 93% accuracy, respectively. Accuracy of in self-consistency test, independent set test, and tenfold cross validation test is LSTM (96%, 94%, 92%), GRU (93%, 94%, 91%), and BLSTM (99%, 98%, 93%), respectively. The findings demonstrate that the proposed BEDLM-CMS can be used effectively applied for early diagnosis and treatment efficacy evaluation of cutaneous melanoma.

Advances in computational methods for identifying cancer driver genes

Cancer driver genes (CDGs) are crucial in cancer prevention, diagnosis and treatment. This study employed computational methods for identifying CDGs, categorizing them into four groups. The major frameworks for each of these four categories were summarized. Additionally, we systematically gathered data from public databases and biological networks, and we elaborated on computational methods for identifying CDGs using the aforementioned databases. Further, we summarized the algorithms, mainly involving statistics and machine learning, used for identifying CDGs. Notably, the performances of nine typical identification methods for eight types of cancer were compared to analyze the applicability areas of these methods. Finally, we discussed the challenges and prospects associated with methods for identifying CDGs. The present study revealed that the network-based algorithms and machine learning-based methods demonstrated superior performance.

BBB-PEP-prediction: improved computational model for identification of blood-brain barrier peptides using blending position relative composition specific features and ensemble modeling

BBPs have the potential to facilitate the delivery of drugs to the brain, opening up new avenues for the development of treatments targeting diseases of the central nervous system (CNS). The obstacle faced in central nervous system disorders stems from the formidable task of traversing the blood-brain barrier (BBB) for pharmaceutical agents. Nearly 98% of small molecule-based drugs and nearly 100% of large molecule-based drugs encounter difficulties in successfully penetrating the BBB. This importance leads to identification of these peptides, can help in healthcare systems. In this study, we proposed an improved intelligent computational model BBB-PEP-Prediction for identification of BBB peptides. Position and statistical moments based features have been computed for acquired benchmark dataset. Four types of ensembles such as bagging, boosting, stacking and blending have been utilized in the methodology section. Bagging employed Random Forest (RF) and Extra Trees (ET), Boosting utilizes XGBoost (XGB) and Light Gradient Boosting Machine (LGBM). Stacking uses ET and XGB as base learners, blending exploited LGBM and RF as base learners, while Logistic Regression (LR) has been applied as Meta learner for stacking and blending. Three classifiers such as LGBM, XGB and ET have been optimized by using Randomized search CV. Four types of testing such as self-consistency, independent set, cross-validation with 5 and 10 folds and jackknife test have been employed. Evaluation metrics such as Accuracy (ACC), Specificity (SPE), Sensitivity (SEN), Mathew's correlation coefficient (MCC) have been utilized. The stacking of classifiers has shown best results in almost each testing. The stacking results for independent set testing exhibits accuracy, specificity, sensitivity and MCC score of 0.824, 0.911, 0.831 and 0.663 respectively. The proposed model BBB-PEP-Prediction shown superlative performance as compared to previous benchmark studies. The proposed system helps in future research and research community for in-silico identification of BBB peptides.

Application of Machine Learning in Predicting Hepatic Metastasis or Primary Site in Gastroenteropancreatic Neuroendocrine Tumors

Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) account for 80% of gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs). GEP-NETs are well-differentiated tumors, highly heterogeneous in biology and origin, and are often diagnosed at the metastatic stage. Diagnosis is commonly through clinical symptoms, histopathology, and PET-CT imaging, while molecular markers for metastasis and the primary site are unknown. Here, we report the identification of multi-gene signatures for hepatic metastasis and primary sites through analyses on RNA-SEQ datasets of pancreatic and small intestinal NETs tissue samples. Relevant gene features, identified from the normalized RNA-SEQ data using the mRMRe algorithm, were used to develop seven Machine Learning models (LDA, RF, CART, k-NN, SVM, XGBOOST, GBM). Two multi-gene random forest (RF) models classified primary and metastatic samples with 100% accuracy in training and test cohorts and >90% accuracy in an independent validation cohort. Similarly, three multi-gene RF models identified the pancreas or small intestine as the primary site with 100% accuracy in training and test cohorts, and >95% accuracy in an independent cohort. Multi-label models for concurrent prediction of hepatic metastasis and primary site returned >98.42% and >87.42% accuracies on training and test cohorts, respectively. A robust molecular signature to predict liver metastasis or the primary site for GEP-NETs is reported for the first time and could complement the clinical management of GEP-NETs.

