Field Of Computational Biology Research Articles

DDREL: From drug-drug relationships to drug repurposing

Analyzing the relationships among various drugs is an essential issue in the field of computational biology. Different kinds of informative knowledge, such as drug repurposing, can be extracted from drug-drug relationships. Scientific literature represents a rich source for the retrieval of knowledge about the relationships between biological concepts, mainly drug-drug, disease-disease, and drug-disease relationships. In this paper, we propose DDREL as a general-purpose method that applies deep learning on scientific literature to automatically extract the graph of syntactic and semantic relationships among drugs. DDREL remarkably outperforms the existing human drug network method and a random network respected to average similarities of drugs’ anatomical therapeutic chemical (ATC) codes. DDREL is able to shed light on the existing deficiency of the ATC codes in various drug groups. From the DDREL graph, the history of drug discovery became visible. In addition, drugs that had repurposing score 1 (diflunisal, pargyline, fenofibrate, guanfacine, chlorzoxazone, doxazosin, oxymetholone, azathioprine, drotaverine, demecarium, omifensine, yohimbine) were already used in additional indication. The proposed DDREL method justifies the predictive power of textual data in PubMed abstracts. DDREL shows that such data can be used to 1- Predict repurposing drugs with high accuracy, and 2- Reveal existing deficiencies of the ATC codes in various drug groups.

Multivariate Data Exploration Through Coordinated Views

Many fields of study still face the challenges inherent to the analysis of complex multidimensional datasets, such as the field of computational biology, whose research of infectious diseases must contend with large protein-protein interaction networks with thousands of genes that vary in expression values over time. In this paper, we explore the visualization of multivariate data through CroP, a data visualization tool with a coordinated multiple views framework where users can adapt the workspace to different problems through flexible panels. In particular, we focus on the visualization of relational and temporal data, the latter being represented through layouts that distort timelines to represent the fluctuations of values across complex datasets, creating visualizations that highlight significant events and patterns. Moreover, CroP provides various layouts and functionalities to not only highlight relationships between different variables, but also dig-down into discovered patterns in order to better understand their sources and their effects. These methods are demonstrated through multiple experiments with diverse multivariate datasets, with a focus on gene expression time-series datasets. In addition to a discussion of our results, we also validate CroP through model and interface tests performed with participants from both the fields of information visualization and computational biology.

Combination of classical and statistical approaches to enhance the fermentation conditions and increase the yield of Lipopeptide(s) by Pseudomonas sp. OXDC12: its partial purification and determining antifungal property.

Around 200 different lipopeptides (LPs) have been identified to date, most of which are produced via Bacillus and Pseudomonas species. The clinical nature of the lipopeptide (LP) has led to a big surge in its research. They show antimicrobial and antitumor activities due to which mass-scale production and purification of LPs are beneficial. Response surface methodology (RSM) approach has emerged as an alternative in the field of computational biology for optimizing the reaction parameters using statistical models. In the present study, Pseudomonas sp. strain OXDC12 was used for production and partial purification of LPs using Thin Layer Chromatography (TLC). The main goal of the study was to increase the overall yield of LPs by optimizing the different variables in the fermentation broth. This was achieved using a combination of one factor at a time (OFAT) and response surface methodology (RSM) approaches. OFAT technique was used to optimize the necessary parameters and was followed by the creation of statistical models (RSM) to optimize the remaining variables. Maximum mycelial growth inhibition (%) against the fungus Mucor sp. was 61.3% for LP. Overall, the combination of both OFAT and RSM helped in increasing the LPs yield by 3 folds from 367mg/L to 1169mg/L.

