Analyzing Effect of Multi-Modality in Predicting Protein-Protein Interactions.

Structural genomics provides an important approach for characterizing and understanding systems biology. As a step toward better integrating protein three-dimensional (3D) structural information in cancer systems biology, we have constructed a Human Cancer Pathway Protein Interaction Network (HCPIN) by analysis of several classical cancer-associated signaling pathways and their physical protein-protein interactions. Many well known cancer-associated proteins play central roles as "hubs" or "bottlenecks" in the HCPIN. At least half of HCPIN proteins are either directly associated with or interact with multiple signaling pathways. Although some 45% of residues in these proteins are in sequence segments that meet criteria sufficient for approximate homology modeling (Basic Local Alignment Search Tool (BLAST) E-value <10(-6)), only approximately 20% of residues in these proteins are structurally covered using high accuracy homology modeling criteria (i.e. BLAST E-value <10(-6) and at least 80% sequence identity) or by actual experimental structures. The HCPIN Website provides a comprehensive description of this biomedically important multipathway network together with experimental and homology models of HCPIN proteins useful for cancer biology research. To complement and enrich cancer systems biology, the Northeast Structural Genomics Consortium is targeting >1000 human proteins and protein domains from the HCPIN for sample production and 3D structure determination. The long range goal of this effort is to provide a comprehensive 3D structure-function database for human cancer-associated proteins and protein complexes in the context of their interaction networks. The network-based target selection (BioNet) approach described here is an example of a general strategy for targeting co-functioning proteins by structural genomics projects.

This paper is intended to explore the research done on identifying the diseased plants and crops using Machine Learning (ML) and Deep Learning (DL) techniques during last 10 years using bibliometric methods. In this study, we used Scopus database to analyze on “Plant disease” or “Crop disease” using “Machine Learning” or “Deep Learning” or “Neural Networks”. This paper focuses on the importance of ML and DL techniques in identifying plant or crop diseases. The database collected from the Scopus is analyzed using VOSviewer software of version 1.6.16. The study is limited to publications from conferences, journals with subject areas are limited to Computer Science, Engineering and languages limited to English and Chinese. Scopus search outputs 824 articles on Plant or Crop diseases with ML, DL and Neural Networks covering conference papers and journal articles. Statistics showed that more articles were published during the last five years and major contributions were from India. By analyzing database on Authors, Subject area, Keywords, Affiliation, Source type it is evident that there is plenty of research scope in this area. Network analysis on diverse parameters specifies that there is a good scope to do research in this topic using advanced deep learning techniques.

Protein interactions play an essential role in studying living systems and life phenomena. A considerable amount of literature has been published on analyzing and predicting protein interactions, such as support vector machine method, homology-based method and similarity-based method, each has its pros and cons. Most existing methods for predicting protein interactions require prior domain knowledge, making it difficult to effectively extract protein features. Single method is dissatisfactory in predicting protein interactions, declaring the need for a comprehensive method that combines the advantages of various methods. On this basis, a deep ensemble learning method called EnAmDNN (Ensemble Deep Neural Networks with Attention Mechanism) is proposed to predict protein interactions which is an appropriate candidate for comprehensive learning, combining multiple models, and considering the advantages of various methods. Particularly, it encode protein sequences by the local descriptor, auto covariance, conjoint triad, pseudo amino acid composition and combine the vector representation of each protein in the protein interaction network. Then it takes advantage of the multi-layer convolutional neural networks to automatically extract protein features and construct an attention mechanism to analyze deep-seated relationships between proteins. We set up four different structures of deep learning models. In the ensemble learning model, second layer data sets are generated with five-fold cross validation from basic learners, then predict the protein interaction network by combining 16 models. Results on five independent PPI data sets demonstrate that EnAmDNN achieves superior prediction performance than other comparing methods.

