Recently diverged species are challenging for identification, yet they frequently of special interest scientifically as well from a regulatory perspective. DNA barcoding has proven instrumental in especially insects and vertebrates, but the identification recently it been reported to be problematic some cases. Problems mostly due incomplete lineage sorting or simply lack 'barcode gap' probably related large effective population size and/or low mutation rate. Our objective was compare six...

The identification of early and stage-specific biomarkers for Alzheimer's disease (AD) is critical, as the development disease-modification therapies may depend on discovery validation such markers. reliable depends new diagnostic algorithms to computationally exploit information in large biological datasets. To identify potential from mRNA expression profile data, we used Logic Mining method unbiased analysis a microarray dataset anti-NGF AD11 transgenic mouse model. gene brain regions was...

Alzheimer's Disease (AD) is a neurodegenaritive disorder characterized by progressive dementia, for which actually no cure known. An early detection of patients affected AD can be obtained analyzing their electroencephalography (EEG) signals, show reduction the complexity, perturbation synchrony, and slowing down rhythms. In this work, we apply procedure that exploits feature extraction classification techniques to EEG whose aim distinguish patient from ones Mild Cognitive Impairment (MCI)...

Electroencephalography (EEG) signal analysis is a fast, inexpensive, and accessible technique to detect the early stages of dementia, such as Mild Cognitive Impairment (MCI) Alzheimer’s disease (AD). In last years, EEG has become an important topic research extract suitable biomarkers determine subject’s cognitive impairment. this work, we propose novel simple efficient method able features with finite response filter (FIR) in double time domain order discriminate among patients affected by...

Specific fragments, coming from short portions of DNA (e.g., mitochondrial, nuclear, and plastid sequences), have been defined as Barcode can be used markers for organisms the main life kingdoms. Species classification with sequences has proven effective on different organisms. Indeed, specific gene regions identified Barcode: COI in animals, rbcL matK plants, ITS fungi. The problem assigns an unknown specimen to a known species by analyzing its Barcode. This task supported reliable methods...

Abstract BLOG (Barcoding with LOG ic) is a diagnostic and character‐based DNA Barcode analysis method. Its aim to classify specimens species based on sequences supervised machine learning approach, using classification rules that compactly characterize in terms of locations key nucleotides. The 2.0 software, its fundamental modules, online/offline user interfaces recent improvements are described. These affect both methodology software design, lead the availability different releases website...

Abstract Background According to many field experts, specimens classification based on morphological keys needs be supported with automated techniques the analysis of DNA fragments. The most successful results in this area are those obtained from a particular fragment mitochondrial DNA, gene cytochrome c oxidase I (COI) (the "barcode"). Since 2004 Consortium for Barcode Life (CBOL) promotes collection barcode and development methods analyze several tasks, among which identification rules...

Alzheimer's Disease (AD) and its preliminary stage - Mild Cognitive Impairment (MCI) are the most widespread neurodegenerative disorders, their investigation remains an open challenge. ElectroEncephalography (EEG) appears as a non-invasive repeatable technique to diagnose brain abnormalities. Despite technical advances, analysis of EEG spectra is usually carried out by experts that must manually perform laborious interpretations. Computational methods may lead quantitative these signals...

Data extraction and integration methods are becoming essential to effectively access take advantage of the huge amounts heterogeneous genomics clinical data increasingly available. In this work, we focus on The Cancer Genome Atlas, a comprehensive archive tumoral containing results high-throughout experiments, mainly Next Generation Sequencing, for more than 30 cancer types.We propose TCGA2BED software tool search retrieve TCGA data, convert them in structured BED format their seamless use...

Abstract Motivation: Nowadays, knowledge extraction methods from Next Generation Sequencing data are highly requested. In this work, we focus on RNA-seq gene expression analysis and specifically case–control studies with rule-based supervised classification algorithms that build a model able to discriminate cases controls. State of the art compute single contains few features (genes). On contrary, our goal is elicit higher amount by computing many models, therefore identify most genes...