The Design and Implementation of an Improved Lightweight BLASTP on CUDA GPU

In the field of computational biology, sequence alignment is a very important methodology. BLAST is a very common tool for performing sequence alignment in bioinformatics provided by National Center for Biotechnology Information (NCBI) in the USA. The BLAST server receives tens of thousands of queries every day on average. Among the procedures of BLAST, the hit detection process whose core architecture is a lookup table is the most time-consuming. In the latest work, a lightweight BLASTP on CUDA GPU with a hybrid query-index table was proposed for servicing the sequence query length shorter than 512, which effectively improved the query efficiency. According to the reported protein sequence length distribution, about 90% of sequences are equal to or smaller than 1024. In this paper, we propose an improved lightweight BLASTP to speed up the hit detection time for longer query sequences. The largest sequence is enlarged from 512 to 1024. As a result, one more bit is required to encode each sequence position. To meet the requirement, an extended hybrid query-index table (EHQIT) is proposed to accommodate three sequence positions in a four-byte table entry, making only one memory access sufficient to retrieve all the position information as long as the number of hits is equal to or smaller than three. Moreover, if there are more than three hits for a possible word, all the position information will be stored in contiguous table entries, which eliminates branch divergence and reduces memory space for pointers to overflow buffer. A square symmetric scoring matrix, Blosum62, is used to determine the relative score made by matching two characters in a sequence alignment. The experimental results show that for queries shorter than 512 our improved lightweight BLASTP outperforms the original lightweight BLASTP with speedups of 1.2 on average. When the number of hit overflows increases, the speedup can be as high as two. For queries shorter than 1024, our improved lightweight BLASTP can provide speedups ranging from 1.56 to 3.08 over the CUDA-BLAST. In short, the improved lightweight BLASTP can replace the original one because it can support a longer query sequence and provide better performance.

Big Data and its Future in Computational Biology: A Literature Review

Big data technologies are used majorly for computational biology research nowadays due to the emerge of massive amounts of biological data that have been generated and collected at an unprecedented speed and scale and with different structures. As an example, the processing of billions of DNA sequence data per day represents the new generation of sequencing technologies. It is expected that the cost of acquiring and analyzing biomedical data will decrease with the assistance of technology upgrades in the computational biology field. The application of big data in health care is a fast-growing field, with many new discoveries and methodologies. In this study, an overview of recent developments and technologies in big data in the computational biology field. Moreover, in this study, we summarize and highlight the challenges, gaps and opportunities to improve and advance big data applications in computational biology. Also, this review presents a useful starting point for the application of Big Data in future Bioinformatics research. Finally, some of the Big Data sources in computational biology that help in applying these new emerging technologies are mentioned.

ISFMDA: Learning Interactions of Selected Features-Based Method for Predicting Potential MicroRNA-Disease Associations

Prediction of potential microRNA-disease associations is one of the important tasks in computational biology fields. Mining more sophisticated features can improve the performance of the prediction methods. This article proposes a novel algorithm (ISFMDA) that can effectively learn low- or high-order interactions of recursive feature elimination selected features by an extreme gradient boosting, a factorization machine, and a deep neural network. As a result, ISFMDA can obtain an area under receiver operating characteristic curve (AUROC) of 0.9342 ± 0.0007 in fivefold cross-validation tests with 51.25% of original features, which verifies the effectiveness of the methods.

An algorithmic approach to determine expertise development using object-related gaze pattern sequences

Eye tracking (ET) technology is increasingly utilized to quantify visual behavior in the study of the development of domain-specific expertise. However, the identification and measurement of distinct gaze patterns using traditional ET metrics has been challenging, and the insights gained shown to be inconclusive about the nature of expert gaze behavior. In this article, we introduce an algorithmic approach for the extraction of object-related gaze sequences and determine task-related expertise by investigating the development of gaze sequence patterns during a multi-trial study of a simplified airplane assembly task. We demonstrate the algorithm in a study where novice (n = 28) and expert (n = 2) eye movements were recorded in successive trials (n = 8), allowing us to verify whether similar patterns develop with increasing expertise. In the proposed approach, AOI sequences were transformed to string representation and processed using the k-mer method, a well-known method from the field of computational biology. Our results for expertise development suggest that basic tendencies are visible in traditional ET metrics, such as the fixation duration, but are much more evident for k-mers of k > 2. With increased on-task experience, the appearance of expert k-mer patterns in novice gaze sequences was shown to increase significantly (p < 0.001). The results illustrate that the multi-trial k-mer approach is suitable for revealing specific cognitive processes and can quantify learning progress using gaze patterns that include both spatial and temporal information, which could provide a valuable tool for novice training and expert assessment.

