A K M Azad - Academia.edu (original) (raw)

Papers by A K M Azad

Research paper thumbnail of Integrating Heterogeneous Datasets for Cancer Module Identification

Methods in Molecular Biology, 2016

The availability of multiple heterogeneous high-throughput datasets provides an enabling resource... more The availability of multiple heterogeneous high-throughput datasets provides an enabling resource for cancer systems biology. Types of data include: Gene expression (GE), copy number aberration (CNA), miRNA expression, methylation, and protein-protein Interactions (PPI). One important problem that can potentially be solved using such data is to determine which of the possible pair-wise interactions among genes contributes to a range of cancer-related events, from tumorigenesis to metastasis. It has been shown by various studies that applying integrated knowledge from multi-omics datasets elucidates such complex phenomena with higher statistical significance than using a single type of dataset individually. However, computational methods for processing multiple data types simultaneously are needed. This chapter reviews some of the computational methods that use integrated approaches to find cancer-related modules.

Research paper thumbnail of Prediction of Drug-Resistive Cross-talks among Signaling Pathways in Breast Cancer by Bayesian Statistical Modeling

Research paper thumbnail of Internet-based laboratory experiments as a part of an engineering technology program

Research paper thumbnail of Single cell data explosion: Deep learning to the rescue

arXiv: Other Quantitative Biology, 2019

The plethora of single-cell multi-omics data is getting treatment with deep learning, a revolutio... more The plethora of single-cell multi-omics data is getting treatment with deep learning, a revolutionary method in artificial intelligence, which has been increasingly expanding its reign over the bioscience frontiers.

Research paper thumbnail of Collocated and non-collocated feedback control of flexible manipulator systems

This paper presents an investigation into the development of a closed-loop control strategy for f... more This paper presents an investigation into the development of a closed-loop control strategy for flexible manipulator systems. A constrained planar single-link flexible manipulator system is considered. A state-space model of the system is developed by linearizing the dynamic equations of motion of the system. A finite difference simulation environment characterizing the behaviour of the manipulator with hub inertia and payload mass is utilized as a test and verification platform. A control strategy incorporating both collocated (hub angle and hub velocity) and non-collocated (end-point acceleration) feedback is proposed and implemented within the single-link flexible manipulator environment. Finally, simulation results, verifying the performance of the developed control strategy, are presented and discussed.

Research paper thumbnail of Linear time algorithms for floor-planning and routing problems

Research paper thumbnail of Effects of Bacille Calmette Guerin (BCG) vaccination during COVID-19 infection

Computers in Biology and Medicine, 2021

Research paper thumbnail of Machine Learning Approaches to Identify Patient Comorbidities and Symptoms That Increased Risk of Mortality in COVID-19

Diagnostics, 2021

Providing appropriate care for people suffering from COVID-19, the disease caused by the pandemic... more Providing appropriate care for people suffering from COVID-19, the disease caused by the pandemic SARS-CoV-2 virus, is a significant global challenge. Many individuals who become infected may have pre-existing conditions that may interact with COVID-19 to increase symptom severity and mortality risk. COVID-19 patient comorbidities are likely to be informative regarding the individual risk of severe illness and mortality. Determining the degree to which comorbidities are associated with severe symptoms and mortality would thus greatly assist in COVID-19 care planning and provision. To assess this we performed a meta-analysis of published global literature, and machine learning predictive analysis using an aggregated COVID-19 global dataset. Our meta-analysis suggested that chronic obstructive pulmonary disease (COPD), cerebrovascular disease (CEVD), cardiovascular disease (CVD), type 2 diabetes, malignancy, and hypertension as most significantly associated with COVID-19 severity in t...