PseU-pred: An ensemble model for accurate identification of pseudouridine sites

Pseudouridine (ψ) is reported to occur frequently in all types of RNA. This uridine modification has been shown to be essential for processes such as RNA stability and stress response. Also, it is linked to a few human diseases, such as prostate cancer, anemia, etc. A few laboratory techniques, such as Pseudo-seq and N3-CMC-enriched Pseudouridine sequencing (CeU-Seq) are used for detecting ψ sites. However, these are laborious and drawn-out methods. The convenience of sequencing data has enabled the development of computationally intelligent models for improving ψ site identification methods. The proposed work provides a prediction model for the identification of ψ sites through popular ensemble methods such as stacking, bagging, and boosting. Features were obtained through a novel feature extraction mechanism with the assimilation of statistical moments, which were used to train ensemble models. The cross-validation test and independent set test were used to evaluate the precision of the trained models. The proposed model outperformed the preexisting predictors and revealed 87% accuracy, 0.90 specificity, 0.85 sensitivity, and a 0.75 Matthews correlation coefficient. A web server has been built and is available publicly for the researchers at https://taseersuleman-y-test-pseu-pred-c2wmtj.streamlit.app/.

Ensemble Learning for Hormone Binding Protein Prediction: A Promising Approach for Early Diagnosis of Thyroid Hormone Disorders in Serum

Hormone-binding proteins (HBPs) are specific carrier proteins that bind to a given hormone. A soluble carrier hormone binding protein (HBP), which can interact non-covalently and specifically with growth hormone, modulates or inhibits hormone signaling. HBP is essential for the growth of life, despite still being poorly understood. Several diseases, according to some data, are caused by HBPs that express themselves abnormally. Accurate identification of these molecules is the first step in investigating the roles of HBPs and understanding their biological mechanisms. For a better understanding of cell development and cellular mechanisms, accurate HBP determination from a given protein sequence is essential. Using traditional biochemical experiments, it is difficult to correctly separate HBPs from an increasing number of proteins because of the high experimental costs and lengthy experiment periods. The abundance of protein sequence data that has been gathered in the post-genomic era necessitates a computational method that is automated and enables quick and accurate identification of putative HBPs within a large number of candidate proteins. A brand-new machine-learning-based predictor is suggested as the HBP identification method. To produce the desirable feature set for the method proposed, statistical moment-based features and amino acids were combined, and the random forest was used to train the feature set. During 5-fold cross validation experiments, the suggested method achieved 94.37% accuracy and 0.9438 F1-scores, respectively, demonstrating the importance of the Hahn moment-based features.

DriverGenePathway: Identifying driver genes and driver pathways in cancer based on MutSigCV and statistical methods

Although computational methods for driver gene identification have progressed rapidly, it is far from the goal of obtaining widely recognized driver genes for all cancer types. The driver gene lists predicted by these methods often lack consistency and stability across different studies or datasets. In addition to analytical performance, some tools may require further improvement regarding operability and system compatibility. Here, we developed a user-friendly R package (DriverGenePathway) integrating MutSigCV and statistical methods to identify cancer driver genes and pathways. The theoretical basis of the MutSigCV program is elaborated and integrated into DriverGenePathway, such as mutation categories discovery based on information entropy. Five methods of hypothesis testing, including the beta-binomial test, Fisher combined p-value test, likelihood ratio test, convolution test, and projection test, are used to identify the minimal core driver genes. Moreover, de novo methods, which can effectively overcome mutational heterogeneity, are introduced to identify driver pathways. Herein, we describe the computational structure and statistical fundamentals of the DriverGenePathway pipeline and demonstrate its performance using eight types of cancer from TCGA. DriverGenePathway correctly confirms many expected driver genes with high overlap with the Cancer Gene Census list and driver pathways associated with cancer development. The DriverGenePathway R package is freely available on GitHub: https://github.com/bioinformatics-xu/DriverGenePathway.

