MPPIF-Net: Identification of Plasmodium Falciparum Parasite Mitochondrial Proteins Using Deep Features with Multilayer Bi-directional LSTM

AI-powered microscopy image analysis for parasitology: integrating human expertise

Microscopy image analysis plays a pivotal role in parasitology research. Deep learning (DL), a subset of artificial intelligence (AI), has garnered significant attention. However, traditional DL-based methods for general purposes are data-driven, often lacking explainability due to their black-box nature and sparse instructional resources. To address these challenges, this article presents a comprehensive review of recent advancements in knowledge-integrated DL models tailored for microscopy image analysis in parasitology. The massive amounts of human expert knowledge from parasitologists can enhance the accuracy and explainability of AI-driven decisions. It is expected that the adoption of knowledge-integrated DL models will open up a wide range of applications in the field of parasitology.

Prediction of submitochondrial proteins localization based on Gene Ontology

Mitochondria, which are double-membrane bound organelles commonly found in eukaryotic cells, play a fundamental role as sites for cellular energy production. Within the mitochondria, there exist substructures called submitochondria, and specific proteins associated with submitochondria have been implicated in various human diseases. Therefore, comprehending the precise localization of these submitochondrial proteins is of utmost importance. Such knowledge not only aids in unraveling their role in the pathogenesis of diseases but also facilitates the development of therapeutic drugs and diagnostic methods. In this study, we proposed a novel method based on Gene Ontology (GO) to predict the localization of the submitochondrial proteins, called GO-Submito. More specifically, the GO-Submito fine-tuned pre-training Bidirectional Encoder Representations from Transformers models to encode GO annotations into vectors. Subsequently, the Multi-head Attention Mechanism was employed to fuse these encoded vectors of GO annotations, enabling precise localization prediction. Through comprehensive evaluation, our results demonstrated that GO-Submito outperforms existing methods, offering a reliable and efficient tool for precisely localizing submitochondrial proteins.

Identification of cancer types from gene expressions using learning techniques

Tumor is the major cause of death all around the world in recent days. Early detection and prediction of a cancer type are important for a patient's well-being. Functional genomic data has recently been used in the effective and early detection of cancer. According to previous research, the use of microarray data in cancer prediction has evidenced two main problems as high dimensionality and limited sample size. Several researchers have used numerous statistical and machine learning-based methods to classify cancer types but still, limitations are there which makes cancer classification a difficult job. Deep Learning (DL) and Convolutional Neural Networks (CNN) have been proven with effective analyses of unstructured data including gene expression data. In the proposed method gene expression data for five types of cancer is collected from The Cancer Genome Atlas (TCGA). Prominent features are selected using a hybrid Particle Swarm Optimization (PSO) and Random Forest (RF) algorithm followed by the use of Principal Component Analysis (PCA) for dimensionality reduction. Finally, for classification blend of Convolutional Neural Network (CNN) and Bi-directional Long Short Term Memory (Bi-LSTM) is used to predict the target type of cancer. Experimental results demonstrate that accuracy of the proposed method is 96.89%. As compared to existing work, our method outperformed with better results.

Weighted entropy deep features on hybrid RNN with LSTM for glucose level and diabetes prediction

Glucose level regulation with essential advice regarding diabetes must be provided to the patients to maintain their diet for diabetes treatment. Therefore, the academic community has focused on implementing novel glucose prediction techniques for decision support systems. Recent computational techniques for diagnosing diabetes have certain limitations, and also they are not evaluated under various datasets obtained from the different people of various countries. This generates inefficiency in the prediction systems to apply it in real-time applications. This paper plans to suggest a hybrid deep learning model for diabetes prediction and glucose level classification. Two benchmark datasets are used in the data collection process for experimenting. Initially, the deep selected features were extracted by the Convolutional Neural Network (CNN). Further, weighted entropy deep features are extracted, where the tuning of weight is taken place by the Modified Escaping Energy-based Harris Hawks Optimization. These features are processed in the glucose level classification using the modified Fuzzy classifier for classifying the high-level and low-level glucose. Further, glucose prediction is done by the Hybrid Recurrent Neural Network (RNN), and Long Short Term Memory (LSTM) termed R-LSTM with parameter optimization. From the experimental result, In the dataset 2 analyses on SMAPE, the MEE-HHO-R-LSTM is 12.5%, 87.5%, 50%, 12.5%, and 2.5% better than SVM, LSTM, DNN, RNN, and RNN-LSTM, at the learning percentage of 75%. The analytical results enforce that the suggested methods attain enhanced prediction performance concerning the evaluation metrics compared to conventional prediction models.