Uncovering Membrane-Bound Models of Coagulation Factors by Combined Experimental and Computational Approaches.

In the life sciences, including hemostasis and thrombosis, methods of structural biology have become indispensable tools for shedding light on underlying mechanisms that govern complex biological processes. Advancements of the relatively young field of computational biology have matured to a point where it is increasingly recognized as trustworthy and useful, in part due to their high space–time resolution that is unparalleled by most experimental techniques to date. In concert with biochemical and biophysical approaches, computational studies have therefore proven time and again in recent years to be key assets in building or suggesting structural models for membrane-bound forms of coagulation factors and their supramolecular complexes on membrane surfaces where they are activated. Such endeavors and the proposed models arising from them are of fundamental importance in describing and understanding the molecular basis of hemostasis under both health and disease conditions. We summarize the body of work done in this important area of research to drive forward both experimental and computational studies toward new discoveries and potential future therapeutic strategies.

JCB Call for Papers for the Special Issue on “40 Years of Computational Biology: In Honor of Professor Michael Waterman's 80th Birthday”

<i>JCB</i> Call for Papers for the Special Issue on “40 Years of Computational Biology: In Honor of Professor Michael Waterman's 80th Birthday”

A Fractional Approach to a Computational Eco-Epidemiological Model with Holling Type-II Functional Response

Eco-epidemiological can be considered as a significant combination of two research fields of computational biology and epidemiology. These problems mainly take ecological systems into account of the impact of epidemiological factors. In this paper, we examine the chaotic nature of a computational system related to the spread of disease into a specific environment involving a novel differential operator called the Atangana–Baleanu fractional derivative. To approximate the solutions of this fractional system, an efficient numerical method is adopted. The numerical method is an implicit approximate method that can provide very suitable numerical approximations for fractional problems due to symmetry. Symmetry is one of the distinguishing features of this technique compared to other methods in the literature. Through considering different choices of parameters in the model, several meaningful numerical simulations are presented. It is clear that hiring a new derivative operator greatly increases the flexibility of the model in describing the different scenarios in the model. The results of this paper can be very useful help for decision-makers to describe the situation related to the problem, in a more efficient way, and control the epidemic.

Guest Editorial: Machine Learning for AI-Enhanced Healthcare and Medical Services: New Development and Promising Solution

The papers in this special section focus on machine learning for artificial intelligent-enhances healthcare and medical services. These services are always among the top concerns for humans, especially under the special situation of COVID-19 pandemic, started from early 2020. In the field of computational biology and bioinformatics, scientists seek various possibilities using computer technologies, especially artificial intelligence (AI) enhanced methods, for healthcare services and medical diagnoses. For example, over the past few years, scientists have been working hard to identify the internal relationships between gene microarrays, cells, tissues, organisms, diseases, etc., and apply the AI, machine learning and deep learning technologies looking for more innovative solutions for new diseases, such as COVID-19. In fact, nowadays, AI technology, such as the convolutional neural network (CNN), is considered has one of the most important computer technologies and has been widely applied in the fields of healthcare engineering, medical research, disease diagnosis, cancer/tumor analysis and etc.

Characterizing Hydropathy of Amino Acid Side Chain in a Protein Environment by Investigating the Structural Changes of Water Molecules Network.

Assessing the hydropathy properties of molecules, like proteins and chemical compounds, has a crucial role in many fields of computational biology, such as drug design, biomolecular interaction, and folding prediction. Over the past decades, many descriptors were devised to evaluate the hydrophobicity of side chains. In this field, recently we likewise have developed a computational method, based on molecular dynamics data, for the investigation of the hydrophilicity and hydrophobicity features of the 20 natural amino acids, analyzing the changes occurring in the hydrogen bond network of water molecules surrounding each given compound. The local environment of each residue is complex and depends on the chemical nature of the side chain and the location in the protein. Here, we characterize the solvation properties of each amino acid side chain in the protein environment by considering its spatial reorganization in the protein local structure, so that the computational evaluation of differences in terms of hydropathy profiles in different structural and dynamical conditions can be brought to bear. A set of atomistic molecular dynamics simulations have been used to characterize the dynamic hydrogen bond network at the interface between protein and solvent, from which we map out the local hydrophobicity and hydrophilicity of amino acid residues.

