No abstract

Table of contents O1 Regulation of genes by telomere length over long distances Jerry W. Shay O2 The microtubule destabilizer KIF2A regulates the postnatal establishment of neuronal circuits in addition to prenatal cell survival, cell migration, and axon elongation, and its loss leading to malformation of cortical development and severe epilepsy Noriko Homma, Ruyun Zhou, Muhammad Imran Naseer, Adeel G. Chaudhary, Mohammed Al-Qahtani, Nobutaka Hirokawa O3 Integration of metagenomics and metabolomics in gut microbiome research Maryam Goudarzi, Albert J. Fornace Jr. O4 A unique integrated system to discern pathogenesis of central nervous system tumors Saleh Baeesa, Deema Hussain, Mohammed Bangash, Fahad Alghamdi, Hans-Juergen Schulten, Angel Carracedo, Ishaq Khan, Hanadi Qashqari, Nawal Madkhali, Mohamad Saka, Kulvinder S. Saini, Awatif Jamal, Jaudah Al-Maghrabi, Adel Abuzenadah, Adeel Chaudhary, Mohammed Al Qahtani, Ghazi Damanhouri O5 RPL27A is a target of miR-595 and deficiency contributes to ribosomal dysgenesis Heba Alkhatabi O6 Next generation DNA sequencing panels for haemostatic and platelet disorders and for Fanconi anaemia in routine diagnostic service Anne Goodeve, Laura Crookes, Nikolas Niksic, Nicholas Beauchamp O7 Targeted sequencing panels and their utilization in personalized medicine Adel M. Abuzenadah O8 International biobanking in the era of precision medicine Jim Vaught O9 Biobank and biodata for clinical and forensic applications Bruce Budowle, Mourad Assidi, Abdelbaset Buhmeida O10 Tissue microarray technique: a powerful adjunct tool for molecular profiling of solid tumors Jaudah Al-Maghrabi O11 The CEGMR biobanking unit: achievements, challenges and future plans Abdelbaset Buhmeida, Mourad Assidi, Leena Merdad O12 Phylomedicine of tumors Sudhir Kumar, Sayaka Miura, Karen Gomez O13 Clinical implementation of pharmacogenomics for colorectal cancer treatment Angel Carracedo, Mahmood Rasool O14 From association to causality: translation of GWAS findings for genomic medicine Ahmed Rebai O15 E-GRASP: an interactive database and web application for efficient analysis of disease-associated genetic information Sajjad Karim, Hend F Nour Eldin, Heba Abusamra, Elham M Alhathli, Nada Salem, Mohammed H Al-Qahtani, Sudhir Kumar O16 The supercomputer facility “AZIZ” at KAU: utility and future prospects Hossam Faheem O17 New research into the causes of male infertility Ashok Agarwa O18 The Klinefelter syndrome: recent progress in pathophysiology and management Eberhard Nieschlag, Joachim Wistuba, Oliver S. Damm, Mohd A. Beg, Taha A. Abdel-Meguid, Hisham A. Mosli, Osama S. Bajouh, Adel M. Abuzenadah, Mohammed H. Al-Q...

We propose Predictive Permutation Feature Selection (PPFS), a novel wrapper-based feature selection method based on the concept of Markov Blanket (MB). Unlike previous MB methods, PPFS is a universal feature selection technique as it can work for both classification as well as regression tasks on datasets containing categorical and/or continuous features. We propose Predictive Permutation Independence (PPI), a new Conditional Independence (CI) test, which enables PPFS to be categorised as a wrapper feature selection method. This is in contrast to current filter based MB feature selection techniques that are unable to harness the advancements in supervised algorithms such as Gradient Boosting Machines (GBM). The PPI test is based on the knockoff framework and utilizes supervised algorithms to measure the association between an individual or a set of features and the target variable. We also propose a novel MB aggregation step that addresses the issue of sample inefficiency. Empirical evaluations and comparisons on a large number of datasets demonstrate that PPFS outperforms state-of-the-art Markov blanket discovery algorithms as well as, well-known wrapper methods. We also provide a sketch of the proof of correctness of our method.

Abstract-Clinical studies in the past have shown that the pathology of Alzheimer's disease (AD) initiates, 10 to 15 years before the visible clinical symptoms of cognitive impairment starts to appear in AD diagnosed patients. Therefore, early diagnosis of the AD using potential early stage cerebrospinal fluid (CSF) biomarkers will be valuable in designing a clinical trial and proper care of AD patients. Therefore, the goal of our study was to generate a classification model to predict earlier stages of the AD using specific early-stage CSF biomarkers obtained from a clinical Alzheimer dataset. The dataset was segmented into variable sizes and classification models based on three machine learning (ML) algorithms, such as Sequential Minimal Optimization (SMO), Naïve Bayes (NB), and J48 were generated. The efficacy of the models to accurately predict the cognitive impairment status was evaluated and compared using various model performance parameters available in Weka software tool. The current findings show that J48 based classification model can be effectively employed for classifying cognitive impaired Alzheimer patient from normal healthy individuals with an accuracy of 98.82%, area under curve (AUC) value of 0.992 and sensitivity & specificity of 99.19% and 97.87%, respectively. The sample size (60% training and 40% independent test data) showed significant improvement in T-test with J48 algorithm when compared with other classifiers tested on Alzheimer dataset.

Purpose:To explore the use of restriction inhibition assay (RIA) to study the binding specificity of some anticancer drugs. Methods: A 448 bp DNA fragment derived from pBCKS+ plasmid (harboring the polylinker region with multiple restriction endonuclease sites) was used as a template for sequence selective inhibition of the test drugs. The template was incubated with different concentrations of anticancer drugs (adriamycin, daunomycin, mitoxantrone, distamycin-A, berberine and palmatine) prior to digestion with restriction endonucleases -HindIII, EcoRI and EcoRV. Results: Mitoxantrone, adriamycin and daunomycin showed specificity for HindIII restriction site (5'-AAGCTT-3') at 220, 100 and 100 µM concentration, respectively. Conversely, distamycin-A showed an affinity for EcoRI (5'-AAATGC-3') restriction sites at a concentration of 10 µM. No binding was observed for berberine and palmatine at a maximum concentration of 2 mM at HindIII, EcoRI and EcoRV restriction sites, respectively. Conclusion: The inhibition of endonucleases by mitoxantrone, adriamycin, daunomycin, distamycin-A, provides direct evidence of the co-existence of concentration and sequence specificity for drug-DNA interaction as well as the need to explore the possible use of RIA for demonstrating the binding specificity of anticancer drugs.