EDLM: Ensemble Deep Learning Model to Detect Mutation for the Early Detection of Cholangiocarcinoma

The most common cause of mortality and disability globally right now is cholangiocarcinoma, one of the worst forms of cancer that may affect people. When cholangiocarcinoma develops, the DNA of the bile duct cells is altered. Cholangiocarcinoma claims the lives of about 7000 individuals annually. Women pass away less often than men. Asians have the greatest fatality rate. Following Whites (20%) and Asians (22%), African Americans (45%) saw the greatest increase in cholangiocarcinoma mortality between 2021 and 2022. For instance, 60-70% of cholangiocarcinoma patients have local infiltration or distant metastases, which makes them unable to receive a curative surgical procedure. Across the board, the median survival time is less than a year. Many researchers work hard to detect cholangiocarcinoma, but this is after the appearance of symptoms, which is late detection. If cholangiocarcinoma progression is detected at an earlier stage, then it will help doctors and patients in treatment. Therefore, an ensemble deep learning model (EDLM), which consists of three deep learning algorithms-long short-term model (LSTM), gated recurrent units (GRUs), and bi-directional LSTM (BLSTM)-is developed for the early identification of cholangiocarcinoma. Several tests are presented, such as a 10-fold cross-validation test (10-FCVT), an independent set test (IST), and a self-consistency test (SCT). Several statistical techniques are used to evaluate the proposed model, such as accuracy (Acc), sensitivity (Sn), specificity (Sp), and Matthew's correlation coefficient (MCC). There are 672 mutations in 45 distinct cholangiocarcinoma genes among the 516 human samples included in the proposed study. The IST has the highest Acc at 98%, outperforming all other validation approaches.

LRT-CLUSTER: A New Clustering Algorithm Based on Likelihood Ratio Test to Identify Driving Genes

Somatic mutations often occur at high relapse sites in protein sequences, which indicates that the location clustering of somatic missense mutations can be used to identify driving genes. However, the traditional clustering algorithm has such problems as the background signal over-fitting, the clustering algorithm is not suitable for mutation data, and the performance of identifying low-frequency mutation genes needs to be improved. In this paper, we propose a linear clustering algorithm based on likelihood ratio test knowledge to identify driver genes. In this experiment, firstly, the polynucleotide mutation rate is calculated based on the prior knowledge of likelihood ratio test. Then, the simulation data set is obtained through the background mutation rate model. Finally, the unsupervised peak clustering algorithm is used to, respectively, evaluate the somatic mutation data and the simulation data to identify the driver genes. The experimental results show that our method achieves a better balance of precision and sensitivity. It can also identify the driver genes missed by other methods, making it an effective supplement to other methods. We also discover some potential linkages between genes and between genes and mutation sites, which is of great value to target drug therapy research. Method framework: Our proposed model framework is as follows. a. Counting mutation sites and the number of mutations in tumor gene elements. b. The nucleotide context mutation frequency is counted based on the likelihood ratio test knowledge, and the background mutation rate model is obtained. c. Based on Monte Carlo simulation method, data sets with the same number of mutations as gene elements are randomly sampled to obtain simulated mutation data, and the sampling frequency of each mutation site is related to the mutation rate of polynucleotide. d. The original mutation data and the simulated mutation data after random reconstruction are clustered by peak density, respectively, and the corresponding clustering scores are obtained. e. We can obtain the clustering information statistics in each gene segment and score of each gene segment from the original single nucleotide mutation data through step d. f. According to the observed score and the simulated clustering score, the p-value of the corresponding gene fragment is calculated. g. We can obtain the clustering information statistics in each gene segment and score of each gene segment from the simulated single nucleotide mutation data through step d.