Research paper thumbnail of Machine Learning Approach to Predicting COVID-19 Disease Severity Based on Clinical Blood Test Data: Statistical Analysis and Model Development

JMIR Medical Informatics, 2021

Background Accurate prediction of the disease severity of patients with COVID-19 would greatly im... more Background Accurate prediction of the disease severity of patients with COVID-19 would greatly improve care delivery and resource allocation and thereby reduce mortality risks, especially in less developed countries. Many patient-related factors, such as pre-existing comorbidities, affect disease severity and can be used to aid this prediction. Objective Because rapid automated profiling of peripheral blood samples is widely available, we aimed to investigate how data from the peripheral blood of patients with COVID-19 can be used to predict clinical outcomes. Methods We investigated clinical data sets of patients with COVID-19 with known outcomes by combining statistical comparison and correlation methods with machine learning algorithms; the latter included decision tree, random forest, variants of gradient boosting machine, support vector machine, k-nearest neighbor, and deep learning methods. Results Our work revealed that several clinical parameters that are measurable in blood...

Research paper thumbnail of Discovering novel cancer bio-markers in acquired lapatinib resistance using Bayesian methods

Briefings in Bioinformatics, 2021

Signalling transduction pathways (STPs) are commonly hijacked by many cancers for their growth an... more Signalling transduction pathways (STPs) are commonly hijacked by many cancers for their growth and malignancy, but demystifying their underlying mechanisms is difficult. Here, we developed methodologies with a fully Bayesian approach in discovering novel driver bio-markers in aberrant STPs given high-throughput gene expression (GE) data. This project, namely ‘PathTurbEr’ (Pathway Perturbation Driver) uses the GE dataset derived from the lapatinib (an EGFR/HER dual inhibitor) sensitive and resistant samples from breast cancer cell lines (SKBR3). Differential expression analysis revealed 512 differentially expressed genes (DEGs) and their pathway enrichment revealed 13 highly perturbed singalling pathways in lapatinib resistance, including PI3K-AKT, Chemokine, Hippo and TGF-$\beta $ singalling pathways. Next, the aberration in TGF-$\beta $ STP was modelled as a causal Bayesian network (BN) using three MCMC sampling methods, i.e. Neighbourhood sampler (NS) and Hit-and-Run (HAR) sampler...

Research paper thumbnail of XTalkiiS: a tool for finding data-driven cross-talks between intra-/inter-species pathways

Cell-cell communication via pathway cross-talks within a single species have been studied in sili... more Cell-cell communication via pathway cross-talks within a single species have been studied in silico recently to decipher various disease phenotype. However, computational prediction of pathway cross-talks among multiple species in a data-driven manner is yet to be explored. In this article, I present XTalkiiS (Cross-talks between inter-/intra species pathways), a tool to automatically predict pathway cross-talks from data-driven models of pathway network, both within the same organism (intra-species) and between two organisms (inter-species). XTalkiiS starts with retrieving and listing up-to-date pathway information in all the species available in KEGG database using RESTful APIs (exploiting KEGG web services) and an in-house built web crawler. I hypothesize that data-driven network models can be built by simultaneously quantifying co-expression of pathway components (i.e. genes/proteins) in matched samples in multiple organisms. Next, XTalkiiS loads a data-driven pathway network an...

Research paper thumbnail of The neighborhood MCMC sampler for learning Bayesian networks

SPIE Proceedings, 2016

Getting stuck in local maxima is a problem that arises while learning Bayesian networks (BNs) str... more Getting stuck in local maxima is a problem that arises while learning Bayesian networks (BNs) structures. In this paper, we studied a recently proposed Markov chain Monte Carlo (MCMC) sampler, called the Neighbourhood sampler (NS), and examined how efficiently it can sample BNs when local maxima are present. We assume that a posterior distribution f(N,E|D) has been defined, where D represents data relevant to the inference, N and E are the sets of nodes and directed edges, respectively. We illustrate the new approach by sampling from such a distribution, and inferring BNs. The simulations conducted in this paper show that the new learning approach substantially avoids getting stuck in local modes of the distribution, and achieves a more rapid rate of convergence, compared to other common algorithms e.g. the MCMC Metropolis-Hastings sampler.

