6^th International Conference on Bioinformatics & Systems Biology August 22-23, 2016 Philadelphia, Pennsylvania, USA

Hichem Metmer received his Bachelor’s degree in Computer Science and Engineering from the University of Batna(Algeria) in 2013. Actually, he is a research student in the Department of Computer Science and Engineering, Nanjing University of Science and Technology, NUST (China). His research interests are in: Brain imaging analysis, pattern recognition and image processing.

The major current research on aging and dementia is focused on diagnostics of the cognitive changes of normal aging and Alzheimer’s disease (AD) and these changes are known as Mild Cognitive Impairment (MCI). In this study, we applied medicalrntechnology and this research has come so far that in recent years it is possible for imaging scans to find out abnormalities and diagnose diseases. Functional Magnetic Resonance Imaging (fMRI) is a useful tool to investigate the modifications in functional connectivity and also allows the evaluation of brain changes that occurs in the progression from healthy aging to MCI and AD. Using fMRI, many studies have extensively investigated functioning and anatomical correlates of the default mode network (DMN) including the Medial Prefrontal Cortex (MPFC), the Posterior Cingulate Cortex (PCC), the Inferior Parietal Lobe (IPL) and the Hippocampus (HP). More importantly, with functional Magnetic Resonance Imaging (fMRI), we investigated resting state network (RSN) activities and this resting state (Rs) fMRI data were analyzed by using Independent Component Analysis (ICA). The aim of this study was to identify the modifications of brain functional connectivity in MCI patients and to show how they can be progressed to AD. As there are no treatments recommended for MCI currently, Mild Cognitive Impairment is becoming a very important subject for researchers andrndeserves more recognition and further study, in order to increase the ability to recognize earlier symptoms of Alzheimer’s disease.

Md Shariful Islam is studying BSc final year in Biotechnology and Genetic Engineering Department, Mawlana Bhashani Science and Technology University, Tangail-1902, Bangladesh. Currently, he is working on “bioinformatics based risk prediction analysis of cancer which is so much rare and initiative in worldwide for\r\nthe risk prediction analysis of cancer”.

Background: Ovarian cancer is the most lethal gynecological cancer which is increasing day by day in developing countries. More than 8 out of 10 (80%) ovarian cancers occur in women over the age of 50. Therefore, identification of genetic factors including mutations in the BRCA1 and BRCA2 gene (breast cancer gene) as well as others factors is very important in developing novel\r\nmethods of ovarian cancer prevention.\r\n\r\nMaterials & Methods: This study was carried out in 521 cancer and non-cancer patients’, data was collected from different diagnostic centres and data was pre-processed. Then a structured questionnaire was used containing details of ovarian cancer risk factors including age, menopause end age, problem during pregnancy, first sex age, any infection in genital area, affected by ovarian cancer, abortion, pregnancy, BMI, menopause after 50, food habit, obesity, excessive alcohol, late menopause, early menopause, hormone therapy, exercise, previous exposure to other sexually transmitted infections (STIs), marital status, genetic risk, outdoor activities and affected any cancer before based on the previous studies.\r\n\r\nResults: After pre-processing, data was clustered using K-means clustering algorithm for identifying relevant and non-relevant data to ovarian cancer. Next significant frequent patterns were discovered using AprioriTid and Decision Tree algorithm. This ovarian cancer risk prediction system will be helpful in detection of a patient’s predisposition to ovarian cancer. Specifically there was no work of ovarian cancer risk prediction system using data mining or statistical approaches.\r\n\r\nConclusions: Most of the Bangladeshi woman does not even know they have ovarian cancer and the majority of cases are diagnosed at late stages when cure is impossible. Therefore early prediction of ovarian cancer should play a pivotal role in the diagnosis process and for an effective preventive strategy.