IGPred-HDnet: Prediction of Immunoglobulin Proteins Using Graphical Features and the Hierarchal Deep Learning-Based Approach

Motivation. Immunoglobulin proteins (IGP) (also called antibodies) are glycoproteins that act as B-cell receptors against external or internal antigens like viruses and bacteria. IGPs play a significant role in diverse cellular processes ranging from adhesion to cell recognition. IGP identifications via the in-silico approach are faster and more cost-effective than wet-lab technological methods. Methods. In this study, we developed an intelligent theoretical deep learning framework, "IGPred-HDnet" for the discrimination of IGPs and non-IGPs. Three types of promising descriptors are feature extraction based on graphical and statistical features (FEGS), amphiphilic pseudo-amino acid composition (Amp-PseAAC), and dipeptide composition (DPC) to extract the graphical, physicochemical, and sequential features. Next, the extracted attributes are evaluated through machine learning, i.e., decision tree (DT), support vector machine (SVM), k-nearest neighbour (KNN), and hierarchical deep network (HDnet) classifiers. The proposed predictor IGPred-HDnet was trained and tested using a 10-fold cross-validation and independent test. Results and Conclusion. The success rates in terms of accuracy (ACC) and Matthew's correlation coefficient (MCC) of IGPred-HDnet on training and independent dataset (Dtrain Dtest) are ACC = 98.00%, 99.10%, and MCC = 0.958, and 0.980 points, respectively. The empirical outcomes demonstrate that the IGPred-HDnet model efficacy on both datasets using the novel FEGS feature and HDnet algorithm achieved superior predictions to other existing computational models. We hope this research will provide great insights into the large-scale identification of IGPs and pharmaceutical companies in new drug design.

iDHU-Ensem: Identification of dihydrouridine sites through ensemble learning models

Background

Dihydrouridine (D) is one of the most significant uridine modifications that have a prominent occurrence in eukaryotes. The folding and conformational flexibility of transfer RNA (tRNA) can be attained through this modification.

Objective

The modification also triggers lung cancer in humans. The identification of D sites was carried out through conventional laboratory methods; however, those were costly and time-consuming. The readiness of RNA sequences helps in the identification of D sites through computationally intelligent models. However, the most challenging part is turning these biological sequences into distinct vectors.

Methods

The current research proposed novel feature extraction mechanisms and the identification of D sites in tRNA sequences using ensemble models. The ensemble models were then subjected to evaluation using k-fold cross-validation and independent testing.

Results

The results revealed that the stacking ensemble model outperformed all the ensemble models by revealing 0.98 accuracy, 0.98 specificity, 0.97 sensitivity, and 0.92 Matthews Correlation Coefficient. The proposed model, iDHU-Ensem, was also compared with pre-existing predictors using an independent test. The accuracy scores have shown that the proposed model in this research study performed better than the available predictors.

Conclusion

The current research contributed towards the enhancement of D site identification capabilities through computationally intelligent methods. A web-based server, iDHU-Ensem, was also made available for the researchers at https://taseersuleman-idhu-ensem-idhu-ensem.streamlit.app/.

Hemolytic-Pred: A machine learning-based predictor for hemolytic proteins using position and composition-based features

Objective

The objective of this study is to propose a novel in-silico method called Hemolytic-Pred for identifying hemolytic proteins based on their sequences, using statistical moment-based features, along with position-relative and frequency-relative information.

Methods

Primary sequences were transformed into feature vectors using statistical and position-relative moment-based features. Varying machine learning algorithms were employed for classification. Computational models were rigorously evaluated using four different validation. The Hemolytic-Pred webserver is available for further analysis at http://ec2-54-160-229-10.compute-1.amazonaws.com/.

Results

XGBoost outperformed the other six classifiers with an accuracy value of 0.99, 0.98, 0.97, and 0.98 for self-consistency test, 10-fold cross-validation, Jackknife test, and independent set test, respectively. The proposed method with the XGBoost classifier is a workable and robust solution for predicting hemolytic proteins efficiently and accurately.