Research paper thumbnail of Effect of Penalty Function Parameter in Objective Function of System Identification

International Journal of Automotive and Mechanical Engineering

Research paper thumbnail of Tape Transport Mechanism Control For a Magnetic Tape Recorder/Player Used for High-Speed Data Transfer

IETE Journal of Education, 1988

In this paper an attempt is made to develop an efficient tape transport mechanism required for hi... more In this paper an attempt is made to develop an efficient tape transport mechanism required for high-speed data transfer. The mechanism is controlled by a microcomputer, which is a party in the data transfer operation. A commercially available transport system has been adopted for this purpose, with simple modification. During data search operation the tape speed is faster than the speed in normal Read/Write operation. At the same time, the Read/Write head position is adjusted in accordance with the tape speed. The system includes facilities for detecting the beginning and the end of the tape, and switching off the amplifier for read and write operation. Taking into account the delay for signal transfer and the time for motor torque, a tape motion velocity profile has been generated. This is used to obtain start, stop and interblock gap length.

Research paper thumbnail of Wireless Sensor Network Protocols Applicable to RFID System

Systems for Ubiquitous Tagging

Research paper thumbnail of Orientation independent compact chipless RFID tag

2012 IEEE International Conference on RFID-Technologies and Applications (RFID-TA), 2012

A design concept of a compact printable orientation independent chipless radio frequency identifi... more A design concept of a compact printable orientation independent chipless radio frequency identification tag is presented with near-field and far-field reading techniques. The tag consists of a circular patch loaded with multiple slot ring resonators and it has the advantage to be read from any orientation with the reader due to its symmetric structure. This tag does not have a ground plane and has higher data density and lower cost compared to the most other existing printable chipless tags. Proximity and slot reading techniques are described in details for different applications. This single-sided, compact and orientation independent tag has a great potential to be used in millions both for identification and authentication.

Research paper thumbnail of Aperture coupled UWB microstrip patch antenna array for mm-Wave chipless RFID tag reader

2012 IEEE International Conference on RFID-Technologies and Applications (RFID-TA), 2012

A 4×4 aperture coupled UWB microstrip patch antenna array for mm-wave chipless RFID tag reader is... more A 4×4 aperture coupled UWB microstrip patch antenna array for mm-wave chipless RFID tag reader is presented. The antenna is operating over the 21-27 GHz frequency band with 20 dBi gain. A systematic approach has been followed to design the antenna array, where, firstly a single antenna element is optimized, then a feed network using multistage power divider is designed and finally, the 4×4 antenna array is developed and optimized. Simulation and measurement results of the antenna are described in details with gain, radiation pattern and impedance behavior. Finally, the assembled antenna is used for measuring mm-wave chipless tag to validate its functional accuracy. The antenna can be used in commercial mm-wave chipless RFID tag reader due to its lower profile, cost and higher gain.

Research paper thumbnail of Query Processing over Distributed Heterogeneous Sensor Networks in Future Internet: Scalable Architecture and Challenges

2010 Second International Conference on Advances in Future Internet, 2010

Research paper thumbnail of Distribution of energy usage in wireless sensor networks for polygonal coverage

2007 10th International Conference on Computer and Information Technology, 2007

ABSTRACT The increasing concentration of data traffic on sensors towards the base station in mult... more ABSTRACT The increasing concentration of data traffic on sensors towards the base station in multi hop and escalating rate of energy drainage due to higher transmission distance at sensors towards the boundary in single hop communication cause an extremely non-uniform energy usage. This non-uniformity in energy usage makes some nodes to die early reducing the networkpsilas lifetime drastically leaving considerable amount of residual energy in others. In this work, we extend the single hop, multi hop and their hybrid transmission policies proposed in V. Mhatre and C. Rosenberg (2004) for polygonal network area instead of circular area. Through mathematical analysis we present the distribution of energy usage among sensors in different rings for polygonal network. Our work presents a more generalized theoretical analysis of network lifetime from transmission policy perspective considering practical deployment scenario. Numerical results show that multi hop transmission performs better than single hop transmission for path loss factor four and hybrid transmission policy performs better in terms of network lifetime and energy distribution than single hop and multi hop transmissions irrespective of network size.

