Microarray technology provides the expression level of many genes. Nowadays, an important issue is to select a small number of informative differentially expressed genes that provide biological knowledge and may be key elements for a disease. With the increasing volume of data generated by modern biomedical studies, software is required for effective identification of differentially expressed genes

Diseases like cancer will lead to changes in gene expression, and it is relevant to identify key regulatory genes that can be linked directly to these changes. This can be done by computing a Regulatory Impact Factor (RIF) score for relevant regulators. However, this computation is based on estimating correlated patterns of gene expression, often Pearson correlation, and an assumption about a set of

The notion of heme as a regulator of many physiological processes via transient binding to proteins is one that is recently being acknowledged. The broad spectrum of the effects of heme makes it important to identify further heme-regulated proteins to understand physiological and pathological processes. Moreover, several proteins were shown to be functionally regulated by interaction with heme, yet

Both intra- and inter-sentential semantic relations in biomedical texts provide valuable information for biomedical research. However, most existing methods either focus on extracting intra-sentential relations and ignore inter-sentential ones or fail to extract inter-sentential relations accurately and regard the instances containing entity relations as being independent, which neglects the interactions

Accumulated evidence shows that the abnormal regulation of long non-coding RNA (lncRNA) is associated with various human diseases. Accurately identifying disease-associated lncRNAs is helpful to study the mechanism of lncRNAs in diseases and explore new therapies of diseases. Many lncRNA-disease association (LDA) prediction models have been implemented by integrating multiple kinds of data resources

The ability to confidently predict health outcomes from gene expression would catalyze a revolution in molecular diagnostics. Yet, the goal of developing actionable, robust, and reproducible predictive signatures of phenotypes such as clinical outcome has not been attained in almost any disease area. Here, we report a comprehensive analysis spanning prediction tasks from ulcerative colitis, atopic

In unsupervised learning and clustering, data integration from different sources and types is a difficult question discussed in several research areas. For instance in omics analysis, dozen of clustering methods have been developed in the past decade. When a single source of data is at play, hierarchical clustering (HC) is extremely popular, as a tree structure is highly interpretable and arguably

After publication of the original article [1], we were notified that legends of Fig. 1 and Fig. 2 have been swapped.

Two-dimensional electrophoresis (2DE) is one of the most widely applied techniques in comparative proteomics. The basic task of 2DE is to identify differential protein expression by quantitative analysis of 2DE images. To reduce the errors of spot quantification in 2DE images, a novel brightness correction method based on gradient interval histogram (GIH) is proposed in this paper. First, GIH equalization

mRNA interaction with other mRNAs and other signaling molecules determine different biological pathways and functions. Gene co-expression network analysis methods have been widely used to identify correlation patterns between genes in various biological contexts (e.g., cancer, mouse genetics, yeast genetics). A challenge remains to identify an optimal partition of the networks where the individual

Function genomic studies will generally result in lists of genes that may provide clues for exploring biological questions and discovering unanticipated functions, based on differential gene expression analysis, differential epigenomic analysis or co-expression network analysis. While tools have been developed to identify biological functions that are enriched in the genes sets, there remains a need

Deep mutational scanning (DMS) studies exploit the mutational landscape of sequence variation by systematically and comprehensively assaying the effect of single amino acid variants (SAVs; also referred to as missense mutations, or non-synonymous Single Nucleotide Variants – missense SNVs or nsSNVs) for particular proteins. We assembled SAV annotations from 22 different DMS experiments and normalized

DNA methylation (DNAm) is an epigenetic regulator of gene expression programs that can be altered by environmental exposures, aging, and in pathogenesis. Traditional analyses that associate DNAm alterations with phenotypes suffer from multiple hypothesis testing and multi-collinearity due to the high-dimensional, continuous, interacting and non-linear nature of the data. Deep learning analyses have

Advanced sequencing machines dramatically speed up the generation of genomic data, which makes the demand of efficient compression of sequencing data extremely urgent and significant. As the most difficult part of the standard sequencing data format FASTQ, compression of the quality score has become a conundrum in the development of FASTQ compression. Existing lossless compressors of quality scores

With the cost of DNA sequencing decreasing, increasing amounts of RNA-Seq data are being generated giving novel insight into gene expression and regulation. Prior to analysis of gene expression, the RNA-Seq data has to be processed through a number of steps resulting in a quantification of expression of each gene/transcript in each of the analyzed samples. A number of workflows are available to help

MicroRNAs (miRNAs) are a kind of small noncoding RNA molecules that are direct posttranscriptional regulations of mRNA targets. Studies have indicated that miRNAs play key roles in complex diseases by taking part in many biological processes, such as cell growth, cell death and so on. Therefore, in order to improve the effectiveness of disease diagnosis and treatment, it is appealing to develop advanced