Conclusions

The proposed method of Hemolytic-Pred with XGBoost classifier is a reliable tool for the timely identification of hemolytic cells and diagnosis of various related severe disorders. The application of Hemolytic-Pred can yield profound benefits in the medical field.

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

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.

m1A-pred: Prediction of Modified 1-methyladenosine Sites in RNA Sequences through Artificial Intelligence

BACKGROUND

The process of nucleotides modification or methyl groups addition to nucleotides is known as post-transcriptional modification (PTM). 1-methyladenosine (m1A) is a type of PTM formed by adding a methyl group to the nitrogen at the 1st position of the adenosine base. Many human disorders are associated with m1A, which is widely found in ribosomal RNA and transfer RNA.

OBJECTIVE

The conventional methods such as mass spectrometry and site-directed mutagenesis proved to be laborious and burdensome. Systematic identification of modified sites from RNA sequences is gaining much attention nowadays. Consequently, an extreme gradient boost predictor, m1A-Pred, is developed in this study for the prediction of modified m1A sites.

METHODS

The current study involves the extraction of position and composition-based properties within nucleotide sequences. The extraction of features helps in the development of the features vector. Statistical moments were endorsed for dimensionality reduction in the obtained features.

RESULTS

Through a series of experiments using different computational models and evaluation methods, it was revealed that the proposed predictor, m1A-pred, proved to be the most robust and accurate model for the identification of modified sites.

AVAILABILITY AND IMPLEMENTATION

To enhance the research on m¹A sites, a friendly server was also developed, which was the final phase of this research.

DHU-Pred: accurate prediction of dihydrouridine sites using position and composition variant features on diverse classifiers

Background

Dihydrouridine (D) is a modified transfer RNA post-transcriptional modification (PTM) that occurs abundantly in bacteria, eukaryotes, and archaea. The D modification assists in the stability and conformational flexibility of tRNA. The D modification is also responsible for pulmonary carcinogenesis in humans.

Objective

For the detection of D sites, mass spectrometry and site-directed mutagenesis have been developed. However, both are labor-intensive and time-consuming methods. The availability of sequence data has provided the opportunity to build computational models for enhancing the identification of D sites. Based on the sequence data, the DHU-Pred model was proposed in this study to find possible D sites.

Methodology

The model was built by employing comprehensive machine learning and feature extraction approaches. It was then validated using in-demand evaluation metrics and rigorous experimentation and testing approaches.

Results

The DHU-Pred revealed an accuracy score of 96.9%, which was considerably higher compared to the existing D site predictors.

Availability and Implementation

A user-friendly web server for the proposed model was also developed and is freely available for the researchers.

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

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.

Deep Learning Approaches for Detection of Breast Adenocarcinoma Causing Carcinogenic Mutations

Genes are composed of DNA and each gene has a specific sequence. Recombination or replication within the gene base ends in a permanent change in the nucleotide collection in a DNA called mutation and some mutations can lead to cancer. Breast adenocarcinoma starts in secretary cells. Breast adenocarcinoma is the most common of all cancers that occur in women. According to a survey within the United States of America, there are more than 282,000 breast adenocarcinoma patients registered each 12 months, and most of them are women. Recognition of cancer in its early stages saves many lives. A proposed framework is developed for the early detection of breast adenocarcinoma using an ensemble learning technique with multiple deep learning algorithms, specifically: Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Bi-directional LSTM. There are 99 types of driver genes involved in breast adenocarcinoma. This study uses a dataset of 4127 samples including men and women taken from more than 12 cohorts of cancer detection institutes. The dataset encompasses a total of 6170 mutations that occur in 99 genes. On these gene sequences, different algorithms are applied for feature extraction. Three types of testing techniques including independent set testing, self-consistency testing, and a 10-fold cross-validation test is applied to validate and test the learning approaches. Subsequently, multiple deep learning approaches such as LSTM, GRU, and bi-directional LSTM algorithms are applied. Several evaluation metrics are enumerated for the validation of results including accuracy, sensitivity, specificity, Mathew’s correlation coefficient, area under the curve, training loss, precision, recall, F1 score, and Cohen’s kappa while the values obtained are 99.57, 99.50, 99.63, 0.99, 1.0, 0.2027, 99.57, 99.57, 99.57, and 99.14 respectively.

