RAMAMOHANARAO KOTAGIRI

Structure-function coupling in the human connectome: A machine learning approach

Publisher: Elsevier BV

Date: 02-2021

DOI: 10.1016/J.NEUROIMAGE.2020.117609

A novel haplotype-based eQTL approach identifies genetic associations not detected through conventional SNP-based methods

Publisher: Cold Spring Harbor Laboratory

Date: 24-07-2020

DOI: 10.1101/2020.07.23.206391

Abstract: The high accuracy of current haplotype phasing tools has enabled the interrogation of haplotype (or phase) information more widely in genetic investigations. Including such information in eQTL analysis complements SNP-based approaches as it has the potential to detect associations that may otherwise be missed. We have developed a haplotype-based eQTL approach called eQTLHap to investigate associations between gene expression and haplotype blocks. Using simulations, we demonstrate that eQTLHap significantly outperforms typical SNP-based eQTL methods when the causal genetic architecture involves multiple SNPs. We show that phasing errors slightly impact the sensitivity of the proposed method ( 4%). Finally, the application of eQTLHap to real GEUVADIS and GTEx datasets finds 22 associations that replicated in larger studies or other tissues and could not be detected using a single-SNP approach. iadbkh/eQTLHap .

Dynamic Straggler Mitigation for Large-Scale Spatial Simulations

Publisher: Association for Computing Machinery (ACM)

Date: 12-04-2023

DOI: 10.1145/3578933

Abstract: Spatial simulations have been widely used to study real-world environments, such as transportation systems. Applications like prediction and analysis of transportation require the simulation to handle millions of objects while running faster than real time. Running such large-scale simulation requires high computational power, which can be provided through parallel distributed computing. Implementations of parallel distributed spatial simulations usually follow a bulk synchronous parallel (BSP) model to ensure the correctness of simulation. The processing in BSP is ided into iterations of computation and communication, running on multiple workers, followed by a global barrier synchronisation to ensure that all communications are concluded. Unfortunately, the BSP model is plagued by the straggler problem, where a delay in any worker slows down the entire simulation. Stragglers may occur for many reasons, including imbalanced workload distribution or communication and synchronisation delays. The straggler problem can become more severe with increasing parallelism and continuous change of workload distribution among workers. This article proposes methods to dynamically mitigate stragglers and tackle communication delays. The proposed strategies can rebalance the workload distribution during simulation. These methods employ the spatial properties of the simulated environments to combine a flexible synchronisation model with decentralised dynamic load balancing and on-demand resource allocation. All proposed methods are implemented and evaluated using a microscopic traffic simulator as an ex le of large-scale spatial simulations. We run traffic simulations for Melbourne, Beijing and New York with different straggler scenarios. Our methods significantly improve simulation performance compared to advanced methods such as global dynamic load balancing.

Strategic Decisions: Survey, Taxonomy, and Future Directions from Artificial Intelligence Perspective

Publisher: Association for Computing Machinery (ACM)

Date: 02-03-2023

DOI: 10.1145/3571807

Abstract: Strategic Decision-Making is always challenging because it is inherently uncertain, ambiguous, risky, and complex. By contrast to tactical and operational decisions, strategic decisions are decisive, pivotal, and often irreversible, which may result in long-term and significant consequences. A strategic decision-making process usually involves many aspects of inquiry, including sensory perception, deliberative thinking, inquiry-based analysis, meta-learning, and constant interaction with the external world. Many unknowns, unpredictabilities, and environmental constraints will shape every aspect of a strategic decision. Traditionally, this task often relies on intuition, reflective thinking, visionary insights, approximate estimates, and practical wisdom. With recent advances in artificial intelligence/machine learning (AI/ML) technologies, we can leverage AI/ML to support strategic decision-making. However, there is still a substantial gap from an AI perspective due to inadequate models, despite the tremendous progress made. We argue that creating a comprehensive taxonomy of decision frames as a representation space is essential for AI because it could offer surprising insights beyond anyone's imaginary boundary today. Strategic decision-making is the art of possibility. This study develops a systematic taxonomy of decision-making frames that consists of six bases, 18 categorical, and 54 elementary frames. We formulate the model using the inquiry method based on Bloom's taxonomy approach. We aim to lay out the computational foundation that is possible to capture a comprehensive landscape view of a strategic problem. Compared with many traditional models, this novel taxonomy covers irrational, non-rational and rational frames capable of dealing with certainty, uncertainty, complexity, ambiguity, chaos, and ignorance.