In the Next Generation Sequencing (NGS) era a large amount of biological data is being sequenced, analyzed, and stored in many public databases, whose interoperability often required to allow an enhanced accessibility. The combination heterogeneous NGS genomic open challenge: analysis from different experiments fundamental practice for study diseases. this work, we propose combine DNA methylation RNA sequencing at gene level supervised knowledge extraction cancer.We retrieve datasets Cancer...

The wide spread of electronic data collection in medical environments leads to an exponential growth clinical extracted from heterogeneous patient samples. Collecting, managing, integrating and analyzing these are essential activities order shed light on diseases related therapies. major issues analysis the incompleteness (missing values), different adopted measure scales, integration disparate procedures. Therefore, main challenges managing data, discovering patients interactions, sources....

Next Generation Sequencing technologies have produced a substantial increase of publicly available genomic data and related clinical/biospecimen information. New models methods to easily access, integrate search them effectively are needed. An effort was made by the Genomic Data Commons (GDC), which defined strict procedures for harmonizing clinical cancer, created GDC portal with its application programming interface (API). In this work, we enhance harmonization applying state art model...

Big Data technologies have significantly increased the possibility for sellers to adopt personalisation strategies, especially in digital markets. Among such price discrimination, a practice where same commodity is sold at different prices, either customer or customers, stands out. Particularly, online airline ticket market has risen attention of economists recent studies, both because its specificity and high data availability. This manuscript enters debate analyses an original way. Indeed,...

Microarray Logic Analyzer (MALA) is a clustering and classification software, particularly engineered for microarray gene expression analysis. The aims of MALA are to cluster the profiles in order reduce amount data be analyzed classify experiments. To fulfil this objective uses machine learning process based methodology, that relies on 1) Discretization, 2) Gene clustering, 3) Feature selection, 4) Formulas computation,5) Classification. In paper we describe software design, different...

Next Generation Sequencing (NGS) machines extract from a biological sample large number of short DNA fragments (reads). These reads are then used for several applications, e.g., sequence reconstruction, assembly, gene expression profiling, mutation analysis. We propose method to evaluate the similarity between reads. This does not rely on alignment and it is based distance frequencies their substrings fixed dimensions (k-mers). compare this alignment-free with measures derived two methods:...

Alignment-free algorithms can be used to estimate the similarity of biological sequences and hence are often applied phylogenetic reconstruction genomes. Most these rely on comparing frequency all distinct substrings fixed length (k-mers) that occur in analyzed sequences. In this paper, we present Logic Alignment Free (LAF), a method combines alignment-free techniques rule-based classification order assign samples their taxa. This searches for minimal subset k-mers whose relative frequencies...

Continuous improvements in next generation sequencing technologies led to ever-increasing collections of genomic sequences, which have not been easily characterized by biologists, and whose analysis requires huge computational effort. The classification species emerged as one the main applications DNA has addressed with several approaches, e.g., multiple alignments-, phylogenetic trees-, statistical- character-based methods.We propose a supervised method based on genetic algorithm identify...

Differences in genomic sequences are crucial for the classification of viruses into different species. In this work, viral DNA belonging to human polyomaviruses BKPyV, JCPyV, KIPyV, WUPyV, and MCPyV analyzed using a logic data mining method order identify nucleotides which able distinguish five polyomaviruses. The approach presented work is successful as it discovers several rules that effectively characterize studied individuated separate precisely one type from other assign an unknown...

The high growth of Next Generation Sequencing data currently demands new knowledge extraction methods. In particular, the RNA sequencing gene expression experimental technique stands out for case-control studies on cancer, which can be addressed with supervised machine learning techniques able to extract human interpretable models composed genes, and their relation investigated disease. State art rule-based classifiers are designed a single classification model, possibly few relevant genes....

Alzheimer's Disease (AD) is the most widespread and incurable neurodegenerative disorder, together with its preliminary stage - Mild Cognitive Impairment (MCI) detection still remains a challenging issue. Electroencephalography (EEG) non-invasive repeatable technique to diagnose brain abnormalities. However, analysis of EEG spectra carried out manually by experts effective computer science methods extract relevant information from these signals become necessity. Through data mining approach,...