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Öğe Correction to: Music of metagenomics—a review of its applications, analysis pipeline, and associated tools (Functional & Integrative Genomics, (2022), 22, 1, (3-26), 10.1007/s10142-021-00810-y)(Springer Science and Business Media Deutschland GmbH, 2022) Wajid, Bilal; Anwar, Faria; Wajid, Imran; Nisar, Haseeb; Meraj, Sharoze; Zafar, Ali; Al‑Shawaqfeh, Mustafa Kamal; Ekti, Ali Riza; Khatoon, Asia; Suchodolski, Jan S.The co-author Imran Wajid would like to update his second affiliation address to “School of Social Sciences, Istanbul Commerce University, Istanbul, Turkey”. The original article has been corrected.Öğe MetumpX-a metabolomics support package for untargeted mass spectrometry(OXFORD UNIV PRESS, 2020) Wajid, Bilal; Iqbal, Hasan; Jamil, Momina; Rafique, Hafsa; Anwar, FariaMotivation: Metabolomics is a data analysis and interpretation field aiming to study functions of small molecules within the organism. Consequently Metabolomics requires researchers in life sciences to be comfortable in downloading, installing and scripting of software that are mostly not user friendly and lack basic GUIs. As the researchers struggle with these skills, there is a dire need to develop software packages that can automatically install software pipelines truly speeding up the learning curve to build software workstations. Therefore, this paper aims to provide MetumpX, a software package that eases in the installation of 103 software by automatically resolving their individual dependencies and also allowing the users to choose which software works best for them. Results: MetumpX is a Ubuntu-based software package that facilitate easy download and installation of 103 tools spread across the standard metabolomics pipeline. As far as the authors know MetumpX is the only solution of its kind where the focus lies on automating development of software workstations.Öğe Music of metagenomics—a review of its applications, analysis pipeline, and associated tools(Springer, 2021) Wajid, Bilal; Anwar, Faria; Wajid, Imran; Nisar, Haseeb; Meraj, Sharoze; Zafar, Ali; Al‑Shawaqfeh, Mustafa Kamal; Ekti, Ali Rıza; Khatoon, Asia; Suchodolsk, Jan S.This humble efort highlights the intricate details of metagenomics in a simple, poetic, and rhythmic way. The paper enforces the signifcance of the research area, provides details about major analytical methods, examines the taxonomy and assembly of genomes, emphasizes some tools, and concludes by celebrating the richness of the ecosystem populated by the “metagenome.”Öğe Survivability Period Prediction in Colon Cancer Patients using Machine Learning(Institute of Electrical and Electronics Engineers Inc., 2023) Tahir, Anoosha; Wajid, Bilal; Anwar, Faria; Awan, Fahim Gohar; Rashid, Umar; Afzal, Fareeha; Anwar, Abdul Rauf; Wajid, ImranKnowledge of survivability is crucial for cancer patients and their families. This paper employs the Surveillance, Epidemiology, and End Results (SEER) database to predict the survivability of colon cancer patients. The research presents four experiments each improving over the previous one, attempting to develop the optimal model. Here (i) experiment 1 conducts regression analyses; (ii) experiment 2 conducts multinomial classification; (iii) experiment 3 emphasizes a multi-tier prediction framework and lastly; (iv) experiment 4 concludes by developing a hybrid model for better prediction of survivability.Öğe Survival Rate Prediction of Blood Cancer (Leukemia) Patients Using Machine Learning Algorithms(Institute of Electrical and Electronics Engineers Inc., 2022) Zarish; Wajid, Bilal; Rashid, Umar; Zahid, Sajida; Anwar, Faria; Awan, Fahim Gohar; Anwar, Abdul Rauf; Wajid, ImranSurvival rate prediction for medical diseases is a complex task that requires high precision. With a low survival rate among reported patients, leukemia is a type of cancer of blood which is caused by the abnormal growth of white blood cells. It is critical to numerically evaluate the rate of survivability of patients suffering from leukemia. To this end, this paper employs a comprehensive database, namely Surveillance, Epidemiology, and End Results (SEER) maintained by The National Cancer Institute in MD, USA, to construct a survivability model for leukemia patients. To accurately predict the survival months of the patients, we develop a multi-class classification problem by binning the target variable into four bins. The resulting accuracy is improved by utilizing a multi-tier classification framework. Although, the final numerical results hold significance from biological viewpoint, it is recommended that a clinically relevant model be drawn with caution.Öğe Unearthing Insights into Metabolic Syndrome by Linking Drugs, Targets, and Gene Expressions Using Similarity Measures and Graph Theory(Bentham Science Publishers, 2024) Zafar, Alwaz; Wajid, Bilal; Shabbir, Ans; Awan, Fahim Gohar; Ahsan, Momina; Ahmad, Sarfraz; Wajid, Imran; Anwar, Faria; Mazhar, FazeelatAims and Objectives: Metabolic syndrome (MetS) is a group of metabolic disorders that includes obesity in combination with at least any two of the following conditions, i.e., insulin resistance, high blood pressure, low HDL cholesterol, and high triglycerides level. Treatment of this syndrome is challenging because of the multiple interlinked factors that lead to increased risks of type-2 diabetes and cardiovascular diseases. This study aims to conduct extensive insilico analysis to (i) find central genes that play a pivotal role in MetS and (ii) propose suitable drugs for therapy. Our objective is to first create a drug-disease network and then identify novel genes in the drug-disease network with strong associations to drug targets, which can help in increasing the therapeutical effects of different drugs. In the future, these novel genes can be used to calculate drug synergy and propose new drugs for the effective treatment of MetS. Methods: For this purpose, we (i) investigated associated drugs and pathways for MetS, (ii) employed eight different similarity measures to construct eight gene regulatory networks, (iii) chose an optimal network, where a maximum number of drug targets were central, (iv) determined central genes exhibiting strong associations with these drug targets and associated disease-causing pathways, and lastly (v) employed these candidate genes to propose suitable drugs. Results: Our results indicated (i) a novel drug-disease network complex, with (ii) novel genes associated with MetS. Conclusion: Our developed drug-disease network complex closely represents MetS with associated novel findings and markers for an improved understanding of the disease and suggested therapy.Öğe Whole-genome sequencing as a first-tier diagnostic framework for rare genetic diseases(Sage, 2021) Nisar, Haseeb; Wajid, Bilal; Shahid, Samiah; Anwar, Faria; Wajid, Imran; Khatoon, Asia; Sattar, Mian Usman; Sadaf, SaimaRare diseases affect nearly 300 million people globally with most patients aged five or less. Traditional diagnostic approaches have provided much of the diagnosis; however, there are limitations. For instance, simply inadequate and untimely diagnosis adversely affects both the patient and their families. This review advocates the use of whole genome sequencing in clinical settings for diagnosis of rare genetic diseases by showcasing five case studies. These examples specifically describe the utilization of whole genome sequencing, which helped in providing relief to patients via correct diagnosis followed by use of precision medicine.