Evaluation of consensus strategies for haplotype phasing

Publisher: Cold Spring Harbor Laboratory

Date: 14-07-2020

DOI: 10.1101/2020.07.13.175786

Abstract: Haplotype phasing is a critical step for many genetic applications but incorrect estimates of phase can negatively impact downstream analyses. One proposed strategy to improve phasing accuracy is to combine multiple independent phasing estimates to overcome the limitations of any in idual estimate. As such a strategy is yet to be thoroughly explored, this study provides a comprehensive evaluation of consensus strategies for haplotype phasing, exploring their performance, along with their constituent tools, across a range of real and simulated datasets with different data characteristics and on the downstream task of genotype imputation. Based on the outputs of existing phasing tools, we explore two different strategies to construct haplotype consensus estimators: voting across outputs from multiple phasing tools and multiple outputs of a single non-deterministic tool. We find the consensus approach from multiple tools reduces switch error by an average of 10% compared to any constituent tool when applied to European populations and has the highest accuracy regardless of population ethnicity, s le size, SNP-density or SNP frequency. Furthermore, a consensus provides a small improvement indirectly the downstream task of genotype imputation regardless of which genotype imputation tools were used. Our results provide guidance on how to produce the most accurate phasing estimates and the tradeoffs that a consensus approach may have. Our implementation of consensus haplotype phasing, consHap, is available freely at iadbkh/consHap .

Performance anomaly detection using isolation-trees in heterogeneous workloads of web applications in computing clouds

Publisher: Wiley

Date: 16-04-2019

DOI: 10.1002/CPE.5306

Linkage Projects - Grant ID: LP100100854

Start Date: 2010

End Date: 09-2011

Amount: $212,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP110102621

Start Date: 2011

End Date: 12-2015

Amount: $697,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP130103705

Start Date: 2013

End Date: 12-2017

Amount: $452,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP0344695

Start Date: 01-2003

End Date: 12-2009

Amount: $351,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP140101969

Start Date: 07-2014

End Date: 04-2018

Amount: $660,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP0880250

Start Date: 2008

End Date: 12-2012

Amount: $234,000.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP130101038

Start Date: 01-2014

End Date: 07-2018

Amount: $280,000.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP0990393

Start Date: 07-2010

End Date: 06-2014

Amount: $380,000.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP0669435

Start Date: 12-2006

End Date: 12-2011

Amount: $295,000.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP0668092

Start Date: 09-2006

End Date: 06-2010

Amount: $174,000.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP140100816

Start Date: 03-2015

End Date: 12-2019

Amount: $300,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP0880215

Start Date: 2008

End Date: 12-2011

Amount: $590,000.00

Funder: Australian Research Council

Linkage Infrastructure, Equipment And Facilities - Grant ID: LE0346856

Start Date: 12-2003

End Date: 12-2004

Amount: $950,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP0209969

Start Date: 2002

End Date: 12-2006

Amount: $370,808.00

Funder: Australian Research Council

Linkage Infrastructure, Equipment And Facilities - Grant ID: LE0883073

Start Date: 2008

End Date: 12-2008

Amount: $200,000.00

Funder: Australian Research Council

Special Research Initiatives - Grant ID: SR0354488

Start Date: 01-2004

End Date: 12-2003

Amount: $10,000.00

Funder: Australian Research Council

Linkage Infrastructure, Equipment And Facilities - Grant ID: LE0561231

Start Date: 2005

End Date: 12-2007

Amount: $671,715.00

Funder: Australian Research Council

Special Research Initiatives - Grant ID: SR0564829

Start Date: 12-2005

End Date: 12-2006

Amount: $80,000.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP0667490

Start Date: 2006

End Date: 12-2010

Amount: $650,000.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP120200130

Start Date: 11-2012

End Date: 12-2016

Amount: $330,000.00

Funder: Australian Research Council

Research Networks - Grant ID: RN0459557

Start Date: 12-2004

End Date: 12-2010

Amount: $1,950,000.00

Funder: Australian Research Council

Special Research Initiatives - Grant ID: SR0354781

Start Date: 01-2004

End Date: 12-2003

Amount: $40,000.00

Funder: Australian Research Council

Linkage Infrastructure, Equipment And Facilities - Grant ID: LE0989384

Start Date: 10-2009

End Date: 12-2011

Amount: $233,000.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP0990369

Start Date: 12-2009

End Date: 12-2015

Amount: $400,000.00

Funder: Australian Research Council

Special Research Initiatives - Grant ID: SR0354767

Start Date: 01-2004

End Date: 12-2003

Amount: $20,000.00

Funder: Australian Research Council

Research Networks - Grant ID: RN0459979

Start Date: 12-2004

End Date: 12-2010

Amount: $2,250,000.00

Funder: Australian Research Council

ARC Centres Of Excellence - Grant ID: CE0348177

Start Date: 2003

End Date: 12-2007

Amount: $5,625,165.00

Funder: Australian Research Council

Research Networks - Grant ID: RN0459895

Start Date: 09-2004

End Date: 12-2011

Amount: $1,600,000.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP0348988

Start Date: 03-2004

End Date: 12-2012

Amount: $93,300.00

Funder: Australian Research Council