Using Chou's 5-Step Rule to Predict DNA-Protein Binding with Multi-scale Complementary Feature.

It is well known that DNA-protein binding (DPB) prediction is not only beneficial to understand the regulation mechanism of gene expression but also a challenging task in the field of computational biology. Traditional methods for DPB prediction that depend on manually extracted features may lead to classification errors. Recently, deep learning such as convolutional neural network (CNN) has been successfully applied to classification tasks and improved DPB prediction performance significantly. Yet, these methods are based on the original DNA sequence modeling, ignoring the hidden complex dependency and complementarity between multiple sequence features. In consideration of this problem, we propose a method to fuse different sequence features and analyze them systematically through multi-scale CNN. First, sliding windows of specified lengths are set on distinct DNA sequences to generate multiple sequence features with unequal lengths. Second, multiple feature sequences are fused and encoded for feature representation. Third, multi-scale CNN with different binding motif lengths is used to automatically learn and mine the influence of internal attributes and hidden complex relations between the fusion sequence features and make full use of the complementary advantages of extracted CNN features to predict DPB. When our model is applied to 690 ChIP-seq datasets, it achieves an average AUC of 0.9112, which is significantly better than the latest methods. The results show that our method is effective for DPB prediction and is freely available at http://121.5.71.120/mscDPB/.

Involvement of Machine Learning Tools in Healthcare Decision Making.

In the present day, there are many diseases which need to be identified at their early stages to start relevant treatments. If not, they could be uncurable and deadly. Due to this reason, there is a need of analysing complex medical data, medical reports, and medical images at a lesser time but with greater accuracy. There are even some instances where certain abnormalities cannot be directly recognized by humans. In healthcare for computational decision making, machine learning approaches are being used in these types of situations where a crucial data analysis needs to be performed on medical data to reveal hidden relationships or abnormalities which are not visible to humans. Implementing algorithms to perform such tasks itself is difficult, but what makes it even more challenging is to increase the accuracy of the algorithm while decreasing the required time for the algorithm to execute. In the early days, processing of large amount of medical data was an important task which resulted in machine learning being adapted in the biological domain. Since this happened, the biology and biomedical fields have been reaching higher levels by exploring more knowledge and identifying relationships which were never observed before. Reaching to its peak now the concern is being diverted towards treating patients not only based on the type of disease but also their genetics, which is known as precision medicine. Modifications in machine learning algorithms are being performed and tested daily to improve the performance of the algorithms in analysing and presenting more accurate information. In the healthcare field, starting from information extraction from medical documents until the prediction or diagnosis of a disease, machine learning has been involved. Medical imaging is a section that was greatly improved with the integration of machine learning algorithms to the field of computational biology. Nowadays, many disease diagnoses are being performed by medical image processing using machine learning algorithms. In addition, patient care, resource allocation, and research on treatments for various diseases are also being performed using machine learning-based computational decision making. Throughout this paper, various machine learning algorithms and approaches that are being used for decision making in the healthcare sector will be discussed along with the involvement of machine learning in healthcare applications in the current context. With the explored knowledge, it was evident that neural network-based deep learning methods have performed extremely well in the field of computational biology with the support of the high processing power of modern sophisticated computers and are being extensively applied because of their high predicting accuracy and reliability. When giving concern towards the big picture by combining the observations, it is noticeable that computational biology and biomedicine-based decision making in healthcare have now become dependent on machine learning algorithms, and thus they cannot be separated from the field of artificial intelligence.

Machine and Deep Learning for Prediction of Subcellular Localization.

