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In this paper, we present a new reference-free and lossless approach to compress next-generation sequencing (NGS) data in FASTQ format, splitting the input FASTQ data into three parts of metadata, short reads and quality scores, and eliminating their redundancy independently according to their own characteristics. Experiments were conducted on five real-world NGS data. The results show that the proposed...
Protein structure comparison is one of the most challenging problem in bioinformatics. This problem is modeled as a contact map overlap problem in which the similarity of the two proteins being compared is measured by the amount of overlap between their corresponding protein contact maps. To find a maximum overlap is proved to be an NP-hard problem in this area. Protein contact map is a two dimensional...
The origin of replication (oriC) plays an important role in the cell cycle as the place where DNA replication is initiated. In bacterial cells, a single replication origin can be found and its correct identification is necessary in the annotation process of newly sequenced genomes. Although the rearrangement of a whole genome sequence according to oriC should be a standard procedure, public databases...
Attention plays a critical role in effective learning. By means of attention assessment, it helps learners improve and review their learning processes, and even discover Attention Deficit Hyperactivity Disorder (ADHD). Hence, this work employs modified smart glasses which have an inward facing camera for eye tracking, and an inertial measurement unit for head pose estimation. The proposed attention...
Shotgun sequencing has facilitated the analysis of complex microbial communities. Recently we have shown how local binary patterns (LBP) from image processing can be used to analyse the sequenced samples. LBP codes represent the data in a sparse high dimensional space. To improve the performance of our pipeline, marginalised stacked autoencoders are used here to learn frequent LBP codes and map the...
Pulmonary nodules detection play a significant role in the early detection and treatment of lung cancer. False positive reduction is the one of the major parts of pulmonary nodules detection systems. In this study a novel method aimed at recognizing real pulmonary nodule among a large group of candidates was proposed. The method consists of three steps: appropriate receptive field selection, feature...
Methanogenic archaea probably played a major role in the evolution of earth's atmosphere. Here we report the results of a comparative analysis of metabolic networks of mesophilic archaeon Methanosarcina acetivorans (M. acetivorans) and the thermophilic archaeon Methanopyrus kandleri (M. kandleri). In this study, we used simulated annealing (SA) to obtain different modules of their metabolic networks...
Ensemble methods for clustering take a collection of input partitions, produced for the same data set, and generate an ensemble partition that tries to preserve the information carried in this collective. Acceptance of the resulting partition(s) by decision makers can be a problem, due to the inherent complexity of ensemble techniques, and the associated lack of intuition on how a consensus has been...
The use of residue-residue contact maps in protein structure prediction (PSP) has proved promising during the last CASP editions (CASP10, 11 and 12). The goals of this work are to carry out an assessment of the information given by contact maps and to develop a strategy to use the contact constraints from these maps to improve the quality of the predicted models in a de novo PSP approach. A residue-residue...
Huynh-Thu and colleagues initially introduce the random forest into field of genetic network inference. Their method, GENIE3, has performed well on genetic network inference problems. However, GENIE3 was designed only for analyzing static expression data that were measured under steady-state conditions. In order to infer genetic networks from time-series of gene expression data, this study proposes...
In this paper, we compare methods for evaluating the fetal state prediction based on Cardiotocography (CTG) data. Antepartum Fetal Monitoring provides information that can be used to predict the state of the fetus during labor to indicate the risk of a fetal acidosis (low blood pH from low oxygen levels). The effectiveness of these predictions is evaluated in a real-time clinical decision support...
This paper explores machine learning application in the case of drug discovery. We apply extreme gradient boosting and K-nearest neighbor to biomedical data and it signiflcantly outperform former studies using feature selection and proper tuning parameters. The novel application motivated by a recent circumstance that there is a need for rapid development of radio-protectors. It mainly targets the...
Alzheimer's disease (AD) is a neurodegenerative disease caused by the progressive death of brain cells over time. It represents the most frequent cause of dementia in the western world, and affects an individual's cognitive ability and psychological capacity. While clinical diagnoses of AD are made primarily on the basis of clinical evaluation and mental health tests, diagnostic certainty is only...
The requirements for treatments vary for different diseases. These have to be considered in order to plan ahead the expenditures for the health care system. In this sense, disease surveillance has a significant impact on resource planning. To this end, we study the problem of predicting the number of incidences for a given disease based on the internet search and access log statistics. A number of...
Bone suppression in lung radiographs is an important task, as it improves the results on other related tasks, such as nodule detection or pathologies classification. In this paper, we propose two architectures that suppress bones in radiographs by treating them as noise. In the proposed methods, we create end-to-end learning frameworks that minimize noise in the images while maintaining sharpness...
The optimisation of classifier performance in pattern recognition and medical prognosis tasks is a complex and poorly miderstood problem. Classifier performance is greatly affected by the choice of artificial neural network architecture and starting weights and biases — yet there exists very little guidance in the literature as to how to choose these parameters. Recently evolutionary artificial neural...
In many real-world problems, as the protein structure prediction (PSP), a number of conflicting objectives have to be simultaneously optimized. In this paper, the Aggregation Tree (AT) method is applied to arrange the energy function terms used by a protein three-dimensional structure prediction program (called GAPF) that is based on a multiobjective genetic algorithm. The results achieved using the...
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