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Murray Patterson
Murray D. Patterson
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2020 – today
- 2024
- [j31]Zahra Tayebi, Sarwan Ali
, Taslim Murad, Imdadullah Khan
PseAAC2Vec protein encoding for TCR protein sequence classification. Comput. Biol. Medicine 170: 107956 (2024) - [j30]Sarwan Ali
Molecular sequence classification using efficient kernel based embedding. Inf. Sci. 679: 121100 (2024) - [j29]Murray Patterson
Special Issue, Part I 19th International Symposium on Bioinformatics Research and Applications (ISBRA 2023). J. Comput. Biol. 31(6): 473-474 (2024) - [j28]Sarwan Ali
Elliptic geometry-based kernel matrix for improved biological sequence classification. Knowl. Based Syst. 304: 112479 (2024) - [j27]Sarwan Ali
From PDB files to protein features: a comparative analysis of PDB bind and STCRDAB datasets. Medical Biol. Eng. Comput. 62(8): 2449-2483 (2024) - [c42]Sarwan Ali, Haris Mansoor, Prakash Chourasia, Yasir Ali, Murray Patterson:
Gaussian Beltrami-Klein Model for Protein Sequence Classification: A Hyperbolic Approach. ISBRA (1) 2024: 52-62 - [c41]Sarwan Ali, Tamkanat E. Ali
A Universal Non-parametric Approach for Improved Molecular Sequence Analysis. PKDD (4) 2024: 194-206 - [c40]Taslim Murad, Sarwan Ali, Murray Patterson:
Weighted Chaos Game Representation for Molecular Sequence Classification. PKDD (4) 2024: 234-245 - [i31]Sarwan Ali, Tamkanat E. Ali, Prakash Chourasia, Murray Patterson:
A Universal Non-Parametric Approach For Improved Molecular Sequence Analysis. CoRR abs/2402.08117 (2024) - [i30]Sarwan Ali, Prakash Chourasia, Murray Patterson:
Expanding Chemical Representation with k-mers and Fragment-based Fingerprints for Molecular Fingerprinting. CoRR abs/2403.19844 (2024) - [i29]Sarwan Ali, Prakash Chourasia, Bipin Koirala, Murray Patterson:
Nearest Neighbor CCP-Based Molecular Sequence Analysis. CoRR abs/2409.04922 (2024) - [i28]Taslim Murad, Prakash Chourasia, Sarwan Ali, Murray Patterson:
DANCE: Deep Learning-Assisted Analysis of Protein Sequences Using Chaos Enhanced Kaleidoscopic Images. CoRR abs/2409.06694 (2024) - [i27]Sarwan Ali, Taslim Murad, Prakash Chourasia, Haris Mansoor, Imdadullah Khan, Pin-Yu Chen, Murray Patterson:
Position Specific Scoring Is All You Need? Revisiting Protein Sequence Classification Tasks. CoRR abs/2410.12655 (2024) - [i26]Sarwan Ali, Prakash Chourasia, Haris Mansoor, Bipin Koirala, Murray Patterson:
MIK: Modified Isolation Kernel for Biological Sequence Visualization, Classification, and Clustering. CoRR abs/2410.15688 (2024) - 2023
- [j26]Mukul S. Bansal
Special Issue: 11th International Computational Advances in Bio and Medical Sciences (ICCABS 2021). J. Comput. Biol. 30(4): 363-365 (2023) - [j25]Sarwan Ali, Babatunde Bello, Zahra Tayebi, Murray Patterson
Characterizing SARS-CoV-2 Spike Sequences Based on Geographical Location. J. Comput. Biol. 30(4): 432-445 (2023) - [j24]Prakash Chourasia, Sarwan Ali, Simone Ciccolella, Gianluca Della Vedova, Murray Patterson