Protein subcellular localization prediction (PSLP), which plays an important role in the field of computational biology, identifies the position and function of proteins in cells without expensive cost and laborious effort. In the past few decades, various methods with different algorithms have been proposed in solving the problem of subcellular localization prediction; machine learning and deep learning constitute a large portion among those proposed methods. In order to provide an overview about those methods, the first part of this article will be a brief review of several state-of-the-art machine learning methods on subcellular localization prediction; then the materials used by subcellular localization prediction is described and a simple prediction method, that takes protein sequences as input and utilizes a convolutional neural network as the classifier, is introduced. At last, a list of notes is provided to indicate the major problems that may occur with this method.

Sequence-based Identification of Allergen Proteins Developed by Integration of PseAAC and Statistical Moments via 5-Step Rule

Background: Allergens are antigens that can stimulate an atopic type I human hypersensitivity reaction by an immunoglobulin E (IgE) reaction. Some proteins are naturally allergenic than others. The challenge for toxicologists is to identify properties that allow proteins to cause allergic sensitization and allergic diseases. The identification of allergen proteins is a very critical and pivotal task. The experimental identification of protein functions is a hectic, laborious and costly task; therefore, computer scientists have proposed various methods in the field of computational biology and bioinformatics using various data science approaches. Objectives: Herein, we report a novel predictor for the identification of allergen proteins. Methods: For feature extraction, statistical moments and various position-based features have been incorporated into Chou’s pseudo amino acid composition (PseAAC), and are used for training of a neural network. Results: The predictor is validated through 10-fold cross-validation and Jackknife testing, which gave 99.43% and 99.87% accurate results. Conclusions: Thus, the proposed predictor can help in predicting the Allergen proteins in an efficient and accurate way and can provide baseline data for the discovery of new drugs and biomarkers.

Analog Electronic Circuits to Model Cooperativity in Hill Process

In the field of computational biology, electronic modeling of bio-cellular processes is in vogue for about a couple of decades. Fast, efficient and scalable electronic mimetics of recurrently found bio-chemical reactions are expected to provide better electronic circuit simulators that can also be used as bio-sensors or implantable biodevices at cellular levels. This paper presents some possible electronic circuit equivalents to model dynamics of one such bio-chemical reaction commonly involved in many bio-cellular processes, specifically pathways in living cells, known as the Hill process. The distinguishing feature of this process is cooperativity which has been modeled in silicon substrate using a pair of transistors, one transistor driving current in the other the same way ligand binding to one receptor site controls the binding affinity of the other receptor sites. Two possible circuits have been proposed and compared to electronically model cooperativity of a Hill reaction. The main idea is to exploit the natural analogies found between structures and processes of a bio-cell and electronic transistor mechanics, to efficiently model fundamental bio-chemical reactions found recurring in bio-processes. These circuits can then be combined and rearranged quickly to form larger, more complex bio-networks, thus mitigating the intricacies involved in modeling of such systems.

On the Logical Design of a Prototypical Data Lake System for Biological Resources.

Biological resources are multifarious encompassing organisms, genetic materials, populations, or any other biotic components of ecosystems, and fine-grained data management and processing of these diverse types of resources proposes a tremendous challenge for both researchers and practitioners. Before the conceptualization of data lakes, former big data management platforms in the research fields of computational biology and biomedicine could not deal with many practical data management tasks very well. As an effective complement to those previous systems, data lakes were devised to store voluminous, varied, and diversely structured or unstructured data in their native formats, for the sake of various analyses like reporting, modeling, data exploration, knowledge discovery, data visualization, advanced analysis, and machine learning. Due to their intrinsic traits, data lakes are thought to be ideal technologies for processing of hybrid biological resources in the format of text, image, audio, video, and structured tabular data. This paper proposes a method for constructing a practical data lake system for processing multimodal biological data using a prototype system named ProtoDLS, especially from the explainability point of view, which is indispensable to the rigor, transparency, persuasiveness, and trustworthiness of the applications in the field. ProtoDLS adopts a horizontal pipeline to ensure the intra-component explainability factors from data acquisition to data presentation, and a vertical pipeline to ensure the inner-component explainability factors including mathematics, algorithm, execution time, memory consumption, network latency, security, and sampling size. The dual mechanism can ensure the explainability guarantees on the entirety of the data lake system. ProtoDLS proves that a single point of explainability cannot thoroughly expound the cause and effect of the matter from an overall perspective, and adopting a systematic, dynamic, and multisided way of thinking and a system-oriented analysis method is critical when designing a data processing system for biological resources.