Lung cancer is the leading cause of cancer-related deaths in both men and women in the United States, and it has a much lower five-year survival rate than many other cancers. Accurate survival analysis is urgently needed for better disease diagnosis and treatment management. In this work, we propose a survival analysis system that takes advantage of recently emerging deep learning techniques. The proposed

RNA-binding proteins interact with their target RNAs at specific sites. These binding sites can be determined genome-wide through individual nucleotide resolution crosslinking immunoprecipitation (iCLIP). Subsequently, the binding sites have to be visualized. So far, no visualization tool exists that is easily accessible but also supports restricted access so that data can be shared among collaborators

Mechanotransduction in bone cells plays a pivotal role in osteoblast differentiation and bone remodelling. Mechanotransduction provides the link between modulation of the extracellular matrix by mechanical load and intracellular activity. By controlling the balance between the intracellular and extracellular domains, mechanotransduction determines the optimum functionality of skeletal dynamics. Failure

In vertebrate genomes, CpG sites can be clustered into CpG islands, and the amount of methylation in a CpG island can change due to gene regulation processes. Thus, single regulatory events can simultaneously change the methylation states of many CpG sites within a CpG island. This should be taken into account when quantifying the amount of change in methylation, for example in form of a branch length

In biomarker discovery, applying domain knowledge is an effective approach to eliminating false positive features, prioritizing functionally impactful markers and facilitating the interpretation of predictive signatures. Several computational methods have been developed that formulate the knowledge-based biomarker discovery as a feature selection problem guided by prior information. These methods often

Finding the tumor location in the prostate is an essential pathological step for prostate cancer diagnosis and treatment. The location of the tumor – the laterality – can be unilateral (the tumor is affecting one side of the prostate), or bilateral on both sides. Nevertheless, the tumor can be overestimated or underestimated by standard screening methods. In this work, a combination of efficient machine

Disease gene prediction is a critical and challenging task. Many computational methods have been developed to predict disease genes, which can reduce the money and time used in the experimental validation. Since proteins (products of genes) usually work together to achieve a specific function, biomolecular networks, such as the protein-protein interaction (PPI) network and gene co-expression networks

Interactions between proteins and non-proteic small molecule ligands play important roles in the biological processes of living systems. Thus, the development of computational methods to support our understanding of the ligand-receptor recognition process is of fundamental importance since these methods are a major step towards ligand prediction, target identification, lead discovery, and more. This

The identification of all matches of a large set of position weight matrices (PWMs) in long DNA sequences requires significant computational resources for which a number of efficient yet complex algorithms have been proposed. We propose BLAMM, a simple and efficient tool inspired by high performance computing techniques. The workload is expressed in terms of matrix-matrix products that are evaluated

Genomic micro-satellites are the genomic regions that consist of short and repetitive DNA motifs. Estimating the length distribution and state of a micro-satellite region is an important computational step in cancer sequencing data pipelines, which is suggested to facilitate the downstream analysis and clinical decision supporting. Although several state-of-the-art approaches have been proposed to

Previously, a seven-cluster pattern claiming to be a universal one in bacterial genomes has been reported. Keeping in mind the most popular theory of chloroplast origin, we checked whether a similar pattern is observed in chloroplast genomes. Surprisingly, eight cluster structure has been found, for chloroplasts. The pattern observed for chloroplasts differs rather significantly, from bacterial one

Melanoma results in the vast majority of skin cancer deaths during the last decades, even though this disease accounts for only one percent of all skin cancers’ instances. The survival rates of melanoma from early to terminal stages is more than fifty percent. Therefore, having the right information at the right time by early detection with monitoring skin lesions to find potential problems is essential

In the field of protein engineering and biotechnology, the discovery and characterization of structural patterns is highly relevant as these patterns can give fundamental insights into protein-ligand interaction and protein function. This paper presents GSP4PDB, a bioinformatics web tool that enables the user to visualize, search and explore protein-ligand structural patterns within the entire Protein

The current study’s purpose is to compare hip structural analysis variables in a group of postmenopausal women with sarcopenia and another group of postmenopausal women with normal skeletal muscle mass index. To do so, the current study included 8 postmenopausal women (whose ages ranged between 65 and 84 years) with sarcopenia and 60 age-matched controls (with normal skeletal muscle mass index (SMI))

The actual task of electrocardiographic examinations is to increase the reliability of diagnosing the condition of the heart. Within the framework of this task, an important direction is the solution of the inverse problem of electrocardiography, based on the processing of electrocardiographic signals of multichannel cardio leads at known electrode coordinates in these leads (Titomir et al. Noninvasiv