Reads2Vec: Efficient Embedding of Raw High-Throughput Sequencing Reads Data. J. Comput. Biol. 30(4): 469-491 (2023) - [j23]Sarwan Ali
ViralVectors: compact and scalable alignment-free virome feature generation. Medical Biol. Eng. Comput. 61(10): 2607-2626 (2023) - [j22]Taslim Murad, Sarwan Ali
Spike2CGR: an efficient method for spike sequence classification using chaos game representation. Mach. Learn. 112(10): 3633-3658 (2023) - [j21]Sarwan Ali
Efficient Approximate Kernel Based Spike Sequence Classification. IEEE ACM Trans. Comput. Biol. Bioinform. 20(6): 3376-3388 (2023) - [c39]Taslim Murad, Sarwan Ali, Prakash Chourasia, Haris Mansoor, Murray Patterson:
Circular Arc Length-Based Kernel Matrix For Protein Sequence Classification. IEEE Big Data 2023: 1429-1437 - [c38]Sarwan Ali, Babatunde Bello, Prakash Chourasia, Ria Thazhe Punathil, Pin-Yu Chen, Imdadullah Khan, Murray Patterson:
Counterfactually Guided Policy Transfer in Clinical Settings. CHIL 2023: 6-18 - [c37]Mansoor Ahmed
Robust Brain Age Estimation via Regression Models and MRI-Derived Features. ICCCI (CCIS Volume) 2023: 661-674 - [c36]Sarwan Ali, Prakash Chourasia, Murray Patterson:
When Biology has Chemistry: Solubility And Drug Subcategory Prediction using SMILES Strings. Tiny Papers @ ICLR 2023 - [c35]Zahra Tayebi, Sarwan Ali, Prakash Chourasia, Taslim Murad, Murray Patterson:
T Cell Receptor Protein Sequences and Sparse Coding: A Novel Approach to Cancer Classification. ICONIP (10) 2023: 215-227 - [c34]Sarwan Ali, Taslim Murad, Murray Patterson:
PCD2Vec: A Poisson Correction Distance Based Approach for Viral Host Classification. IJCNN 2023: 1-8 - [c33]Prakash Chourasia, Zahra Tayebi, Sarwan Ali, Murray Patterson:
Empowering Pandemic Response with Federated Learning for Protein Sequence Data Analysis. IJCNN 2023: 1-8 - [c32]Taslim Murad, Sarwan Ali, Murray Patterson:
A New Direction in Membranolytic Anticancer Peptides classification: Combining Spaced k-mers with Chaos Game Representation. INNS DLIA@IJCNN 2023: 666-675 - [c31]Sarwan Ali, Pin-Yu Chen, Murray Patterson:
Unveiling the Robustness of Machine Learning Models in Classifying COVID-19 Spike Sequences. ISBRA 2023: 1-15 - [c30]Sarwan Ali, Usama Sardar, Imdadullah Khan
Efficient Sequence Embedding for SARS-CoV-2 Variants Classification. ISBRA 2023: 16-30 - [c29]Usama Sardar, Sarwan Ali, Muhammad Sohaib Ayub, Muhammad Shoaib, Khurram Bashir, Imdadullah Khan, Murray Patterson:
Sequence-Based Nanobody-Antigen Binding Prediction. ISBRA 2023: 227-240 - [c28]Sarwan Ali, Prakash Chourasia, Murray Patterson:
PDB2Vec: Using 3D Structural Information for Improved Protein Analysis. ISBRA 2023: 376-386 - [c27]Sarwan Ali, Haris Mansoor, Prakash Chourasia, Murray Patterson:
Hist2Vec: Kernel-Based Embeddings for Biological Sequence Classification. ISBRA 2023: 387-397 - [c26]Prakash Chourasia, Taslim Murad, Sarwan Ali, Murray Patterson:
Enhancing t-SNE Performance for Biological Sequencing Data Through Kernel Selection. ISBRA 2023: 442-452 - [c25]Zahra Tayebi, Akshay Juyal, Alexander Zelikovsky, Murray Patterson:
