Computational Biology

HCV is a leading cause of hepatocellular carcinoma and cirrhosis all over the world. Claudins belong to family of tight junction's proteins that are responsible for establishing barriers for controlling the flow of molecules around cells. For therapeutic strategies, regulation of viral entry into the host cells holds a lot of promise. During HCV infection claudin-1 is highly expressed in liver and believed to be associated with HCV virus entry after HCV binding with or without co-receptor CD81. The claudin-1 assembly with tight junctions is regulated by post translational modifications. During claudins assembly and disassembly with tight junctions, phosphorylation is required at C-terminal tail. In cellular proteins, interplay between phosphorylation and O-b-GlcNAc modification is believed to be functional switch, but it is very difficult to monitor these functional and vibrant changes in vivo. Netphos 2.0 and Disphos 1.3 programs were used for potential phosphorylation; NetPhosK 1.0 and KinasePhos for kinase prediction; and YinOYang 1.2 and OGPET to predict possible O-glycosylation sites. We also identified Yin Yang sites that may have potential for O-b-GlcNAc and phosphorylation interplay at same Ser/Thr residues. We for the first time proposed that alternate phosphorylation and O-b-GlcNAc modification on Ser 192, Ser 205, Ser 206; and Thr 191 may provide an on/off switch to regulate assembly of claudin-1 at tight junctions. In addition these phosphorylation sites may be targeted by novel chemotherapeutic agents to prevent phosphorylation lead by HCV viral entry complex.

In the pursuit of sensitive and quantitative methods to detect and diagnose cancer, nanotechnology has been identified as a field of great promise. Semiconductor quantum dots are nanoparticles with intense, stable fluorescence, and could enable the detection of tens to hundreds of cancer biomarkers in blood assays, on cancer tissue biopsies, or as contrast agents for medical imaging. With the emergence of gene and protein profiling and microarray technology, high-throughput screening of biomarkers has generated databases of genomic and expression data for certain cancer types, and has identified new cancer-specific markers. Quantum dots have the potential to expand this in vitro analysis, and extend it to cellular, tissue and whole-body multiplexed cancer biomarker imaging.

7th International Conference on Artificial Intelligence and Applications (AI 2021) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and its applications. The Conference
looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and
share cutting-edge development in the field. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to.

Ridden .Markov Models (HMMs) are applied t.0 the problems of statistical modeling, database searching and multiple sequence alignment of protein families and protein domains. These methods are demonstrated on the globin family, the protein kinase catalytic domain, and the EF-hand calcium binding motif. In each case the parameters of an HMM are estimated from a training set of unaligned sequences. After the HMM is built, it is used to obtain a multiple alignment of all the training sequences. It is also used to search the . SWISS-PROT 22 database for other sequences. that are members of the given protein family, or contain the given domain. The Hi" produces multiple alignments of good quality that agree closely with the alignments produced by programs that incorporate threedimensional structural information. When employed in discrimination tests (by examining how closely the sequences in a database fit the globin, kinase and EF-hand HMMs), the '\ HMM is able to distinguish members of these families from non-members with a high degree ' of accuracy. Both the HMM and PROFILESEARCH ( a technique used to search for relationships between a protein sequence and multiply aligned sequences) perform better in these tests than PROSITE (a dictionary of sites and patterns in proteins). The HMM appecvs to have a slight advantage over PROFILESEARCH in terms of lower rates of false negatives and false positives, even though the HMM is trained using only unaligned sequences, whereas PROFILESEARCH requires aligned training sequences. Our results suggest the presence of an EF-hand calcium binding motif in a highly conserved and evolutionary preserved putative intracellular region of 155 residues in the a-l subunit of L-type calcium channels which play an important role in excitation-contraction coupling. This region has been suggested to contain the functional domains that are typical or essential for all L-type calcium channels regardless of whether they couple to ryanodine receptors, conduct ions or both. \ 8 Abbreviations HMM, hidden Marko,. ,,,dels; length from 130 to 170 residues (with few excep-' m, Expectetion-Maximization; ML. maximum tions) and two domains, the protein kinase catalytic like€ihood; MAP, maximum a posten'wi; hTL-score.