Research paper thumbnail of Energy Efficient and Hop Constraint Intra-Cluster Transmission for Heterogeneous Sensor Networks

2008 IEEE Wireless Communications and Networking Conference, 2008

Research paper thumbnail of Integrating Heterogeneous Datasets for Cancer Module Identification

Methods in Molecular Biology, 2016

The availability of multiple heterogeneous high-throughput datasets provides an enabling resource... more The availability of multiple heterogeneous high-throughput datasets provides an enabling resource for cancer systems biology. Types of data include: Gene expression (GE), copy number aberration (CNA), miRNA expression, methylation, and protein-protein Interactions (PPI). One important problem that can potentially be solved using such data is to determine which of the possible pair-wise interactions among genes contributes to a range of cancer-related events, from tumorigenesis to metastasis. It has been shown by various studies that applying integrated knowledge from multi-omics datasets elucidates such complex phenomena with higher statistical significance than using a single type of dataset individually. However, computational methods for processing multiple data types simultaneously are needed. This chapter reviews some of the computational methods that use integrated approaches to find cancer-related modules.

Research paper thumbnail of Prediction of Drug-Resistive Cross-talks among Signaling Pathways in Breast Cancer by Bayesian Statistical Modeling

Research paper thumbnail of Internet-based laboratory experiments as a part of an engineering technology program

Research paper thumbnail of Single cell data explosion: Deep learning to the rescue

arXiv: Other Quantitative Biology, 2019

The plethora of single-cell multi-omics data is getting treatment with deep learning, a revolutio... more The plethora of single-cell multi-omics data is getting treatment with deep learning, a revolutionary method in artificial intelligence, which has been increasingly expanding its reign over the bioscience frontiers.

Research paper thumbnail of Collocated and non-collocated feedback control of flexible manipulator systems

This paper presents an investigation into the development of a closed-loop control strategy for f... more This paper presents an investigation into the development of a closed-loop control strategy for flexible manipulator systems. A constrained planar single-link flexible manipulator system is considered. A state-space model of the system is developed by linearizing the dynamic equations of motion of the system. A finite difference simulation environment characterizing the behaviour of the manipulator with hub inertia and payload mass is utilized as a test and verification platform. A control strategy incorporating both collocated (hub angle and hub velocity) and non-collocated (end-point acceleration) feedback is proposed and implemented within the single-link flexible manipulator environment. Finally, simulation results, verifying the performance of the developed control strategy, are presented and discussed.

Research paper thumbnail of Linear time algorithms for floor-planning and routing problems

Research paper thumbnail of Effects of Bacille Calmette Guerin (BCG) vaccination during COVID-19 infection

Computers in Biology and Medicine, 2021

Research paper thumbnail of Machine Learning Approaches to Identify Patient Comorbidities and Symptoms That Increased Risk of Mortality in COVID-19

Diagnostics, 2021

Providing appropriate care for people suffering from COVID-19, the disease caused by the pandemic... more Providing appropriate care for people suffering from COVID-19, the disease caused by the pandemic SARS-CoV-2 virus, is a significant global challenge. Many individuals who become infected may have pre-existing conditions that may interact with COVID-19 to increase symptom severity and mortality risk. COVID-19 patient comorbidities are likely to be informative regarding the individual risk of severe illness and mortality. Determining the degree to which comorbidities are associated with severe symptoms and mortality would thus greatly assist in COVID-19 care planning and provision. To assess this we performed a meta-analysis of published global literature, and machine learning predictive analysis using an aggregated COVID-19 global dataset. Our meta-analysis suggested that chronic obstructive pulmonary disease (COPD), cerebrovascular disease (CEVD), cardiovascular disease (CVD), type 2 diabetes, malignancy, and hypertension as most significantly associated with COVID-19 severity in t...