Simulating Tumor Evolution from scDNA-Seq as an Accumulation of both SNVs and CNAs. ISBRA 2023: 530-540 - [c24]Sarwan Ali, Prakash Chourasia, Murray Patterson:
Anderson Acceleration for Bioinformatics-Based Machine Learning. KDH@IJCAI 2023 - [c23]Sarwan Ali, Usama Sardar
BioSequence2Vec: Efficient Embedding Generation for Biological Sequences. PAKDD (2) 2023: 173-185 - [e2]Xuan Guo
Bioinformatics Research and Applications - 19th International Symposium, ISBRA 2023, Wrocław, Poland, October 9-12, 2023, Proceedings. Lecture Notes in Computer Science 14248, Springer 2023, ISBN 978-981-99-7073-5 [contents] - [i25]Sarwan Ali, Prakash Chourasia, Murray Patterson:
Anderson Acceleration For Bioinformatics-Based Machine Learning. CoRR abs/2302.00347 (2023) - [i24]Prakash Chourasia, Taslim Murad, Zahra Tayebi, Sarwan Ali, Imdadullah Khan
Efficient Classification of SARS-CoV-2 Spike Sequences Using Federated Learning. CoRR abs/2302.08688 (2023) - [i23]Taslim Murad, Sarwan Ali, Murray Patterson:
Exploring The Potential Of GANs In Biological Sequence Analysis. CoRR abs/2303.02421 (2023) - [i22]Sarwan Ali, Usama Sardar, Murray Patterson, Imdadullah Khan:
BioSequence2Vec: Efficient Embedding Generation For Biological Sequences. CoRR abs/2304.00291 (2023) - [i21]Sarwan Ali, Prakash Chourasia, Zahra Tayebi, Babatunde Bello, Murray Patterson:
ViralVectors: Compact and Scalable Alignment-free Virome Feature Generation. CoRR abs/2304.02891 (2023) - [i20]Sarwan Ali, Taslim Murad, Murray Patterson:
PCD2Vec: A Poisson Correction Distance-Based Approach for Viral Host Classification. CoRR abs/2304.06731 (2023) - [i19]Sarwan Ali, Babatunde Bello, Prakash Chourasia, Ria Thazhe Punathil, Pin-Yu Chen, Imdadullah Khan
Virus2Vec: Viral Sequence Classification Using Machine Learning. CoRR abs/2304.12328 (2023) - [i18]Zahra Tayebi, Sarwan Ali, Prakash Chourasia, Taslim Murad, Murray Patterson:
T Cell Receptor Protein Sequences and Sparse Coding: A Novel Approach to Cancer Classification. CoRR abs/2304.13145 (2023) - [i17]Mansoor Ahmed, Usama Sardar, Sarwan Ali, Shafiq Alam, Murray Patterson, Imdadullah Khan:
Robust Brain Age Estimation via Regression Models and MRI-derived Features. CoRR abs/2306.05514 (2023) - [i16]Usama Sardar
Sequence-Based Nanobody-Antigen Binding Prediction. CoRR abs/2308.01920 (2023) - 2022
- [j20]Kaustubh Khandai, Cristian Navarro-Martinez, Brendan Smith, Rebecca Buonopane, S. Ashley Byun, Murray Patterson
Determining Significant Correlation Between Pairs of Extant Characters in a Small Parsimony Framework. J. Comput. Biol. 29(10): 1132-1154 (2022) - [j19]Sarwan Ali
Efficient analysis of COVID-19 clinical data using machine learning models. Medical Biol. Eng. Comput. 60(7): 1881-1896 (2022) - [c22]Taslim Murad, Prakash Chourasia, Sarwan Ali, Murray Patterson:
Hashing2Vec: Fast Embedding Generation for SARS-CoV-2 Spike Sequence Classification. ACML 2022: 754-769 - [c21]Prakash Chourasia, Sarwan Ali, Murray Patterson:
Informative Initialization and Kernel Selection Improves t-SNE for Biological Sequences. IEEE Big Data 2022: 101-106 - [c20]Sarwan Ali, Taslim Murad, Prakash Chourasia, Murray Patterson:
