The 14th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB) aims to promote the interaction among the scientific community to discuss applications of CS/AI with an interdisciplinary character, exploring the interactions between sub-areas of CS/AI, Bioinformatics, Chemoinformatics and Systems Biology. However, developing such algorithms is crucial and critical in terms of exploring the knowledge of a physician in synchronizing with the algorithm development. The 12th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB) aims to promote the interaction among the scientific community to discuss applications of CS/AI with an interdisciplinary character, exploring the interactions between sub-areas of CS/AI, Bioinformatics, Chemoinformatics and Systems Biology. Basically, drug development is hindered by a high rate of failure regarding their toxicity and efficacy profiles. We will be providing unlimited waivers of publication charges for accepted research articles as well as case reports and case series related to COVID-19. In supervised method to train the model, a known set of genetic information is required (for example, the start and end of the gene, promotors, enhancers, active sites, functional regions, splicing sites, and regulatory regions) in order to set the predictive models. White et al., “Whole-genome random sequencing and assembly of Haemophilus influenzae Rd,”, E. S. Lander, “Initial impact of the sequencing of the human genome,”, L. A. After these outbreaks, more public health laboratories have started to utilize NGS technology. The machine-learning working mechanism is generally classified under four steps: filtering, data preprocessing, feature extraction, and model fitting and model evaluation. The medical advantage of computational biology is anticipated to boost the market during the forecast period. [141] to identify the perfect targets having the highest probability of binding with bioactive compounds. B. Aggarwal, D. Danda, S. Gupta, and P. Gehlot, “Models for prevention and treatment of cancer: problems vs promises,”, G. Francia and R. S. Kerbel, “Raising the bar for cancer therapy models,”, C. G. Begley and L. M. Ellis, “Raise standards for preclinical cancer research,”, J. M. L. Ebos, “Prodding the beast: assessing the Impact of treatment-induced metastasis,”, M. M. Gottesman, J. Ludwig, D. Xia, and G. Szakács, “Defeating drug resistance in cancer,”, K. S. Sherlach and P. D. Roepe, “Drug resistance associated membrane proteins,”, B. Mansoori, A. Mohammadi, S. Davudian, S. Shirjang, and B. Baradaran, “The different mechanisms of cancer drug resistance: a brief review,”, T. W. Synold, I. Dussault, and B. M. Forman, “The orphan nuclear receptor SXR coordinately regulates drug metabolism and efflux,”, Y.-Y. Some other RF-based scoring functions such as B2B score [136], SFC score RF [137], and RF-IChem [138] have been developed to calculate the docking scores. Standardized NGS tests have been adopted in many countries’ public laboratories for surveillance and in addition, NGS rated highly in specialized hospital laboratories [14, 15]. Based on the study, Ballester et al. 2019, Article ID 8427042, 15 pages, 2019., 1School of Humanities, Nanyang Technological University, 14 Nanyang Dr, Singapore, 2Singapore Institute of Manufacturing Technology, 2 Fusionopolis Way, Singapore, 3Department of Neuroscience Technology, College of Applied Medical Sciences, Imam Abdulrahman Bin Faisal University, Jubail 35816, Saudi Arabia, 4Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia. However, in clinical trials, most of the drugs are rejected due to toxicity and lack of efficacy. The final process is the variant calling, which is an important step for identifying correct variants/mutations from artifacts stemming from the prepared library, sequencing, mapping or alignment, and sample enrichment. In structure-based virtual screening, RF-score have been applied and performed well in identifying the targets. Chemical Equations Global Matching (also known as the Needleman-Wunsch problem) and Local Sequence Matching(also known as the Smith-Waterman problem) makes use of our knowledge about the proteins of an organism to understand more about other organisms proteins. As such, there are currently greater prospects for precision medicine to come into the foreground of cancer treatment. An automated integrated system, involving the analysis of genetic variants by deep/machine learning methods, molecular modeling, high throughput structure-based virtual screening, molecular docking, and molecular dynamics simulation methods, will enable rapid and accurate identification of precision drugs (Figure 2). The authors declared no conflicts of interest. Comparison of performance, strengths and weaknesses of promising sequencing platforms. This model