Research paper thumbnail of Machine Learning Approach to Predicting COVID-19 Disease Severity Based on Clinical Blood Test Data: Statistical Analysis and Model Development

JMIR Medical Informatics, 2021

Background Accurate prediction of the disease severity of patients with COVID-19 would greatly im... more Background Accurate prediction of the disease severity of patients with COVID-19 would greatly improve care delivery and resource allocation and thereby reduce mortality risks, especially in less developed countries. Many patient-related factors, such as pre-existing comorbidities, affect disease severity and can be used to aid this prediction. Objective Because rapid automated profiling of peripheral blood samples is widely available, we aimed to investigate how data from the peripheral blood of patients with COVID-19 can be used to predict clinical outcomes. Methods We investigated clinical data sets of patients with COVID-19 with known outcomes by combining statistical comparison and correlation methods with machine learning algorithms; the latter included decision tree, random forest, variants of gradient boosting machine, support vector machine, k-nearest neighbor, and deep learning methods. Results Our work revealed that several clinical parameters that are measurable in blood...

Research paper thumbnail of Discovering novel cancer bio-markers in acquired lapatinib resistance using Bayesian methods

Briefings in Bioinformatics, 2021

Signalling transduction pathways (STPs) are commonly hijacked by many cancers for their growth an... more Signalling transduction pathways (STPs) are commonly hijacked by many cancers for their growth and malignancy, but demystifying their underlying mechanisms is difficult. Here, we developed methodologies with a fully Bayesian approach in discovering novel driver bio-markers in aberrant STPs given high-throughput gene expression (GE) data. This project, namely ‘PathTurbEr’ (Pathway Perturbation Driver) uses the GE dataset derived from the lapatinib (an EGFR/HER dual inhibitor) sensitive and resistant samples from breast cancer cell lines (SKBR3). Differential expression analysis revealed 512 differentially expressed genes (DEGs) and their pathway enrichment revealed 13 highly perturbed singalling pathways in lapatinib resistance, including PI3K-AKT, Chemokine, Hippo and TGF-$\beta $ singalling pathways. Next, the aberration in TGF-$\beta $ STP was modelled as a causal Bayesian network (BN) using three MCMC sampling methods, i.e. Neighbourhood sampler (NS) and Hit-and-Run (HAR) sampler...

Research paper thumbnail of XTalkiiS: a tool for finding data-driven cross-talks between intra-/inter-species pathways

Cell-cell communication via pathway cross-talks within a single species have been studied in sili... more Cell-cell communication via pathway cross-talks within a single species have been studied in silico recently to decipher various disease phenotype. However, computational prediction of pathway cross-talks among multiple species in a data-driven manner is yet to be explored. In this article, I present XTalkiiS (Cross-talks between inter-/intra species pathways), a tool to automatically predict pathway cross-talks from data-driven models of pathway network, both within the same organism (intra-species) and between two organisms (inter-species). XTalkiiS starts with retrieving and listing up-to-date pathway information in all the species available in KEGG database using RESTful APIs (exploiting KEGG web services) and an in-house built web crawler. I hypothesize that data-driven network models can be built by simultaneously quantifying co-expression of pathway components (i.e. genes/proteins) in matched samples in multiple organisms. Next, XTalkiiS loads a data-driven pathway network an...

Research paper thumbnail of The neighborhood MCMC sampler for learning Bayesian networks

SPIE Proceedings, 2016

Getting stuck in local maxima is a problem that arises while learning Bayesian networks (BNs) str... more Getting stuck in local maxima is a problem that arises while learning Bayesian networks (BNs) structures. In this paper, we studied a recently proposed Markov chain Monte Carlo (MCMC) sampler, called the Neighbourhood sampler (NS), and examined how efficiently it can sample BNs when local maxima are present. We assume that a posterior distribution f(N,E|D) has been defined, where D represents data relevant to the inference, N and E are the sets of nodes and directed edges, respectively. We illustrate the new approach by sampling from such a distribution, and inferring BNs. The simulations conducted in this paper show that the new learning approach substantially avoids getting stuck in local modes of the distribution, and achieves a more rapid rate of convergence, compared to other common algorithms e.g. the MCMC Metropolis-Hastings sampler.