Background: The Mycobacterium tuberculosis complex (MTBC) bacteria include the slowly growing, host-associated bacteria Mycobacterium tuberculosis, Mycobacterium Bovis, Mycobacterium microti, Mycobacterium africanum, Mycobacterium pinnipedii. Aim: Comparative Functional Genomics Studies for understanding the Hypothetical Proteins in Mycobacterium tuberculosis variant microti 12. Objective: A computational genomics study was performed to understand the 247 hypothetical protein genes. Functional annotation of virtual proteins was performed on different servers to maximize confidence level. Methods: Sequence Retrieval. The whole genome sequences for the Mycobacterium tuberculosis micro variant 12 were retrieved from the KEGG database ( http://www.genome.jp/kegg/) and were used for screening 247 hypothetical proteins (Fig. 1 ). Functional Annotation and Sub-cellular localization. The Mycobacterium tuberculosis micro variant 12 hypothetical proteins were screened and sorted out from the genome and were individually analyzed for the presence of conserved functional domains by using computational biology tools like CDD-BLAST ( https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi) ;Pfam ( http://pfam.xfam.org/ncbiseq/398365647); The subcellular localization of hypothetical proteins was determined by CELLO2GO ( http://cello.life.nctu.edu.tw). These web tools can search the defined conserved domains in the sequences available in the online servers or databases and assist in the classification of proteins in the appropriate families. Protein Structure Prediction. The in-silico structure predictions of the hypothetical protein sequences showing functional properties were carried out by using the PS2 Protein Structure Prediction Server ( http://www.ps2.life.nctu.edu.tw/). The online server helps to generate the 3D structures of the hypothetical proteins. The server accepts the sequences in FASTA format as a query to generate resultant proteins 3D structures. The structure determination is completely based on the conserved template regions detected during functional annotations. Protein-protein interaction through String database: The interaction of each hypothetical protein analyzed for functional characteristics was subjected to a protein-protein interaction server for the prediction of a possible functional role in interaction amongst the available known proteins ( https://string-db.org/). This information can help us to further validated the functional role of such hypothetical proteins and their possible role in the Mycobacterium Tuberculosis micro variant. Protein secondary structure prediction through JPred4: The secondary structure prediction of all the hypothetical proteins was determined through JPred4 ( http://www.compbio.dundee.ac.uk/jpred4/index.html) and served to identify the available secondary structures in the unknown hypothetical protein sequences. These further help us to understand the available templates in the uncharacterized protein sequences for the prediction of novel functions associated with these proteins. The predictions were further characterized by the Phyre2 server for structural modeling and prediction of templates based on comparative analysis based on conserved domains. Protein modeling, prediction, and analysis through Phyre2. The hypothetical proteins which were identified to have functional properties were further characterized by the Phyre2 server ( http://www.sbg.bio.ic.ac.uk/phyre2) for structural modeling and prediction of templates based on comparative analysis based on conserved domains. Results: A computational genomics study was performed to understand the 247 hypothetical protein genes Functional annotation of virtual proteins, and was performed on different servers to maximize confidence level. The functional prediction was performed by CDD-Blast and Pfam. The gene sequences of proteins have probably been successfully functionally annotated, characterized, and their subcellular localization and 3-D structural predictions have been predicted computationally. Online automated bioinformatics tools such as CDD-Blast, Pfam, CELLO2GO and PS2-Server were used for the structural and functional characterization of screened hypothetical proteins. The structure, function, and subcellular localization of a hypothetical protein from Mycobacterium tuberculosis variant microti 12 have been obtained and presented (Fig. 2 ). Also, the three-dimensional structure generated after using the template with the highest score was displayed as the template ID in the structure column of the respective hypothetical protein. However, as systems biology denies hypothetical protein functions, the structures of such proteins can be tested through biological processes and experiments, making them suitable for understanding their role in the life cycle, pathogenesis, and drug development. We can further explore these predictive possibilities in pharmaceuticals, and other clinically relevant studies. This study by HP helped find structure-function relationships in Mycobacterium tuberculosis variant microti 12 using a variety of bioinformatics tools. The string database made predictions about protein-protein interactions and the template helped us predict a hypothetical protein structure and even helped us find its 3D protein structure. Protein profiling can be performed on structures retrieved from these servers. This is useful for proteomics studies, including protein-protein interactions, protein expression of specific hypothetical proteins, and post-translational modifications of protein-coding genes. Further understanding of these hypothetical proteins can help us to know more about the Mycobacterium tuberculosis complex (MTBC) and may assist in Drugs and inhibitors against different pathogens within this complex. Conclusion: The all-inclusive bioinformatic study has helped to functionally elucidate 247 hypothetical proteins, which have resulted and made it easier to understand many functional proteins available in the Mycobacterium tuberculosis micro variant 12. The subcellular localization of the 247 sorted hypothetical proteins was also carried &amp;amp; which further helped us understand the localization of identified enzymes or proteins. We have successfully characterized the 247 unknown proteins of hypothetical protein sequences from Mycobacterium tuberculosis micro variant 12 to validate their structure and functions of the gene products. These predicted functions and three-dimensional structures may lead to establishing their role in the life cycle of the bacterium. This computationally generated data can also be further used for developing new protocols for new vaccines against Mycobacterium tuberculosis micro variant 12 that are essential for preventing infection, diseases, and transmission. This complete result of Hypothetical Protein is needed for further studies of the whole genomic of the Mycobacterium Tuberculosis micro variant 12 for their function interpretation which further help in the understanding of its functions as well as structure. Moreover, this interpretation would help us to study the evolution of Mycobacterium Tuberculosis micro variant 12 which further helps in the process of discovering the drugs to inhibit the causes of diseases.