is then used to find new genes that are similar to the genes of the training dataset. As such, specific modern computational algorithms are required to analyze and interpret the data. The field of bioinformatics experienced explosive growth starting in the mid-1990s, driven largely by the Human Genome Project and by rapid advances in … Supervised methods can only be used if a known training dataset of genetic codes available. Identifying all deleterious variants through experimental validation is quite complicated work since it would require large amounts of labor and resources. Van Walle, I. Chinen, J. Campos, E. Trees, and B. Gilpin, “Pulse Net International vision for the implementation of whole genome sequencing for global foodborne disease surveillance,”, M. Struelens, “Rapid microbial NGS and bioinformatics: translation into practice. The aim of predictive models built based on machine learning approaches to draw conclusions from a sample of past observations and to transfer these conclusions to the entire population. The number of potential drugs such as olaparib and iniparib showed promising results in preclinical stages. Using large amounts of aggregated data, the AI can discover and learn further transforming these data into “usable” knowledge. Achetez neuf ou d'occasion These predicted scores have been commonly used in medical genetics to identify the deleterious variant from the benign. However, other parameters such as accuracy, specificity, sensitivity, and area under the curve (AUC) were not completely evaluated. Later versions of DNA sequencing technology were able to generate short reads (50–400 bp) and long reads (1–100 kb). Next-generation sequencing tec… Machine learning methodologies have a wide range of application areas, and one of the most important applications is the identification of genetic variants and mutations [114, 118]. As we can see, artificial intelligence has acquired a key role in shaping the future of the health sector. Nuclear receptors and ATP-dependent membrane transporters are the primary factors that mediate the intrinsic cellular resistance [56]. Due to the availability of dense 3D measurements via technologies such as magnetic resonance imaging (MRI), computational anatomy has emerged as a subfield of medical imaging and bioengineering for extracting anatomical coordinate systems at the morphome scale in 3D. The difference of this track from many applied sessions at ECCB is that it bridge academia and other applications fields of computational biology and to cross-disseminate both sides. Results of the 10th International Conference on Practical Applications of Computational Biology & Bioinformatics held held in Sevilla, Spain, from 1st to 3rd June 2016 Discusses applications of Computational Intelligence with an interdisciplinary character, exploring the interactions between, Bioinformatics, Chemoinformatics and Systems Biology In a number of cases, tumors such as hepatocellular carcinoma, malignant melanoma, and renal cancer frequently show intrinsic resistance to anticancer drugs even without prior exposure to chemotherapy, resulting in a poor response during the initial stages of the treatment [5]. Fu, “Incorporating predicted functions of nonsynonymous variants into gene-based analysis of exome sequencing data: a comparative study,”, F. Gnad, A. Baucom, K. Mukhyala, G. Manning, and Z. Zhang, “Assessment of computational methods for predicting the effects of missense mutations in human cancers,”, C. Rodrigues, A. Santos-Silva, E. Costa, and E. Bronze-Da-Rocha, “Performance of in silico tools for the evaluation of UGT1A1 missense variants,”, E. König, J. Rainer, and F. S. Domingues, “Computational assessment of feature combinations for pathogenic variant prediction,”, S. Richards, N. Aziz, S. Bale et al., “Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology,”, X. Liu, C. Wu, C. Li, and E. Boerwinkle, “dbNSFP v3.0: a one-stop database of functional predictions and annotations for human nonsynonymous and splice-site SNVs,”, K. Wang, M. Li, and H. Hakonarson, “ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data,”. Theoretically, all mutations including in the genomic region or variant allele frequency (VAF) can be identified with sufficient read depth. Understanding the underlying mechanisms of the patient’s responses to cancer drugs and the unravelling of their genetic code would lead to the identification of new precision therapies that may improve the patient’s overall health and quality of life. The root cause of these cancers is often the modernized lifestyles [37–39]. Clearly, biology is increasingly becoming a science of information, requiring tools from the computational sciences. We use tools such as high performance computing with the aim of understanding and curing disease. Even