In biomedicine, infrared thermography is the most promising technique among other conventional methods for revealing the differences in skin temperature, resulting from the irregular temperature dispersion, which is the significant signaling of diseases and disorders in human body. Given the process of detecting emitted thermal radiation of human body temperature by infrared imaging, we, in this study

Phenotype prediction problems are usually considered ill-posed, as the amount of samples is very limited with respect to the scrutinized genetic probes. This fact complicates the sampling of the defective genetic pathways due to the high number of possible discriminatory genetic networks involved. In this research, we outline three novel sampling algorithms utilized to identify, classify and characterize

For optimization of microfluidic devices for the analysis of blood samples, it is useful to simulate blood cells as elastic objects in flow of blood plasma. In such numerical models, we primarily need to take into consideration the movement and behavior of the dominant component of the blood, the red blood cells. This can be done quite precisely in small channels and within a short timeframe. However

Screening programs use mammography as primary diagnostic tool for detecting breast cancer at an early stage. The diagnosis of some lesions, such as microcalcifications, is still difficult today for radiologists. In this paper, we proposed an automatic binary model for discriminating tissue in digital mammograms, as support tool for the radiologists. In particular, we compared the contribution of different

Chronic diseases are becoming more widespread each year in developed countries, mainly due to increasing life expectancy. Among them, diabetes mellitus (DM) and essential hypertension (EH) are two of the most prevalent ones. Furthermore, they can be the onset of other chronic conditions such as kidney or obstructive pulmonary diseases. The need to comprehend the factors related to such complex diseases

Methylated RNA immunoprecipitation sequencing (MeRIP-Seq) is a popular sequencing method for studying RNA modifications and, in particular, for N6-methyladenosine (m6A), the most abundant RNA methylation modification found in various species. The detection of enriched regions is a main challenge of MeRIP-Seq analysis, however current tools either require a long time or do not fully utilize features

The selection and prioritization of drug targets is a central problem in drug discovery. Computational approaches can leverage the growing number of large-scale human genomics and proteomics data to make in-silico target identification, reducing the cost and the time needed. We developed a machine learning approach to score proteins to generate a druggability score of novel targets. In our model we

Illumina sequencing of a marker gene is popular in metagenomic studies. However, Illumina paired-end (PE) reads sometimes cannot be merged into single reads for subsequent analysis. When mergeable PE reads are limited, one can simply use only first reads for taxonomy annotation, but that wastes information in the second reads. Presumably, including second reads should improve taxonomy annotation. However

To identify and prioritize the influential hub genes in a gene-set or biological pathway, most analyses rely on calculation of marginal effects or tests of statistical significance. These procedures may be inappropriate since hub nodes are common connection points and therefore may interact with other nodes more often than non-hub nodes do. Such dependence among gene nodes can be conjectured based

All-Food-Sequencing (AFS) is an untargeted metagenomic sequencing method that allows for the detection and quantification of food ingredients including animals, plants, and microbiota. While this approach avoids some of the shortcomings of targeted PCR-based methods, it requires the comparison of sequence reads to large collections of reference genomes. The steadily increasing amount of available reference

BACKGROUND Cancer associated copy number variation (CNV) events provide important information for identifying patient subgroups and suggesting treatment strategies. Technical and logistical issues, however, make it challenging to accurately detect abnormal copy number events in a cost-effective manner in clinical studies. RESULTS Here we present CNV Radar, a software tool that utilizes next-generation

With the rapid development of whole exome sequencing (WES), an increasing number of tools are being proposed for copy number variation (CNV) detection based on this technique. However, no comprehensive guide is available for the use of these tools in clinical settings, which renders them inapplicable in practice. To resolve this problem, in this study, we evaluated the performances of four WES-based

BACKGROUND Bulked segregant analysis (BSA), coupled with next-generation sequencing, allows the rapid identification of both qualitative and quantitative trait loci (QTL), and this technique is referred to as BSA-Seq here. The current SNP index method and G-statistic method for BSA-Seq data analysis require relatively high sequencing coverage to detect significant single nucleotide polymorphism (SNP)-trait

BACKGROUND Traditional selection in livestock and crops focuses on additive genetic values or breeding values of the individuals. While traditional selection utilizes variation between individuals, differences between gametes within individuals have been less frequently exploited in selection programs. With the successful implementation of genomic selection in livestock and crops, estimation and selection

Duplex sequencing is the most accurate approach for identification of sequence variants present at very low frequencies. Its power comes from pooling together multiple descendants of both strands of original DNA molecules, which allows distinguishing true nucleotide substitutions from PCR amplification and sequencing artifacts. This strategy comes at a cost—sequencing the same molecule multiple times