A novel deep learning-assisted hybrid network for plasmodium falciparum parasite mitochondrial proteins classification

Plasmodium falciparum is a parasitic protozoan that can cause malaria, which is a deadly disease. Therefore, the accurate identification of malaria parasite mitochondrial proteins is essential for understanding their functions and identifying novel drug targets. For classifying protein sequences, several adaptive statistical techniques have been devised. Despite significant gains, prediction performance is still constrained by the lack of appropriate feature descriptors and learning strategies in current systems. Moreover, good ground truth data is important for Artificial Intelligence (AI)-based models but there is a lack of that data in the literature. Therefore, in this work, we propose a novel hybrid network that combines 1D Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (BGRU) to classify the malaria parasite mitochondrial proteins. Furthermore, we curate a sequential data that are collected from National Center for Biotechnology Information (NCBI) and UniProtKB/Swiss-Prot proteins databanks to prepare a dataset that can be used by the research community for AI-based algorithms evaluation. We obtain 4204 cases after preprocessing of the collected data and denote this set of proteins as PF4204. Finally, we conduct an ablation study on several conventional and deep models using PF4204 and the benchmark PF2095 datasets. The proposed model 'CNN-BGRU' obtains the accuracy values of 0.9096 and 0.9857 on PF4204 and PF2095 datasets, respectively. In addition, the CNN-BGRU is compared with state-of-the-arts, where the results illustrate that it can extract robust features and identify proteins accurately.

To Assist Oncologists: An Efficient Machine Learning-Based Approach for Anti-Cancer Peptides Classification

In the modern technological era, Anti-cancer peptides (ACPs) have been considered a promising cancer treatment. It’s critical to find new ACPs to ensure a better knowledge of their functioning processes and vaccine development. Thus, timely and efficient ACPs using a computational technique are highly needed because of the enormous peptide sequences generated in the post-genomic era. Recently, numerous adaptive statistical algorithms have been developed for separating ACPs and NACPs. Despite great advancements, existing approaches still have insufficient feature descriptors and learning methods, limiting predictive performance. To address this, a trustworthy framework is developed for the precise identification of ACPs. Particularly, the presented approach incorporates four hypothetical feature encoding mechanisms namely: amino acid, dipeptide, tripeptide, and an improved version of pseudo amino acid composition are applied to indicate the motif of the target class. Moreover, principal component analysis (PCA) is employed for feature pruning, while selecting optimal, deep, and highly variated features. Due to the diverse nature of learning, experiments are performed over numerous algorithms to select the optimum operating method. After investigating the empirical outcomes, the support vector machine with hybrid feature space shows better performance. The proposed framework achieved an accuracy of 97.09% and 98.25% over the benchmark and independent datasets, respectively. The comparative analysis demonstrates that our proposed model outperforms as compared to the existing methods and is beneficial in drug development, and oncology.

NIFtHool: an informatics program for identification of NifH proteins using deep neural networks

Atmospheric nitrogen fixation carried out by microorganisms has environmental and industrial importance, related to the increase of soil fertility and productivity. The present work proposes the development of a new high precision system that allows the recognition of amino acid sequences of the nitrogenase enzyme (NifH) as a promising way to improve the identification of diazotrophic bacteria. For this purpose, a database obtained from UniProt built a processed dataset formed by a set of 4911 and 4782 amino acid sequences of the NifH and non-NifH proteins respectively. Subsequently, the feature extraction was developed using two methodologies: (i) k-mers counting and (ii) embedding layers to obtain numerical vectors of the amino acid chains. Afterward, for the embedding layer, the data was crossed by an external trainable convolutional layer, which received a uniform matrix and applied convolution using filters to obtain the feature maps of the model. Finally, a deep neural network was used as the primary model to classify the amino acid sequences as NifH protein or not. Performance evaluation experiments were carried out, and the results revealed an accuracy of 96.4%, a sensitivity of 95.2%, and a specificity of 96.7%. Therefore, an amino acid sequence-based feature extraction method that uses a neural network to detect N-fixing organisms is proposed and implemented. NIFtHool is available from: https://nifthool.anvil.app/

Human Activity Recognition via Hybrid Deep Learning Based Model

In recent years, Human Activity Recognition (HAR) has become one of the most important research topics in the domains of health and human-machine interaction. Many Artificial intelligence-based models are developed for activity recognition; however, these algorithms fail to extract spatial and temporal features due to which they show poor performance on real-world long-term HAR. Furthermore, in literature, a limited number of datasets are publicly available for physical activities recognition that contains less number of activities. Considering these limitations, we develop a hybrid model by incorporating Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for activity recognition where CNN is used for spatial features extraction and LSTM network is utilized for learning temporal information. Additionally, a new challenging dataset is generated that is collected from 20 participants using the Kinect V2 sensor and contains 12 different classes of human physical activities. An extensive ablation study is performed over different traditional machine learning and deep learning models to obtain the optimum solution for HAR. The accuracy of 90.89% is achieved via the CNN-LSTM technique, which shows that the proposed model is suitable for HAR applications.