An analytical study on the identification of N-linked glycosylation sites using machine learning model

N-linked is the most common type of glycosylation which plays a significant role in identifying various diseases such as type I diabetes and cancer and helps in drug development. Most of the proteins cannot perform their biological and psychological functionalities without undergoing such modification. Therefore, it is essential to identify such sites by computational techniques because of experimental limitations. This study aims to analyze and synthesize the progress to discover N-linked places using machine learning methods. It also explores the performance of currently available tools to predict such sites. Almost seventy research articles published in recognized journals of the N-linked glycosylation field have shortlisted after the rigorous filtering process. The findings of the studies have been reported based on multiple aspects: publication channel, feature set construction method, training algorithm, and performance evaluation. Moreover, a literature survey has developed a taxonomy of N-linked sequence identification. Our study focuses on the performance evaluation criteria, and the importance of N-linked glycosylation motivates us to discover resources that use computational methods instead of the experimental method due to its limitations.

Machine learning techniques for identification of carcinogenic mutations, which cause breast adenocarcinoma

Breast adenocarcinoma is the most common of all cancers that occur in women. According to the United States of America survey, more than 282,000 breast cancer patients are registered each year; most of them are women. Detection of cancer at its early stage saves many lives. Each cell contains the genetic code in the form of gene sequences. Changes in the gene sequences may lead to cancer. Replication and/or recombination in the gene base sometimes lead to a permanent change in the nucleotide sequence of the genome, called a mutation. Cancer driver mutations can lead to cancer. The proposed study develops a framework for the early detection of breast adenocarcinoma using machine learning techniques. Every gene has a specific sequence of nucleotides. A total of 99 genes are identified in various studies whose mutations can lead to breast adenocarcinoma. This study uses the dataset taken from 4127 human samples, including men and women from more than 12 cohorts. A total of 6170 mutations in gene sequences are used in this study. Decision Tree, Random Forest, and Gaussian Naïve Bayes are applied to these gene sequences using three evaluation methods: independent set testing, self-consistency testing, and tenfold cross-validation testing. Evaluation metrics such as accuracy, specificity, sensitivity, and Mathew’s correlation coefficient are calculated. The decision tree algorithm obtains the best accuracy of 99% for each evaluation method.

DNAPred_Prot: Identification of DNA-Binding Proteins Using Composition- and Position-Based Features

In the domain of genome annotation, the identification of DNA-binding protein is one of the crucial challenges. DNA is considered a blueprint for the cell. It contained all necessary information for building and maintaining the trait of an organism. It is DNA, which makes a living thing, a living thing. Protein interaction with DNA performs an essential role in regulating DNA functions such as DNA repair, transcription, and regulation. Identification of these proteins is a crucial task for understanding the regulation of genes. Several methods have been developed to identify the binding sites of DNA and protein depending upon the structures and sequences, but they were costly and time-consuming. Therefore, we propose a methodology named “DNAPred_Prot”, which uses various position and frequency-dependent features from protein sequences for efficient and effective prediction of DNA-binding proteins. Using testing techniques like 10-fold cross-validation and jackknife testing an accuracy of 94.95% and 95.11% was yielded, respectively. The results of SVM and ANN were also compared with those of a random forest classifier. The robustness of the proposed model was evaluated by using the independent dataset PDB186, and an accuracy of 91.47% was achieved by it. From these results, it can be predicted that the suggested methodology performs better than other extant methods for the identification of DNA-binding proteins.