Spike2Signal: Classifying Coronavirus Spike Sequences with Deep Learning. BigDataService 2022: 81-88 - [c19]Sarwan Ali
PSSM2Vec: A Compact Alignment-Free Embedding Approach for Coronavirus Spike Sequence Classification. ICONIP (7) 2022: 420-432 - [e1]Mukul S. Bansal
Computational Advances in Bio and Medical Sciences: 11th International Conference, ICCABS 2021, Virtual Event, December 16-18, 2021, Revised Selected Papers. Lecture Notes in Computer Science 13254, Springer International Publishing 2022, ISBN 978-3-031-17530-5 [contents] - [i15]Sarwan Ali, Babatunde Bello, Prakash Chourasia, Ria Thazhe Punathil, Yijing Zhou, Murray Patterson:
PWM2Vec: An Efficient Embedding Approach for Viral Host Specification from Coronavirus Spike Sequences. CoRR abs/2201.02273 (2022) - [i14]Sarwan Ali, Bikram Sahoo, Alexander Zelikovskiy, Pin-Yu Chen, Murray Patterson:
Benchmarking Machine Learning Robustness in Covid-19 Genome Sequence Classification. CoRR abs/2207.08898 (2022) - [i13]Sarwan Ali, Bikram Sahoo, Muhammad Asad Khan, Alexander Zelikovsky, Imdadullah Khan
Efficient Approximate Kernel Based Spike Sequence Classification. CoRR abs/2209.04952 (2022) - [i12]Prakash Chourasia, Sarwan Ali, Simone Ciccolella, Gianluca Della Vedova, Murray Patterson:
Reads2Vec: Efficient Embedding of Raw High-Throughput Sequencing Reads Data. CoRR abs/2211.08267 (2022) - [i11]Prakash Chourasia, Sarwan Ali, Murray Patterson:
Informative Initialization and Kernel Selection Improves t-SNE for Biological Sequences. CoRR abs/2211.09263 (2022) - 2021
- [j18]Zahra Tayebi, Sarwan Ali
Robust Representation and Efficient Feature Selection Allows for Effective Clustering of SARS-CoV-2 Variants. Algorithms 14(12): 348 (2021) - [j17]Simone Ciccolella
Inferring cancer progression from Single-Cell Sequencing while allowing mutation losses. Bioinform. 37(3): 326-333 (2021) - [j16]Andrew Melnyk, Fatemeh Mohebbi, Sergey Knyazev, Bikram Sahoo, Roya Hosseini
From Alpha to Zeta: Identifying Variants and Subtypes of SARS-CoV-2 Via Clustering. J. Comput. Biol. 28(11): 1113-1129 (2021) - [j15]Sarwan Ali
Simpler and Faster Development of Tumor Phylogeny Pipelines. J. Comput. Biol. 28(11): 1142-1155 (2021) - [j14]Simone Ciccolella, Murray Patterson
Effective Clustering for Single Cell Sequencing Cancer Data. IEEE J. Biomed. Health Informatics 25(11): 4068-4078 (2021) - [c18]Sarwan Ali, Murray Patterson
Spike2Vec: An Efficient and Scalable Embedding Approach for COVID-19 Spike Sequences. IEEE BigData 2021: 1533-1540 - [c17]Prakash Chourasia
Clustering SARS-CoV-2 Variants from Raw High-Throughput Sequencing Reads Data. ICCABS 2021: 133-148 - [c16]Sarwan Ali
A k-mer Based Approach for SARS-CoV-2 Variant Identification. ISBRA 2021: 153-164 - [c15]Brendan Smith, Cristian Navarro-Martinez, Rebecca Buonopane, S. Ashley Byun, Murray Patterson
Correlated Evolution in the Small Parsimony Framework. ISBRA 2021: 608-619 - [i10]Sarwan Ali, Bikram Sahoo, Naimat Ullah, Alexander Zelikovskiy, Murray Patterson, Imdadullah Khan:
