Total papers published in IF journals - 8,
Total Impact - ~ 32
Citations - 45
Conference Publications - 2
Ongoing research - Unpublished manuscripts - 3
Thesis - 3 (Ph.D, M.S., and B.S.)
Khan A.A., Ali R.H., and Mirza B. (2020),
Evolutionary history of Alzheimer disease-causing protein family Presenilins with pathological implications,
Journal of Molecular Evolution.
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Impact Factor 1.821
Ali R.H., Bogusz M., Whelan S. (2019),
Identifying Clusters of High Confidence Homologies in Multiple Sequence Alignments,
Molecular Biology and Evolution 10(1093):142.
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Impact Factor: 14.787
Mushtaq M., Ali R.H., Kashuba V., Klein G., Kashuba E. (2016)
S18 family of mitochondrial ribosomal proteins: evolutionary history and Gly132 polymorphism in colon carcinoma,
Oncotarget 7(34):55649.
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Impact Factor: 5.168
Mohammad D.K., Ali R.H., Turunen J.J., Nore B.F., Smith C.I.E. (2016),
B Cell Receptor Activation Predominantly Regulates AKT-mTORC1/2 Substrates Functionally Related to RNA Processing,
PloS One 11(8):e0160255.
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Impact Factor: 2.766
Ali R.H., Muhammad S.A., Arvestad L. (2016)
GenFamClust: an accurate, synteny-aware and reliable homology inference algorithm,
BMC Evolutionary Biology 16(1):120.
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Impact Factor: 3.027
Ali R.H., Bark M., Jorge M., Muhammad S.A., Sjöstrand J., Zubair S.M., Abbas R.M., Arvestad L. (2014)
VMCMC: a graphical and statistical analysis tool for Markov chain Monte Carlo traces,
BMC Bioinformatics 18(1):97.
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Impact Factor: 2.213
Ali R.H., Khan A.A. (2014)
Tracing the evolution of FERM domain of Kindlins,
Molecular Phylogenetics and Evolution 80(1):193.
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Impact Factor 4.018
Ali R.H., Muhammad S.A., Arvestad L. (2013)
Quantitative synteny scoring improves homology inference and partitioning of gene families,
BMC Bioinformatics 14(15):S2.
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Impact Factor: 2.213
Ali N., Ansari S., Halim Z., Ali R.H., Khan M.F., and Khan M. (2019)
Breast cancer classification and proof of key artificial neural network terminologies,
13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS).
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Mushtaq M., Ali R.H., Kashuba V., Klein G., Kashuba E. (2015)
A link between the evolutionary history of mitochondrial ribosomal proteins of S18 family and GLY132 polymorphism in colon cancer,
Journal of World Mitochondria Society 2(1).
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Ali R.H., Boguzs M., Whelan S. “Evaluating the quality of multiple sequence alignment benchmarks for evolutionary analysis” [Manuscript].
Ali R.H., Arvestad L., “Normalized and standardized estimated sample size: two novel techniques for convergence assessment and burn-in estimation for multi-parameter Markov chain Monte Carlo traces” [Manuscript].
Ali R.H., Arvestad L., “GenFam: a gene family and homology database for Eukaryotes based on quantitative synteny and neighborhood correlation similarity scores” [Manuscript].
Developed and implemented a new statistical convergence assessment and burn-in estimation technique based on Estimated Sample Size (ESS) for multiple Markov chain Monte Carlo parameters for phylogenetic tree.
Developed software “VMCMC”, a visualization and statistical tool for Markov chain Monte Carlo analysis of phylogenetic trees.
Designed and implemented a synteny based algorithm “GenFamClust” that quantifies gene synteny and combines it with protein similarity to infer gene homology.
Compared and validated the accuracy, cluster size, similarity and differences between GenFamClust and other homology inference algorithms using Chordatic, Fungi and simulated datasets.
Applied “GenFamClust” algorithm on a large dataset consisting of whole genome species and inferred gene families and synteny conservation. The results are available in the database “GenFam”.
Determining the relationship between age and the degree of a gene class network graph.
Extended the original work to determine the relationship for pathway class graph as well.
Worked on a new notion of gene class and pathway class graphs.
Lip Synchronization with Speech signal, a mixture of many different domains of Computer Science.
Reflects my command of diverse Computer Science fields such as Digital Signal Processing, Computer Graphics, Artificial Intelligence and Artificial Neural Networks.