EPIMUTESTR: a nearest neighbor machine learning approach to predict cancer driver genes from the evolutionary action of coding variants

Discovering rare cancer driver genes is difficult because their mutational frequency is too low for statistical detection by computational methods. EPIMUTESTR is an integrative nearest-neighbor machine learning algorithm that identifies such marginal genes by modeling the fitness of their mutations with the phylogenetic Evolutionary Action (EA) score. Over cohorts of sequenced patients from The Cancer Genome Atlas representing 33 tumor types, EPIMUTESTR detected 214 previously inferred cancer driver genes and 137 new candidates never identified computationally before of which seven genes are supported in the COSMIC Cancer Gene Census. EPIMUTESTR achieved better robustness and specificity than existing methods in a number of benchmark methods and datasets.

Analyses of human cancer driver genes uncovers evolutionarily conserved RNA structural elements involved in posttranscriptional control

Experimental breakthroughs have provided unprecedented insights into the genes involved in cancer. The identification of such cancer driver genes is a major step in gaining a fuller understanding of oncogenesis and provides novel lists of potential therapeutic targets. A key area that requires additional study is the posttranscriptional control mechanisms at work in cancer driver genes. This is important not only for basic insights into the biology of cancer, but also to advance new therapeutic modalities that target RNA—an emerging field with great promise toward the treatment of various cancers. In the current study we performed an in silico analysis on the transcripts associated with 800 cancer driver genes (10,390 unique transcripts) that identified 179,190 secondary structural motifs with evidence of evolutionarily ordered structures with unusual thermodynamic stability. Narrowing to one transcript per gene, 35,426 predicted structures were subjected to phylogenetic comparisons of sequence and structural conservation. This identified 7,001 RNA secondary structures embedded in transcripts with evidence of covariation between paired sites, supporting structure models and suggesting functional significance. A select set of seven structures were tested in vitro for their ability to regulate gene expression; all were found to have significant effects. These results indicate potentially widespread roles for RNA structure in posttranscriptional control of human cancer driver genes.

LBCEPred: a machine learning model to predict linear B-cell epitopes

B-cell epitopes have the capability to recognize and attach to the surface of antigen receptors to stimulate the immune system against pathogens. Identification of B-cell epitopes from antigens has a great significance in several biomedical and biotechnological applications, provides support in the development of therapeutics, design and development of an epitope-based vaccine and antibody production. However, the identification of epitopes with experimental mapping approaches is a challenging job and usually requires extensive laboratory efforts. However, considerable efforts have been placed for the identification of epitopes using computational methods in the recent past but deprived of considerable achievements. In this study, we present LBCEPred, a python-based web-tool (http://lbcepred.pythonanywhere.com/), build with random forest classifier and statistical moment-based descriptors to predict the B-cell epitopes from the protein sequences. LBECPred outperforms all sequence-based available models that are currently in use for the B-cell epitopes prediction, with 0.868 accuracy value and 0.934 area under the curve. Moreover, the prediction performance of proposed models compared to other state-of-the-art models is 56.3% higher on average for Mathews Correlation Coefficient. LBCEPred is easy to use tool even for novice users and has also shown the models stability and reliability, thus we believe in its significant contribution to the research community and the area of bioinformatics.

ORI-Deep: improving the accuracy for predicting origin of replication sites by using a blend of features and long short-term memory network

Replication of DNA is an important process for the cell division cycle, gene expression regulation and other biological evolution processes. It also has a crucial role in a living organism's physical growth and structure. Replication of DNA comprises of three stages known as initiation, elongation and termination, whereas the origin of replication sites (ORI) is the location of initiation of the DNA replication process. There exist various methodologies to identify ORIs in the genomic sequences, however, these methods have used either extensive computations for execution, or have limited optimization for the large datasets. Herein, a model called ORI-Deep is proposed to identify ORIs from the multiple cell type genomic sequence benchmark data. An efficient method is proposed using a deep neural network to identify ORIs for four different eukaryotic species. For better representation of data, a feature vector is constructed using statistical moments for the training and testing of data and is further fed to a long short-term memory (LSTM) network. To prove the effectiveness of the proposed model, we applied several validation techniques at different levels to obtain seven accuracy metrics, and the accuracy score for self-consistency, 10-fold cross-validation, jackknife and the independent set test is observed to be 0.977, 0.948, 0.976 and 0.977, respectively. Based on the results, it can be concluded that ORI-Deep can efficiently predict the sites of origin replication in DNA sequence with high accuracy. Webserver for ORI-Deep is available at (https://share.streamlit.io/waqarhusain/orideep/main/app.py), whereas source code is available at (https://github.com/WaqarHusain/OriDeep).