Large structural variants (SVs) in the human genome are difficult to detect and study by conventional sequencing technologies. With long-range genome analysis platforms, such as optical mapping, one can identify large SVs (>2 kb) across the genome in one experiment. Analyzing optical genome maps of 154 individuals from the 26 populations sequenced in the 1000 Genomes Project, we find that phylogenetic population patterns of large SVs are similar to those of single nucleotide variations in 86% of the human genome, while~2% of the genome has high structural complexity. We are able to characterize SVs in many intractable regions of the genome, including segmental duplications and subtelomeric, pericentromeric, and acrocentric areas. In addition, we discover~60 Mb of non-redundant genome content missing in the reference genome sequence assembly. Our results highlight the need for a comprehensive set of alternate haplotypes from different populations to represent SV patterns in the genome.

More than 7,000 rare diseases (RDs) exist worldwide, affecting approximately 350 million people, out of which only 5% have treatment. The development of novel genome sequencing techniques has accelerated the discovery and diagnosis in RDs. However, most patients remain undiagnosed. Epigenetics has emerged as a promise for diagnosis and therapies in common disorders (e.g., cancer) with several epimarkers and epidrugs already approved and used in clinical practice. Hence, it may also become an opportunity to uncover new disease mechanisms and therapeutic targets in RDs. In this “big data” age, the amount of information generated, collected, and managed in (bio)medicine is increasing, leading to the need for its rapid and efficient collection, analysis, and characterization. Artificial intelligence (AI), particularly deep learning, is already being successfully applied to analyze genomic information in basic research, diagnosis, and drug discovery and is gaining momentum in the epigene...

We report the construction of a genome-wide fish metabolic network model, MetaFishNet, and its application to analyzing high throughput gene expression data. This model is a stepping stone to broader applications of fish systems biology, for example by guiding study design through comparison with human metabolism and the integration of multiple data types. MetaFishNet resources, including a pathway enrichment analysis tool, are accessible at http://metafishnet.appspot.com.

he fossils described elsewhere in this issue probably left no descendants, are not very old, and were found on a remote island. Despite this, they are among the most outstanding discoveries in palaeoanthropology for half a century. The two papers concerned-by Brown et al. 1 and Morwood et al. 2-appear on pages 1055 and 1087 of this issue, and respectively describe the fossils and their archaeological context. The find is startling.It is of a pygmysized, small-brained hominin, which lived as recently as 18,000 years ago, and which was found on the island of Flores together with stone tools, dwarf elephants and Komodo dragons. Discoveries don't get better than that. The Flores fossils add a new and surprising twig to the hominin family tree, which diverged from the chimpanzee lineage about 7 million years ago. The first African hominins existed 7-1.2 million years ago, were 1-1.5 metres tall, walked upright on two legs (that is, were bipedal), and had chimpanzee-size brains. These early forms comprised as many as six genera and fourteen species, of which the australopithecines are the best known. By 2.5 million years ago, our own genus, Homo, had emerged, with its different body shape, slower growth, greater reliance on meat in the diet, and 'encephalization'-larger brains than expected for body size. These were the first hominins to make stone tools systematically and to colonize Eurasia. They include the familiar names of H. habilis, H. erectus, H. neanderthalensis and, finally, H. sapiens, which put in an appearance about 160,000 years ago. The new fossil is part of this Homo group (Fig. 1). Flores lies to the east of Java, and was probably never connected to the mainland. The presence of 800,000-year-old simple stone tools first attracted attention in 1998 (ref. 3), raising the controversial possibility that H. erectus had produced them and had crossed major sea barriers to reach Flores. Now we have the announcement of the discovery of an 18,000-year-old hominin skeleton from a cave, Liang Bua, on Flores. Although this date is more than 140,000 years after modern humans evolved in Africa, more than 25,000 years after H. sapiens reached Australia, and about 10,000 years after the last known Neanderthal, the

Analysis of HLA data involves queries on web portals, whose search parameters are data stored in laboratories' databases. In order to automate these queries, one approach is to structure laboratory data into a database and to develop bioinformatic tools to perform the data mapping. In this context, we developed the LabSystem Gen tool that allows users to create a Laboratory Information System, without programming. Additionally we implemented a framework that provides bioinformatic tools, transparent access to public HLA (human leukocyte antigen) information resources. We demonstrated the LabSystemGen system by implementing BMDdb, which is a LIMS that manages data of recipients and donors of organ transplant.