Proteins, considered fundamental building blocks of life, play an important role across various scientific domains, including novel protein design, protein structure prediction, and new drug discovery. Each of these fields necessitates the detailed analysis of a large number of protein structures. Traditionally, the determination of protein structures through biological and physical methods has been prohibitively expensive. Consequently, computational methods for predicting protein structures have gained significant traction. Such methods often generate multiple structural approximations, or decoys, which are then evaluated using protein quality assessment (QA) techniques to identify the most promising candidates for further research. This evaluation process, also known as estimation of model accuracy (EMA), is crucial for advancing our understanding of protein. The task of protein structure QA can be viewed as mapping a protein's structure to its evaluative score, a process increasingly addressed through deep learning technologies. These technologies, recognized for their powerful pattern learning capabilities, are at the forefront of tackling significant research challenges within this domain. This dissertation describes five major contributions to the field. Firstly, DeepRank2, a multimodel method, utilizes a 1D multi-layer perceptron to predict the GDT-TS score of tertiary protein structures. Secondly, the performance of DeepRank2 and its variants was assessed in the CASP14 category for model accuracy estimation. Thirdly, DISTEMA employs an attentive 2D convolutional neural network (CNN) that relies solely on distance-based features to predict the GDT-TS score of tertiary structures. Fourthly, DProQA introduces an innovative gated-graph transformer approach for evaluating protein quaternary structures. Lastly, this work includes a comprehensive survey on deep learning-based methods for quaternary protein structure QA, covering relevant datasets, evaluation metrics, protein representations, and advancements in deep learning techniques over the past five years. Both DISTEMA and DProQA have been released as open-source software and are available to the scientific community.

A systematic review of deep learning techniques for rice disease recognition: Current trends and future directions

This research explores the integration of deep learning techniques into medical imaging for early breast cancer detection. Focused on enhancing current methodologies, the study develops a specialized deep learning model using a diverse dataset of medical images. The primary objectives include evaluating the model's performance, identifying strengths and limitations, and addressing ethical considerations inherent in deploying such technology in healthcare. The findings offer significant implications for advancing early breast cancer detection, potentially revolutionizing diagnostic practices and improving patient outcomes. The study contributes to bridging existing gaps in the literature, providing novel insights into the potential of deep learning in the context of medical imaging. By examining the model's efficacy, ethical considerations, and its broader impact on healthcare, this research lays the foundation for further innovations in the critical intersection of artificial intelligence and early cancer diagnostics.

A novel entropy-based mapping method for determining the protein-protein interactions in viral genomes by using coevolution analysis

Objectives: To design a new framework to efficiently parallelize the steps of VLASPD algorithm using a hybridized apriori and fp-growth on GPU; to implement the existing and proposed framework in CUDA;to improve the performance factors like computational time, memory and CPU utilization.Methods/Statistical Analysis: This paper proposes the acceleration of Protein-Protein Interactions (PPIs) prediction on Graphics Processing Units(GPUs). A GPU can provide more processing cores and computational power in the same cost as a CPU.Findings: The frequently occurring patterns in the protein sequences can be used for PPIs prediction.The moving of the approaches from fixed length to variable length lead to computational complexity but also is found to be advantageous.Applications/Improvements:Sequence biology is since being researched by various computer engineers, the GPUs can be employed for predicting various sequence interactions like DNA-Proteins, etc. Since the GPU runs the parallel code efficiently, the methodology can be further improved if efficiently parallelized.