Atrous Convolutions and Residual GRU Based Architecture for Matching Power Demand with Supply

Nowadays, for efficient energy management, local demand-supply matching in power grid is emerging research domain. However, energy demand is increasing day by day in many countries due to rapid growth of the population and most of their work being reliant on electronic devices. This problem has highlighted the significance of effectively matching power demand with supply for optimal energy management. To resolve this issue, we present an intelligent deep learning framework that integrates Atrous Convolutional Layers (ACL) with Residual Gated Recurrent Units (RGRU) to establish balance between the demand and supply. Moreover, it accurately predicts short-term energy and delivers a systematic method of communication between consumers and energy distributors as well. To cope with the varying nature of electricity data, first data acquisition step is performed where data are collected from various sources such as smart meters and solar plants. In the second step a pre-processing method is applied on raw data to normalize and clean the data. Next, the refined data are passed to ACL for spatial feature extraction. Finally, a sequential learning model RGRU is used that learns from complicated patterns for the final output. The proposed model obtains the smallest values of Mean Square Error (MSE) including 0.1753, 0.0001, 0.0177 over IHEPC, KCB, and Solar datasets, respectively, which manifests better performance as compared to existing approaches.

iDeepSubMito: identification of protein submitochondrial localization with deep learning

Mitochondria are membrane-bound organelles containing over 1000 different proteins involved in mitochondrial function, gene expression and metabolic processes. Accurate localization of those proteins in the mitochondrial compartments is critical to their operation. A few computational methods have been developed for predicting submitochondrial localization from the protein sequences. Unfortunately, most of these computational methods focus on employing biological features or evolutionary information to extract sequence features, which greatly limits the performance of subsequent identification. Moreover, the efficiency of most computational models is still under explored, especially the deep learning feature, which is promising but requires improvement. To address these limitations, we propose a novel computational method called iDeepSubMito to predict the location of mitochondrial proteins to the submitochondrial compartments. First, we adopted a coding scheme using the ProteinELMo to model the probability distribution over the protein sequences and then represent the protein sequences as continuous vectors. Then, we proposed and implemented convolutional neural network architecture based on the bidirectional LSTM with self-attention mechanism, to effectively explore the contextual information and protein sequence semantic features. To demonstrate the effectiveness of our proposed iDeepSubMito, we performed cross-validation on two datasets containing 424 proteins and 570 proteins respectively, and consisting of four different mitochondrial compartments (matrix, inner membrane, outer membrane and intermembrane regions). Experimental results revealed that our method outperformed other computational methods. In addition, we tested iDeepSubMito on the M187, M983 and MitoCarta3.0 to further verify the efficiency of our method. Finally, the motif analysis and the interpretability analysis were conducted to reveal novel insights into subcellular biological functions of mitochondrial proteins. iDeepSubMito source code is available on GitHub at https://github.com/houzl3416/iDeepSubMito.

Towards Efficient Building Designing: Heating and Cooling Load Prediction via Multi-Output Model

In the current technological era, energy-efficient buildings have a significant research body due to increasing concerns about energy consumption and its environmental impact. Designing an appropriate energy-efficient building depends on its layout, such as relative compactness, overall area, height, orientation, and distribution of the glazing area. These factors directly influence the cooling load (CL) and heating load (HL) of residential buildings. An accurate prediction of these load facilitates a better management of energy consumption and enhances the living standards of inhabitants. Most of the traditional machine learning (ML)-based approaches are designed for single-output (SO) prediction, which is a tedious task due to separate training processes for each output with low performance. In addition, these approaches have a high level of nonlinearity between input and output, which need more enhancement in terms of robustness, predictability, and generalization. To tackle these issues, we propose a novel framework based on gated recurrent unit (GRU) that reliably predicts the CL and HL concurrently. To the best of our knowledge, we are the first to propose a multi-output (MO) sequential learning model followed by utility preprocessing under the umbrella of a unified framework. A comprehensive set of ablation studies on ML and deep learning (DL) techniques is done over an energy efficiency dataset, where the proposed model reveals an incredible performance as compared to other existing models.