A comprehensive tool for accurate identification of methyl-Glutamine sites

Methylation is a biochemical process involved in nearly all of the human body functions. Glutamine is considered an indispensable amino acid that is susceptible to methylation via post-translational modification (PTM). Modern research has proved that methylation plays a momentous role in the progression of most types of cancers. Therefore, there is a need for an effective method to predict glutamine sites vulnerable to methylation accurately and inexpensively. The motive of this study is the formulation of an accurate method that could predict such sites with high accuracy. Various computationally intelligent classifiers were employed for their formulation and evaluation. Rigorous validations prove that deep learning performs best as compared to other classifiers. The accuracy (ACC) and the area under the receiver operating curve (AUC) obtained by 10-fold cross-validation was 0.962 and 0.981, while with the jackknife testing, it was 0.968 and 0.980, respectively. From these results, it is concluded that the proposed methodology works sufficiently well for the prediction of methyl-glutamine sites. The webserver's code, developed for the prediction of methyl-glutamine sites, is freely available at https://github.com/s20181080001/WebServer.git. The code can easily be set up by any intermediate-level Python user.

Identification of stress response proteins through fusion of machine learning models and statistical paradigms

Proteins are a vital component of cells that perform physiological functions to ensure smooth operations of bodily functions. Identification of a protein's function involves a detailed understanding of the structure of proteins. Stress proteins are essential mediators of several responses to cellular stress and are categorized based on their structural characteristics. These proteins are found to be conserved across many eukaryotic and prokaryotic linkages and demonstrate varied crucial functional activities inside a cell. The in-vivo, ex vivo, and in-vitro identification of stress proteins are a time-consuming and costly task. This study is aimed at the identification of stress protein sequences with the aid of mathematical modelling and machine learning methods to supplement the aforementioned wet lab methods. The model developed using Random Forest showed remarkable results with 91.1% accuracy while models based on neural network and support vector machine showed 87.7% and 47.0% accuracy, respectively. Based on evaluation results it was concluded that random-forest based classifier surpassed all other predictors and is suitable for use in practical applications for the identification of stress proteins. Live web server is available at http://biopred.org/stressprotiens , while the webserver code available is at https://github.com/abdullah5naveed/SRP_WebServer.git.

iTAGPred: A Two-Level Prediction Model for Identification of Angiogenesis and Tumor Angiogenesis Biomarkers

A crucial biological process called angiogenesis plays a vital role in migration, growth, and wound healing of endothelial cells and other processes that are controlled by chemical signals. Angiogenesis is the process that controls the growth of blood vessels within tissues while angiogenesis proteins play a significant role in the proper working of this process. The balancing of these signals is necessary for the proper working of angiogenesis. Unbalancing of these signals increases blood vessel formation, which causes abnormal growth or several diseases including cancer. The proposed work focuses on developing a two-layered prediction model using different classifiers like random forest (RF), neural network, and support vector machine. The first level performs in silico identification of angiogenesis proteins based on the primary structure. In the case the protein is an angiogenesis protein, then the second level predicts whether the protein is linked with tumor angiogenesis or not. The performance of the model is evaluated through various validation techniques. The model was evaluated using k-fold cross-validation, independent, self-consistency, and jackknife testing. The overall accuracy using an RF classifier for angiogenesis at the first level was 97.8% and for tumor angiogenesis at the second level was 99.5%, ANN showed 94.1% accuracy for angiogenesis and 79.9% for tumor angiogenesis, and the accuracy of SVM for angiogenesis was 78.8% and for tumor angiogenesis was 65.19%.