The antimicrobial peptide human α-defensin 5 (HD5) is expressed in Paneth cells, secretory epithelial cells in the small intestine. Unlike other characterized defensins, HD5 is stored in secretory vesicles as a propeptide. The storage quantities of HD5 are ∼90-450 µg per cm 2 of mucosal surface area, which is sufficient to generate microbicidal concentrations in the intestinal lumen. HD5 peptides isolated from the intestinal lumen are proteolytically processed forms-HD5(56-94) and HD5(63-94)-that are cleaved at the Arg 55 -Ala 56 and Arg 62 -Thr 63 sites, respectively.We show here that a specific pattern of trypsin isozymes is expressed in Paneth cells, that trypsin colocalizes with HD5 and that this protease can efficiently cleave HD5 propeptide to forms identical to those isolated in vivo. By acting as a prodefensin convertase in human Paneth cells, trypsin is involved in the regulation of innate immunity in the small intestine.

The Human Connectome Project (HCP) has developed protocols, standard operating and quality control procedures, and a suite of informatics tools to enable high throughput data collection, data sharing, automated data processing and analysis, and data mining and visualization. Quality control procedures include methods to maintain data collection consistency over time, to measure head motion, and to establish quantitative modality-specific overall quality assessments. Database services developed as customizations of the XNAT imaging informatics platform support both internal daily operations and open access data sharing. The Connectome Workbench visualization environment enables user interaction with HCP data and is increasingly integrated with the HCP's database services. Here we describe the current state of these procedures and tools and their application in the ongoing HCP study.

The DNA damage response triggered by bacterial cytolethal distending toxins (CDTs) is associated with activation of the actin-regulating protein RhoA and phosphorylation of the downstream-regulated mitogen-activated protein kinase (MAPK) p38, which promotes the survival of intoxicated (i.e. cells exposed to a bacterial toxin) cells. To identify the effectors of this CDT-induced survival response, we screened a library of 4492 Saccharomyces cerevisiae mutants that carry deletions in nonessential genes for reduced growth following inducible expression of CdtB. We identified 78 genes whose deletion confers hypersensitivity to toxin. Bioinformatics analysis revealed that DNA repair and endocytosis were the two most overrepresented signaling pathways. Among the human orthologs present in our data set, FEN1 and TSG101 regulate DNA repair and endocytosis, respectively, and also share common interacting partners with RhoA. We further demonstrate that FEN1, but not TSG101, regulates cell sur...

Correct prediction of the translation initiation site (TIS) is an important issue in genomic research. We show that feature generation together with correlation based feature selection can be used with a variety of machine learning algorithms to give highly accurate translation initiation site prediction. Only very few features are needed and the results achieve comparable accuracy to the best existing approaches. Our approach has the advantage that it does not require one to devise a special prediction method; rather standard machine learning classifiers are shown to give very good performance on the selected features. The raw and generated features which we have found to be important are the following: positions −3 and −1 in the sequence; upstream k-grams for k=3, 4, and 5; stop-codon frequency; downstream in-frame 3-gram; and the distance of ATG to the beginning of the sequence. The best result, with an overall accuracy of 90%, is obtained by selecting only seven features from this set. The same features retrained with the use of a scanning model achieves an overall accuracy of 94% on this dataset.

The objective of this study is to find a correlation between the abdominal aortic aneurysm (AAA) geometric parameters, wall stress shear (WSS), abdominal flow patterns, intraluminal thrombus (ILT), and AAA arterial wall rupture using computational fluid dynamics (CFD). Real AAA 3D models were created by three-dimensional (3D) reconstruction of in vivo acquired computed tomography (CT) images from 5 patients. Based on 3D AAA models, high quality volume meshes were created using an optimal tetrahedral aspect ratio for the whole domain. In order to quantify the WSS and the recirculation inside the AAA, a 3D CFD using finite elements analysis was used. The CFD computation was performed assuming that the arterial wall is rigid and the blood is considered a homogeneous Newtonian fluid with a density of 1050 kg/m3and a kinematic viscosity of4×10-3Pa·s. Parallelization procedures were used in order to increase the performance of the CFD calculations. A relation between AAA geometric paramet...