BACKGROUND Many cancers arise from mutations in cells within epithelial tissues. Mutations manifesting at the subcellular level influence the structure and function of the tissue resulting in cancer. Previous work has proposed how cell level properties can lead to mutant cell invasion, but has not incorporated detailed subcellular modelling RESULTS: We present a framework that allows the straightforward

BACKGROUND The systems-scale analysis of cellular metabolites, "metabolomics," provides data ideal for applications in metabolic engineering. However, many of the computational tools for strain design are built around Flux Balance Analysis (FBA), which makes assumptions that preclude direct integration of metabolomics data into the underlying models. Finding a way to retain the advantages of FBA's

BACKGROUND Cancer prognosis prediction is valuable for patients and clinicians because it allows them to appropriately manage care. A promising direction for improving the performance and interpretation of expression-based predictive models involves the aggregation of gene-level data into biological pathways. While many studies have used pathway-level predictors for cancer survival analysis, a comprehensive

BACKGROUND Genome-wide association studies (GWAS) provide a powerful means to identify associations between genetic variants and phenotypes. However, GWAS techniques for detecting epistasis, the interactions between genetic variants associated with phenotypes, are still limited. We believe that developing an efficient and effective GWAS method to detect epistasis will be a key for discovering sophisticated

BACKGROUND In this paper, we explore the concept of multi-objective optimization in the field of metabolic engineering when both continuous and integer decision variables are involved in the model. In particular, we propose a multi-objective model that may be used to suggest reaction deletions that maximize and/or minimize several functions simultaneously. The applications may include, among others

BACKGROUND The study of functional associations between ncRNAs and human diseases is a pivotal task of modern research to develop new and more effective therapeutic approaches. Nevertheless, it is not a trivial task since it involves entities of different types, such as microRNAs, lncRNAs or target genes whose expression also depends on endogenous or exogenous factors. Such a complexity can be faced

BACKGROUND The transitions between epithelial (E) and mesenchymal (M) cell phenotypes are essential in many biological processes like tissue development and cancer metastasis. Previous studies, both modeling and experimental, suggested that in addition to E and M states, the network responsible for these phenotypes exhibits intermediate phenotypes between E and M states. The number and importance of

BACKGROUND Protein microarrays are a versatile and widely used tool for analyzing complex protein mixtures. Membrane arrays utilize antibodies which are captured on a membrane to specifically immobilize several proteins of interest at once. Using detection antibodies, the bound protein-antibody-complex is converted into visual signals, which can be quantified using densitometry. The reliability of

BACKGROUND The spatial configuration of chromosomes is essential to various cellular processes, notably gene regulation, while architecture related alterations, such as translocations and gene fusions, are often cancer drivers. Thus, eliciting chromatin conformation is important, yet challenging due to compaction, dynamics and scale. However, a variety of recent assays, in particular Hi-C, have generated

BACKGROUND Shotgun metagenomes are often assembled prior to annotation of genes which biases the functional capacity of a community towards its most abundant members. For an unbiased assessment of community function, short reads need to be mapped directly to a gene or protein database. The ability to detect genes in short read sequences is dependent on pre- and post-sequencing decisions. The objective

BACKGROUND MicroRNA (miRNA) regulation is associated with several diseases, including neurodegenerative diseases. Several approaches can be used for modeling miRNA regulation. However, their precision may be limited for analyzing multidimensional data. Here, we addressed this question by integrating shape analysis and feature selection into miRAMINT, a methodology that we used for analyzing multidimensional

BACKGROUND Single-cell RNA sequencing (scRNA-seq) is an emerging technology that can assess the function of an individual cell and cell-to-cell variability at the single cell level in an unbiased manner. Dimensionality reduction is an essential first step in downstream analysis of the scRNA-seq data. However, the scRNA-seq data are challenging for traditional methods due to their high dimensional measurements

BACKGROUND ChIP (Chromatin immunoprecipitation)-exo has emerged as an important and versatile improvement over conventional ChIP-seq as it reduces the level of noise, maps the transcription factor (TF) binding location in a very precise manner, upto single base-pair resolution, and enables binding mode prediction. Availability of numerous peak-callers for analyzing ChIP-exo reads has motivated the

BACKGROUND Advances in whole genome sequencing strategies have provided the opportunity for genomic and comparative genomic analysis of a vast variety of organisms. The analysis results are highly dependent on the quality of the genome assemblies used. Assessment of the assembly accuracy may significantly increase the reliability of the analysis results and is therefore of great importance. RESULTS