A k-mer Based Approach for SARS-CoV-2 Variant Identification. CoRR abs/2108.03465 (2021) - [i9]Sarwan Ali, Tamkanat E. Ali, Muhammad Asad Khan, Imdadullah Khan
Effective and scalable clustering of SARS-CoV-2 sequences. CoRR abs/2108.08143 (2021) - [i8]Sarwan Ali, Murray Patterson:
Spike2Vec: An Efficient and Scalable Embedding Approach for COVID-19 Spike Sequences. CoRR abs/2109.05019 (2021) - [i7]Sarwan Ali, Babatunde Bello, Murray Patterson:
Classifying COVID-19 Spike Sequences from Geographic Location Using Deep Learning. CoRR abs/2110.00809 (2021) - [i6]Sarwan Ali, Yijing Zhou, Murray Patterson:
Efficient Analysis of COVID-19 Clinical Data using Machine Learning Models. CoRR abs/2110.09606 (2021) - [i5]Zahra Tayebi, Sarwan Ali, Murray Patterson:
Robust Representation and Efficient Feature Selection Allows for Effective Clustering of SARS-CoV-2 Variants. CoRR abs/2110.09622 (2021) - 2020
- [j13]Simone Ciccolella
gpps: an ILP-based approach for inferring cancer progression with mutation losses from single cell data. BMC Bioinform. 21-S(1): 413 (2020) - [c14]Andrew Melnyk
Clustering Based Identification of SARS-CoV-2 Subtypes. ICCABS 2020: 127-141
2010 – 2019
- 2019
- [j12]Raffaella Rizzi
Overlap graphs and de Bruijn graphs: data structures for de novo genome assembly in the big data era. Quant. Biol. 7(4): 278-292 (2019) - [c13]Simone Ciccolella
Effective Clustering for Single Cell Sequencing Cancer Data. BCB 2019: 437-446 - [c12]Giulia Bernardini
A Rearrangement Distance for Fully-Labelled Trees. CPM 2019: 28:1-28:15 - [i4]Giulia Bernardini, Paola Bonizzoni, Gianluca Della Vedova, Murray Patterson:
A rearrangement distance for fully-labelled trees. CoRR abs/1904.01321 (2019) - 2018
- [j11]Stefano Beretta, Murray Patterson
HapCHAT: adaptive haplotype assembly for efficiently leveraging high coverage in long reads. BMC Bioinform. 19(1): 252:1-252:19 (2018) - [c11]Simone Ciccolella, Mauricio Soto Gomez, Murray Patterson, Gianluca Della Vedova
gpps: an ILP-based approach for inferring cancer progression with mutation losses from single cell data. ICCABS 2018: 1 - 2017
- [c10]Gianluca Della Vedova
Character-Based Phylogeny Construction and Its Application to Tumor Evolution. CiE 2017: 3-13 - 2016
- [j10]Andrea Bracciali, Marco Aldinucci
PWHATSHAP: efficient haplotyping for future generation sequencing. BMC Bioinform. 17(S-11): 342 (2016) - 2015
- [j9]Murray Patterson
WhatsHap: Weighted Haplotype Assembly for Future-Generation Sequencing Reads. J. Comput. Biol. 22(6): 498-509 (2015) - 2014
- [c9]Marco Aldinucci
High-Performance Haplotype Assembly. CIBB 2014: 245-258 - [c8]Murray Patterson, Tobias Marschall, Nadia Pisanti, Leo van Iersel, Leen Stougie, Gunnar W. Klau
WhatsHap: Haplotype Assembly for Future-Generation Sequencing Reads. RECOMB 2014: 237-249 - 2013
- [j8]Mohammed El-Kebir
Mapping proteins in the presence of paralogs using units of coevolution. BMC Bioinform. 14(S-15): S18 (2013) - [j7]Murray Patterson, Gergely J. Szöllosi