DNA sequence reconstruction based on innovated hybridization technique of probabilistic cellular automata and particle swarm optimization

DNA sequence reconstruction is a challenging research problem in the computational biology field. The evolution of the DNA is too complex to be characterized by a few parameters. Therefore, there is a need for a modeling approach for analyzing DNA patterns. In this paper, we proposed a novel framework for DNA pattern analysis. The proposed framework consists of two main stages. The first stage is for analyzing the DNA sequences evolution, whereas the other stage is for the reconstruction process. We utilized cellular automata (CA) rules for analyzing and predicting the DNA sequence. Then, a modified procedure for the reconstruction process is introduced, which is based on the Probabilistic Cellular Automata (PCA) integrated with Particle Swarm Optimization (PSO) algorithm. This integration makes the proposed framework more efficient and achieves optimum transition rules. Our innovated model leans on the hypothesis that mutations are probabilistic events. As a result, their evolution can be simulated as a PCA model. The main objective of this paper is to analyze various DNA sequences to predict the changes that occur in DNA during evolution (mutations). We used a similarity score as a fitness measure to detect symmetry relations, which is appropriate for numerous extremely long sequences. Results are given for the CpG-methylation-deamination processes, which are regions of DNA where a guanine nucleotide follows a cytosine nucleotide in the linear sequence of bases. The DNA evolution is handled as the evolved colored paradigms. Therefore, incorporating probabilistic components help to produce a tool capable of foretelling the likelihood of specific mutations. Besides, it shows their capabilities in dealing with complex relations.

A consensus multi-view multi-objective gene selection approach for improved sample classification

BackgroundIn the field of computational biology, analyzing complex data helps to extract relevant biological information. Sample classification of gene expression data is one such popular bio-data analysis technique. However, the presence of a large number of irrelevant/redundant genes in expression data makes a sample classification algorithm working inefficiently. Feature selection is one such high-dimensionality reduction technique that helps to maximize the effectiveness of any sample classification algorithm. Recent advances in biotechnology have improved the biological data to include multi-modal or multiple views. Different ‘omics’ resources capture various equally important biological properties of entities. However, most of the existing feature selection methodologies are biased towards considering only one out of multiple biological resources. Consequently, some crucial aspects of available biological knowledge may get ignored, which could further improve feature selection efficiency.ResultsIn this present work, we have proposed a Consensus Multi-View Multi-objective Clustering-based feature selection algorithm called CMVMC. Three controlled genomic and proteomic resources like gene expression, Gene Ontology (GO), and protein-protein interaction network (PPIN) are utilized to build two independent views. The concept of multi-objective consensus clustering has been applied within our proposed gene selection method to satisfy both incorporated views. Gene expression data sets of Multiple tissues and Yeast from two different organisms (Homo Sapiens and Saccharomyces cerevisiae, respectively) are chosen for experimental purposes. As the end-product of CMVMC, a reduced set of relevant and non-redundant genes are found for each chosen data set. These genes finally participate in an effective sample classification.ConclusionsThe experimental study on chosen data sets shows that our proposed feature-selection method improves the sample classification accuracy and reduces the gene-space up to a significant level. In the case of Multiple Tissues data set, CMVMC reduces the number of genes (features) from 5565 to 41, with 92.73% of sample classification accuracy. For Yeast data set, the number of genes got reduced to 10 from 2884, with 95.84% sample classification accuracy. Two internal cluster validity indices - Silhouette and Davies-Bouldin (DB) and one external validity index Classification Accuracy (CA) are chosen for comparative study. Reported results are further validated through well-known biological significance test and visualization tool.