though it is a challenging task to combine AI algorithms and computational chemistry to explore the chemical datasets in order to identify the potential drug candidates in high magnitude of time, the molecular mechanics/quantum mechanics inspired artificial intelligence developers will likely be widely used to speed up the process while keeping quantum mechanical precision. A. von Lilienfeld, “Big data meets quantum chemistry approximations: the Δ-machine learning approach,”, L. Shen, J. Wu, and W. Yang, “Multiscale quantum mechanics/molecular mechanics simulations with neural networks,”. Ain, A. Aleksandrova, F. D. Roessler, and P. J. Ballester, “Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening,”, P. J. Ballester and J. Tenure-Track Assistant Professor of Computational Biology. A. Bygraves, E. Feil et al., “Multilocus sequence typing: a portable approach to the identification of clones within populations of pathogenic microorganisms,”, R. Fleischmann, M. Adams, O. The primary role of those identified drugs is to achieve the highest therapeutic effect by eliminating tumor cells, with less adverse effects. Through the AI technology, the company has found two better drugs, which are more promising in killing Ebola virus. Moreover, acquired drug resistance induced by environmental and genetic factors that enhance the development of drug resistant tumor cell or induce mutations of genes involved in relevant metabolic pathways [61, 62]. For example, AutoDock Vina can be incorporated with RF-Score-VS-enhanced method to get better performance in the virtual screening. This technical combination truly supporting AI approaches become a live technique in drug discovery. The machine learning approach called convolutional neural networks (CNNs) applied to the identification of genetic variants and mutations. Future work in this area is expected to consider physicochemical properties and structural information of the target protein. Cornell has a university-wide plan in the science of genomics; the Department of Computer Science is playing a critical role in this initiative. Computational systems biology approaches to decipher cancer signaling pathways have been proposed as an essential mode to gain insight into biology of cancer cells. Most artifacts occur in less frequency rate and are less likely to create a problem since in this case homozygous reference would be the most likely genotype. It is necessary to bring radical change in the current computational methodology in order to identify precision drugs. In order to improve the scoring function performance, most of the AI techniques adopted the five major algorithms, namely, SVM, Bayesian, RF, deep neural network, and feed-forward ANN approaches. It has integrated the functional consequences of allele frequencies, different computational methods, and other clinical and genetic information associated with all possible coding variants [97]. The methodology combined with the collection of genetic variants, prediction of pathogenicity using various computational tools, modeling the protein three-dimensional structure with particular variant/s, molecular docking of standard drug with variant/mutant structures, virtual screening to identify the specific drug, and performing molecular dynamics simulation allow for a better understanding of the efficacy of the drug (Figure 1). The working mechanism and performance have been extensively discussed in many review articles [17, 18]. Predictions made from computational modelling can be interrogated using functional genomics screens and orthogonal sequencing, proteomics and high-throughput imaging approaches. Mills, “Overcoming implementation challenges of personalized cancer therapy,”, F. Bray, J. Ferlay, I. Soerjomataram, R. L. Siegel, L. A. Torre, and A. Jemal, “Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries,”, M. M. Jemal, J. Ludwig, D. Xia, and G. Szakacs, “Defeating drug resistance in cancer,”, M. M. Gottesman, “Mechanisms of cancer drug resistance,”, F. Sanger, S. Nicklen, and A. R. Coulson, “DNA sequencing with chain-terminating inhibitors,”, M. C. J. Maiden, J. For somatic variant calling unified haplotype and genotype calling algorithms have been used, but the core algorithms are not formulated for this analysis following that it performs poorly for low-frequency somatic variants, and this information is highlighted in some independent studies as well as in the GATK documentation [32, 33]. A reason for the majority of global deaths is the occurrence of noncommunicable diseases (NCDs) [35]. RF-Score-VS is the enhanced (DUD-E) scoring function that was trained on the full directory of useful decoy data sets (a set of 102 targets was docked with 15,426 active and 893,897 inactive ligands) [142]. Between 1975 and 2005, the Sanger