Lateral gene transfer, rearrangement, reconciliation. BMC Bioinform. 14(S-15): S4 (2013) - [c7]Cédric Chauve, Murray Patterson, Ashok Rajaraman:
Hypergraph Covering Problems Motivated by Genome Assembly Questions. IWOCA 2013: 428-432 - [i3]Ján Manuch, Murray Patterson, Roland Wittler, Cédric Chauve, Eric Tannier:
Linearization of ancestral multichromosomal genomes. CTW 2013: 169-173 - [i2]Cédric Chauve, Murray Patterson, Ashok Rajaraman:
Hypergraph covering problems motivated by genome assembly questions. CoRR abs/1306.4353 (2013) - 2012
- [j6]Ján Manuch, Murray Patterson
Linearization of ancestral multichromosomal genomes. BMC Bioinform. 13(S-19): S11 (2012) - [j5]Ján Manuch, Murray Patterson, Cédric Chauve:
Hardness results on the gapped consecutive-ones property problem. Discret. Appl. Math. 160(18): 2760-2768 (2012) - 2011
- [j4]Roland Wittler, Ján Manuch, Murray Patterson, Jens Stoye
Consistency of Sequence-Based Gene Clusters. J. Comput. Biol. 18(9): 1023-1039 (2011) - [j3]Ján Manuch, Murray Patterson:
The Complexity of the Gapped Consecutive-Ones Property Problem for Matrices of Bounded Maximum Degree. J. Comput. Biol. 18(9): 1243-1253 (2011) - [c6]Cédric Chauve, Ján Manuch, Murray Patterson, Roland Wittler:
Tractability Results for the Consecutive-Ones Property with Multiplicity. CPM 2011: 90-103 - [c5]Ján Manuch, Murray Patterson, Arvind Gupta:
Towards a Characterisation of the Generalised Cladistic Character Compatibility Problem for Non-branching Character Trees. ISBRA 2011: 440-451 - 2010
- [c4]Ján Manuch, Murray Patterson, Sheung-Hung Poon
Complexity of Finding Non-Planar Rectilinear Drawings of Graphs. GD 2010: 305-316 - [c3]Ján Manuch, Murray Patterson:
The Complexity of the Gapped Consecutive-Ones Property Problem for Matrices of Bounded Maximum Degree. RECOMB-CG 2010: 278-289
2000 – 2009
- 2009
- [j2]Cédric Chauve, Ján Manuch, Murray Patterson:
On the Gapped Consecutive-Ones Property. Electron. Notes Discret. Math. 34: 121-125 (2009) - [c2]Ján Manuch, Murray Patterson, Arvind Gupta:
On the Generalised Character Compatibility Problem for Non-branching Character Trees. COCOON 2009: 268-276 - [i1]Cédric Chauve, Ján Manuch, Murray Patterson:
Hardness Results for the Gapped Consecutive-Ones Property. CoRR abs/0912.0309 (2009) - 2007
- [c1]Murray Patterson, Yongmei Liu, Eugenia Ternovska, Arvind Gupta:
Grounding for Model Expansion in k-Guarded Formulas with Inductive Definitions. IJCAI 2007: 161-166 - 2003
- [j1]Thomas Bruckner
High-Resolution Modeling of Energy-Services Supply Systems Using deeco: Overview and Application to Policy Development. Ann. Oper. Res. 121(1-4): 151-180 (2003)
Coauthor Index
[j31] [j30] [j28] [j27] [c42] [c41] [c40] [i31] [i30] [i29] [i28] [i27] [i26] [j25] [j24] [j23] [j22] [j21] [c39] [c38] [c37] [c36] [c35] [c34] [c33] [c32] [c31] [c30] [c29] [c28] [c27] [c26] [c24] [c23] [i25] [i24] [i23] [i22] [i21] [i20] [i19] [i18] [i17] [i16] [j19] [c22] [c21] [c20] [c19] [i15] [i14] [i13] [i12] [i11] [j18] [j15] [c18] [c17] [c16] [i10] [i9] [i8] [i7] [i6] [i5]
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