We used overlapping and nested homozygous deletions, contig building, genomic sequencing, and physical and transcript mapping to further define a approximately 630-kb lung cancer homozygous deletion region harboring one or more tumor suppressor genes (TSGs) on chromosome 3p21.3. This location was identified through somatic genetic mapping in tumors, cancer cell lines, and premalignant lesions of the lung and breast, including the discovery of several homozygous deletions. The combination of molecular manual methods and computational predictions permitted us to detect, isolate, characterize, and annotate a set of 25 genes that likely constitute the complete set of protein-coding genes residing in this approximately 630-kb sequence. A subset of 19 of these genes was found within the deleted overlap region of approximately 370-kb. This region was further subdivided by a nesting 200-kb breast cancer homozygous deletion into two gene sets: 8 genes lying in the proximal approximately 120-...

Detrital zircons more than 4 billion years old from the Jack Hills metasedimentary belt, Yilgarn craton, Western Australia, are the oldest identified fragments of the Earth's crust 1,2 and are unique in preserving information on the earliest evolution of the Earth. Inclusions of quartz, K-feldspar and monazite in the zircons 3 , in combination with an enrichment of light rare-earth elements 4,5 and an estimated low zircon crystallization temperature 6 , have previously been used as evidence for early recycling of continental crust, leading to the production of granitic melts in the Hadean era. Here we present the discovery of microdiamond inclusions in Jack Hills zircons with an age range from 3,058 6 7 to 4,252 6 7 million years. These include the oldest known diamonds found in terrestrial rocks, and introduce a new dimension to the debate on the origin of these zircons and the evolution of the early Earth 6-10. The spread of ages indicates that either conditions required for diamond formation were repeated several times during early Earth history or that there was significant recycling of ancient diamond. Mineralogical features of the Jack Hills diamonds-such as their occurrence in zircon, their association with graphite and their Raman spectroscopic characteristics-resemble those of diamonds formed during ultrahigh-pressure metamorphism and, unless conditions on the early Earth were unique, imply a relatively thick continental lithosphere and crust-mantle interaction at least 4,250 million years ago. Research on .3,900-Myr-old zircon grains from the Jack Hills has mainly focused on their chemical and isotopic compositions. In this study, we have investigated the mineral inclusions in 1,000 randomly chosen zircon grains, collected from a sample of the Jack Hills conglomerate from the initial 'discovery' site 1. U-Pb ages of the zircon grains were determined using a sensitive high-resolution ion microprobe (SHRIMP II), following the analytical procedure described in detail elsewhere 11,12. Optical examination revealed that about half of the zircon population contains inclusions larger than ,1 mm. These inclusions were further investigated by confocal micro-Raman spectroscopy. A Raman spectrum of a mineral reflects both its structural and chemical properties and can thus be used as a fingerprint for its identification. We have identified apatite, quartz, xenotime, monazite, rutile, biotite, amphibole, K-feldspar and plagioclase inclusions in the Jack Hills zircon, some of which have also been recognized in previous studies 3,4. In addition, we unambiguously identified diamond inclusions in 45 zircon grains, mostly associated with graphite, but in some cases with apatite and quartz. The Raman spectrum of diamond is characterized by a single firstorder band near 1,332 cm 21 with a typical width (full-width at halfmaximum, FWHM) of ,1.7 cm 21 , reflecting a triply degenerate CC stretching vibration with F 2g symmetry, and sometimes by a secondorder feature near 2,600 cm 21 (ref. 13). The Raman spectra of most of the diamond inclusions ('diamond 1') in the Jack Hills zircons Nature