method was the predominant sequencing methodology. The high cost of drug development will probably affect the ability of patients with financial limitations to acquire the treatment. A. Second, the processed reads are mapped with the reference genome to identify the sequence, which is followed by base-by-base alignment. Van Der Reijden, E. Hellstrom-Lindberg, and J. H. Jansen, “Evaluating variant calling tools for non-matched next generation sequencing data,”, V. Bansal, “A statistical method for the detection of variants from next-generation resequencing of DNA pools,”, A. R. Omran, “The epidemiologic transition: a theory of the epidemiology of population change,”, O. Gersten and J. R. Wilmoth, “The cancer transition in Japan since 1951,”, F. Bray, “Transitions in human development and the global cancer burden,” in, M. Maule and F. Merletti, “Cancer transition and priorities for cancer control,”, J. Ferlay, M. Colombet, I. Soerjomataram et al., “Global and Regional Estimates of the Incidence and Mortality for 38 Cancers,”, D. M. Parkin, F. Bray, J. Ferlay, and P. Pisani, “Global cancer statistics, 2002,”, A. Jemal, F. Bray, M. M. Center, J. Ferlay, E. Ward, and D. Forman, “Global cancer statistics,”, L. A. Torre, F. Bray, R. L. Siegel, J. Ferlay, J. Lortet-Tieulent, and A. Jemal, “Global cancer statistics, 2012,”, C. P. Adams and V. V. Brantner, “Estimating the cost of new drug development: is it really $802 million?”, J. A number of studies have been performed by utilizing different computational approaches to identify the precision drugs that are suitable to particular genetic variant/s [91–94]. Review articles are excluded from this waiver policy. Global Matching 2. An investigation has to be made further in examining the drug-gable targets other than the reputed signaling molecules. By utilizing the full capacity of a sequencing machine, the cost can be effectively further reduced. ANI also has the caliber to deeply analyze the data set, find new correlation, draw conclusion, and support physicians. This book highlights the latest research on practical applications of computational biology and bioinformatics, and addresses emerging experimental and sequencing techniques that are posing new challenges for bioinformatics and computational biology. Later in the early 2000s, another new technology emerged, namely, next generation sequencing (NGS) technology, which truly revolutionized the DNA sequencing process by reducing the time, cost, and labor. The position is for a fixed-term period of 3 years with the possibility of a 4th year. Without such AI technology, such a drug discovery would take several years, however, with the AI system will doing it in less than one day [113]. The strong generalization and learning process and machine-learning methods implementing aspects of AI models have been successfully implemented in different stages of the virtual screening pipeline. The RF-based RF-score [128], SVM-based ID-score [130], and ANN-based NNScore are the AI-based non-predetermined scoring functions that have been developed to identify potential ligands with high accuracy rate. ‎This book features 21 papers spanning many different sub-fields in bioinformatics and computational biology, presenting the latest research on the practical applications to promote fruitful interactions between young researchers in different areas related to the field. General pipeline of computational analysis of the brain transcriptome Brain samples are collected and the expression of all genes in each region is profiled by either microarray or next-generation sequencing. In most cases for the missense variant identification tool development, all these methods have been adopted [88–90] and those tools are utilized in our studies [91–94]. Genomic data used in machine learning models are classified under three categories 60% as training data, 30% as model testing data, and 10% as model validation data. However, such a period is followed by a poor outcome, as cancer responds well to chemotherapy initially but later shows resistance due to development of resistance. Additionally, computational pharmacology also uses tools of computational biology to visualize and simulate … The recent advanced AI-based non-predetermined scoring methods outperform well in comparison with classical approaches in binding affinity predictions that have been discussed in several reviews [131–133]. Next-generation sequencing tec… Superintelligence: paths, dangers, strategies,” 2014. © 2020 Springer Nature Switzerland AG. In some other cases, a chemotherapy agent may initially show its desired outcome. For single nucleotide variation and short indels (typically size ≤10 bp), the primary procedure is to check for nonreference nucleotide bases from the stack of sequence that cover each position.