The detection of lung and colon cancer is a critical challenge in medical diagnosis, and machine learning (ML) and deep learning (DL) techniques are increasingly being used to enhance accuracy and efficiency. This review focuses on the integration of ML and DL methods for the combined detection of lung and colon cancer, emphasizing their strengths, limitations, and future potential. The motivation behind this study is to address the growing demand for accurate and early detection of these cancers, which significantly impacts treatment outcomes. Current methods often struggle with feature complexity, image variability, and computational intensity, which limit their real-world applicability. The aim is to consolidate various ML and DL techniques that have been employed for this purpose, highlighting how hybrid models can improve detection rates. The objective of this review is to provide a comprehensive analysis of different methodologies, their datasets, pre-processing techniques, feature extraction methods, and evaluation parameters. This review also explores recent advancements, such as transfer learning combined with fine-tuning techniques, which can further optimize performance in cancer detection. The findings suggest that while current methods show promise, further improvements in model generalization, interpretability, and computational efficiency are required to overcome existing limitations and expand clinical use.

Prediction of protein-protein interaction (PPI) is one of the most challenging problems in biology. Although great progress has been devoted to the development of methodology for predicting PPIs and PIN using machine learning methods, the problem is still far from being solved since the application of most existing methods is limited. In this study, we propose a method for PPI prediction based on amino acids differences between pairs of protein sequences. 10-fold cross-validation tests based on human PPI datasets with balanced positive-to-negative ratios indicate that it performs comparably well. Therefore, our finding suggests that amino acids differences of interacting protein pairs are relevant to the prediction of PPIs and hence provide important information on sequence-based encoding schemes.

Improved prediction of protein-protein interactions using novel negative samples, features, and an ensemble classifier.

Breast carcinoma stands as one of the most perilous afflictions affecting females, lacking an effective treatment to date. Recent advancements in deep learning techniques, coupled with artificial intelligence (AI), have demonstrated promising results in breast cancer identification. This innovation facilitates early detection, consequently enhancing patient survival rates. Deep learning necessitates minimal human intervention for feature extraction, contrasting with traditional machine learning methods. The ML and DL techniques are practised and comparison of all these techniques were shown. Specifically, emphasis is placed on genomic and histopathologic imaging data. Various algorithms, including R, SVM, logistic regression, KNN, Naïve Bayes, CNN, and ANN, are thoroughly researched and valuated to gauge their efficacy. Furthermore, many screening protocols were deployed to identify and examine the datasets. Lastly, the paper explores the challenges encountered and the most possible directions to detect the breast cancer .Hence researchers and clinicians with a thorough understanding and insights into this deep learning domain.

Advancements in deep learning techniques caused a paradigm shift in feature extraction for image perception from handcrafted methods to deep methods. However, these deep features if learned through unsupervised methods bear large memory footprints and are prone to the curse of dimensionality. Traditional feature reduction schemes involving aggregation of these learned visual descriptors may lead to loss of essential information necessary for their obvious discrimination. Therefore, this research studies various feature reduction techniques for remote sensing image features. We also propose an deep discriminative network with dimensionality reduction (DAE-DR), exploiting stacked autoencoder based solution to abbreviate unsupervised features without significantly affecting their discriminative and regenerative characteristics. It is observed that the spatial dimensions encoded in the feature vector are more important than increasing the number of network filters for efficient image reconstruction. Validation of our approach has been tested for remote sensing image retrieval (RSIR) problem. Results demonstrate that our proposed network achieves 25 times reduction in feature size with only 0.8 times depletion of retrieval score.

Plant diseases pose significant threats to agriculture, adversely affecting both crop yield and quality. This study offers a comprehensive overview of plant pathology, examining various types of diseases, their causative agents, and the intricate interactions between plants and pathogens. This study explored the integration of advanced deep learning and machine learning techniques. A dataset of plant leaf diseases, sourced from an online repository, was augmented with additional data featuring 11 West African plant species. The dataset underwent rigorous preprocessing to ensure compatibility with machine learning models. This study employed the ResNet50 Convolutional Neural Network (CNN) for feature extraction and XGBoost for classification, achieving a remarkable accuracy of 98.81% in differentiating between healthy and diseased plant leaves. The performance of the developed model was evaluated using key metrics, including accuracy, precision, recall, F1-score, confusion matrix, and ROC curve, and was found to outperform existing models in terms of accuracy. Furthermore, the model was successfully integrated into a mobile application, demonstrating efficient performance. This approach presents a scalable solution for precision agriculture, enhancing crop health management and boosting agricultural productivity.