Many research groups are estimating trees containing anywhere from a few thousands to hundreds of thousands of species, toward the eventual goal of the estimation of a Tree of Life, containing perhaps as many as several million leaves. These phylogenetic estimations present enormous computational challenges, and current computational methods are likely to fail to run even on data sets in the low end of this range. One approach to estimate a large species tree is to use phylogenetic estimation methods (such as maximum likelihood) on a supermatrix produced by concatenating multiple sequence alignments for a collection of markers; however, the most accurate of these phylogenetic estimation methods are extremely computationally intensive for data sets with more than a few thousand sequences. Supertree methods, which assemble phylogenetic trees from a collection of trees on subsets of the taxa, are important tools for phylogeny estimation where phylogenetic analyses based upon maximum likelihood (ML) are infeasible. In this paper, we introduce SuperFine, a metamethod that utilizes a novel two-step procedure in order to improve the accuracy and scalability of supertree methods. Our study, using both simulated and empirical data, shows that SuperFine-boosted supertree methods produce more accurate trees than standard supertree methods, and run quickly on very large data sets with thousands of sequences. Furthermore, SuperFine-boosted matrix representation with parsimony (MRP, the most well-known supertree method) approaches the accuracy of ML methods on supermatrix data sets under realistic conditions.

In an increasing proportion of cases, hepatocellular carcinoma (HCC) develops in patients with nonalcoholic fatty liver disease (NAFLD). Mutations in telomerase reverse transcriptase (hTERT) are associated with familial liver diseases. The aim of this study was to examine telomere length and germline hTERT mutations as associated with NAFLD-HCC. In 40 patients with NAFLD-HCC, 45 with NAFLD-cirrhosis and 64 healthy controls, peripheral blood telomere length was evaluated by qRT-PCR and hTERT coding regions and intron-exon boundaries sequenced. We further analyzed 78 patients affected by primary liver cancer (NAFLD-PLC, 76 with HCC). Enrichment of rare coding mutations (allelic frequency <0.001) was evaluated by Burden test. Functional consequences were estimated in silico and by over-expressing protein variants in HEK-293 cells. We found that telomere length was reduced in individuals with NAFLD-HCC versus those with cirrhosis (P = 0.048) and healthy controls (P = 0.0006), indepen...

A sample-sequencing strategy combined with slot-blot hybridization and FISH was used to study the composition of the repetitive component of the sunflower genome. One thousand six hundred thirty-eight sequences for a total of 954,517 bp were analyzed. The fraction of sequences that can be classified as repetitive using computational and hybridization approaches amounts to 62% in total. Almost two thirds remain as yet uncharacterized in nature. Of those characterized, most belong to the gypsy superfamily of LTR-retrotransposons. Unlike in other species, where single families can account for large fractions of the genome, it appears that no transposon family has been amplified to very high levels in sunflower. All other known classes of transposable elements were also found. One family of unknown nature (contig 61) was the most repeated in the sunflower genome. The evolution of the repetitive component in the Helianthus genus and in other Asteraceae was studied by comparative analysis of the hybridization of total genomic DNAs from these species to the sunflower smallinsert library and compared to gene-based phylogeny. Very little similarity is observed between Helianthus species and two related Asteraceae species outside of the genus. Most repetitive elements are similar in annual and perennial Helianthus species indicating that sequence amplification largely predates such divergence. Gypsy-like elements are more represented in the annuals than in the perennials, while copia-like elements are similarly represented, attesting a different amplification history of the two superfamilies of LTR-retrotransposons in the Helianthus genus. Sequences from Helianthus annuus randomly sheared genomic DNA library and sequences of genes used for phylogenetic analyses are available at the URL: https://services.appliedgenomics.org/sequencesexport/26-Helianthus/.

Recent progress in genomics and experimental biology has brought exponential growth of the biological information available for computational analysis in public genomics databases. However, applying the potentially enormous scientific value of this information to the understanding of biological systems requires computing and data storage technology of an unprecedented scale. The grid, with its aggregated and distributed computational and storage infrastructure, offers an ideal platform for high-throughput bioinformatics analysis. ...

Drug-drug interactions (DDIs) are responsible for many serious adverse events; their detection is crucial for patient safety but also very challenging. In recent years, several drugs have been withdrawn from the market due to interaction related Adverse Events (AEs). Currently, the US Food and Drug Administration (FDA) and pharmaceutical companies are showing great interest in the development of improved tools for identifying DDIs.