Research paper thumbnail of Effect of Penalty Function Parameter in Objective Function of System Identification

International Journal of Automotive and Mechanical Engineering

Research paper thumbnail of Tape Transport Mechanism Control For a Magnetic Tape Recorder/Player Used for High-Speed Data Transfer

IETE Journal of Education, 1988

In this paper an attempt is made to develop an efficient tape transport mechanism required for hi... more In this paper an attempt is made to develop an efficient tape transport mechanism required for high-speed data transfer. The mechanism is controlled by a microcomputer, which is a party in the data transfer operation. A commercially available transport system has been adopted for this purpose, with simple modification. During data search operation the tape speed is faster than the speed in normal Read/Write operation. At the same time, the Read/Write head position is adjusted in accordance with the tape speed. The system includes facilities for detecting the beginning and the end of the tape, and switching off the amplifier for read and write operation. Taking into account the delay for signal transfer and the time for motor torque, a tape motion velocity profile has been generated. This is used to obtain start, stop and interblock gap length.

Research paper thumbnail of Wireless Sensor Network Protocols Applicable to RFID System

Systems for Ubiquitous Tagging

Research paper thumbnail of Orientation independent compact chipless RFID tag

2012 IEEE International Conference on RFID-Technologies and Applications (RFID-TA), 2012

A design concept of a compact printable orientation independent chipless radio frequency identifi... more A design concept of a compact printable orientation independent chipless radio frequency identification tag is presented with near-field and far-field reading techniques. The tag consists of a circular patch loaded with multiple slot ring resonators and it has the advantage to be read from any orientation with the reader due to its symmetric structure. This tag does not have a ground plane and has higher data density and lower cost compared to the most other existing printable chipless tags. Proximity and slot reading techniques are described in details for different applications. This single-sided, compact and orientation independent tag has a great potential to be used in millions both for identification and authentication.

Research paper thumbnail of Aperture coupled UWB microstrip patch antenna array for mm-Wave chipless RFID tag reader

2012 IEEE International Conference on RFID-Technologies and Applications (RFID-TA), 2012

A 4×4 aperture coupled UWB microstrip patch antenna array for mm-wave chipless RFID tag reader is... more A 4×4 aperture coupled UWB microstrip patch antenna array for mm-wave chipless RFID tag reader is presented. The antenna is operating over the 21-27 GHz frequency band with 20 dBi gain. A systematic approach has been followed to design the antenna array, where, firstly a single antenna element is optimized, then a feed network using multistage power divider is designed and finally, the 4×4 antenna array is developed and optimized. Simulation and measurement results of the antenna are described in details with gain, radiation pattern and impedance behavior. Finally, the assembled antenna is used for measuring mm-wave chipless tag to validate its functional accuracy. The antenna can be used in commercial mm-wave chipless RFID tag reader due to its lower profile, cost and higher gain.

Research paper thumbnail of Query Processing over Distributed Heterogeneous Sensor Networks in Future Internet: Scalable Architecture and Challenges

2010 Second International Conference on Advances in Future Internet, 2010

Research paper thumbnail of Distribution of energy usage in wireless sensor networks for polygonal coverage

2007 10th International Conference on Computer and Information Technology, 2007

ABSTRACT The increasing concentration of data traffic on sensors towards the base station in mult... more ABSTRACT The increasing concentration of data traffic on sensors towards the base station in multi hop and escalating rate of energy drainage due to higher transmission distance at sensors towards the boundary in single hop communication cause an extremely non-uniform energy usage. This non-uniformity in energy usage makes some nodes to die early reducing the networkpsilas lifetime drastically leaving considerable amount of residual energy in others. In this work, we extend the single hop, multi hop and their hybrid transmission policies proposed in V. Mhatre and C. Rosenberg (2004) for polygonal network area instead of circular area. Through mathematical analysis we present the distribution of energy usage among sensors in different rings for polygonal network. Our work presents a more generalized theoretical analysis of network lifetime from transmission policy perspective considering practical deployment scenario. Numerical results show that multi hop transmission performs better than single hop transmission for path loss factor four and hybrid transmission policy performs better in terms of network lifetime and energy distribution than single hop and multi hop transmissions irrespective of network size.

Research paper thumbnail of Energy Efficient and Hop Constraint Intra-Cluster Transmission for Heterogeneous Sensor Networks

2008 IEEE Wireless Communications and Networking Conference, 2008