We describe a predictive model, applicable on a large scale to predict novel DDIs based on similarity of drug interaction candidates to drugs involved in established DDIs. The underlying assumption is that if drug A and drug B interact to produce a specific biological effect, then drugs similar to drug A (or drug B) are likely to interact with drug B (or drug A) to produce the same effect. We constructed a 352 drug DDI network from a 2011 snapshot of a widely-used drug safety database, which contains 3 700 established DDIs, and used it to develop the proposed model for predicting future DDIs. The target similarity for all selected pairs of drugs in DrugBank was computed to identify DDI candidates. The proposed model mainly follows two distinct approaches: the first one is ‘Which forces the preservation of existing (known) DDIs’ and the other one is ‘without forced to preserve existing DDIs.’ Predictions were made under each of these approaches using three different techniques: target similarity score, side effect similarity (P-score) and resulting score.

The methodology was evaluated using Drugbank 2014 snapshot as a gold standard for the same set of drugs. The proposed model generates novel DDIs with an average accuracy of 95% for force to preserve existing (known) DDIs. Average accuracy for without forced to preserve existing DDIs is 92%. These two approaches also give average AUC (Area Under the Curve) of 0.9834 and 0.8651 respectively.

The results presented in this study demonstrate the usefulness of the proposed network based drug-drug interaction methodology as a promising approach. The method described in this article is very simple, efficient, and biologically sound.

Keywords— Drug-drug Interactions, Adverse Events, Target similarity score, P-score.

One of the central problems in modern biology is to identify the complete set of interactions among the proteins in a cell. The structural interaction of proteins and their domains in networks is one of the most basic molecular mechanisms for biological cells. Structural evidence indicates that, interacting pairs of close homologs usually interact in the same way. In this article, we make use of both homology and inter-domain linker region knowledge to predict interaction between protein pairs solely by amino acid sequence information. High quality core set of 150 yeast proteins obtained from the Database of Interacting Proteins (DIP) was considered to test the accuracy of the proposed method. The strongest prediction of the method reached over 70% accuracy. These results show great potential for the proposed method.

Power spectral analysis of the heart rate and blood pressure variations has commonly used to provide indices of autonomic cardiovascular modulation, but the effect of changing posture from lying to standing on these variations and the interpretation of their power spectra is still largely in dispute. It was due to the reason that till now no study was made yet that clearly outlines the variations in terms of RR-interval and blood pressure series from lying to standing position. Thus the aim of this paper lies in the application of classifying the subjects based on their RR-intervals, systolic and diastolic blood pressure series, prior to spectral analysis, at two different physical activity related postures. In this paper K-Nearest Neighbor algorithm has been proposed as a classifier for classifying the subjects based on lying and standing postures. Here we also studied the classification accuracy achievable with a KNN classifier using three different methods (i) Euclidean (ii) City block and (iii) Correlation of calculating the nearest distance in order to propose the optimal one. Further an attempt has been made to evaluate each of these methods for five different values of K=1, 3, 5, 7 and 9 in order to propose the best fit value of K for classifying the subjects. After performing the comparative analysis between these three methods of distance metrics and for different values of K, it is found that K=1 is the best choice out of 3, 5, 7 and 9 and Correlation has been emerged as one of the optimal method for computing the nearest distance with highest classification accuracy of 98.60 % with K=1 for lying and 99.95 % with K=1 for standing postures.

Biomedical informatics lacks a clear and theoretically grounded definition. Many proposed definitions focus on data, information, and knowledge, but do not provide an adequate definition of these terms. Leveraging insights from the philosophy of information, we define informatics as the science of information, where information is data plus meaning. Biomedical informatics is the science of information as applied to or studied in the context of biomedicine. Defining the object of study of informatics as data plus meaning clearly distinguishes the field from related fields, such as computer science, statistics and biomedicine, which have different objects of study. The emphasis on data plus meaning also suggests that biomedical informatics problems tend to be difficult when they deal with concepts that are hard to capture using formal, computational definitions. In other words, problems where meaning must be considered are more difficult than problems where manipulating data without regard for meaning is sufficient. Furthermore, the definition implies that informatics research, teaching, and service should focus on biomedical information as data plus meaning rather than only computer applications in biomedicine.