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Article ; Online: DeepBend: An interpretable model of DNA bendability.

Khan, Samin Rahman / Sakib, Sadman / Rahman, M Sohel / Samee, Md Abul Hassan

iScience

2023 Volume 26, Issue 2, Page(s) 105945

Abstract: The bendability of genomic DNA impacts chromatin packaging and protein-DNA binding. However, we do not have a comprehensive understanding of the motifs influencing DNA bendability. Recent high-throughput technologies such as Loop-Seq offer an opportunity ...

Abstract	The bendability of genomic DNA impacts chromatin packaging and protein-DNA binding. However, we do not have a comprehensive understanding of the motifs influencing DNA bendability. Recent high-throughput technologies such as Loop-Seq offer an opportunity to address this gap but the lack of accurate and interpretable machine learning models still remains. Here we introduce DeepBend, a convolutional neural network model with convolutions designed to directly capture the motifs underlying DNA bendability and their periodic occurrences or relative arrangements that modulate bendability. DeepBend consistently performs on par with alternative models while giving an extra edge through mechanistic interpretations. Besides confirming the known motifs of DNA bendability, DeepBend also revealed several novel motifs and showed how the spatial patterns of motif occurrences influence bendability. DeepBend's genome-wide prediction of bendability further showed how bendability is linked to chromatin conformation and revealed the motifs controlling the bendability of topologically associated domains and their boundaries.
Language	English
Publishing date	2023-01-07
Publishing country	United States
Document type	Journal Article
ISSN	2589-0042
ISSN (online)	2589-0042
DOI	10.1016/j.isci.2023.105945
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Article ; Online: MAMMLE: A Framework for Phylogeny Estimation Based on Multiobjective Application-aware Multiple Sequence Alignment and Maximum Likelihood Ensemble.

Nayeem, Muhammad Ali / Samudro, Naser Anjum / Rahman, M Saifur / Rahman, M Sohel

Journal of computational biology : a journal of computational molecular cell biology

2023 Volume 30, Issue 3, Page(s) 245–249

Abstract: Motivation: ...

Abstract	Motivation:
MeSH term(s)	Phylogeny ; Sequence Alignment ; Algorithms ; Software
Language	English
Publishing date	2023-01-27
Publishing country	United States
Document type	Journal Article
ZDB-ID	2030900-4
ISSN	1557-8666 ; 1066-5277
ISSN (online)	1557-8666
ISSN	1066-5277
DOI	10.1089/cmb.2021.0533
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Article ; Online: 3C-GAN: class-consistent CycleGAN for malaria domain adaptation model.

Rahman, Aimon / Rahman, M Sohel / Mahdy, M R C

Biomedical physics & engineering express

2021 Volume 7, Issue 5

Abstract: Unpaired domain translation models with distribution matching loss such as CycleGAN are now widely being used to shift domain in medical images. However, synthesizing medical images using CycleGAN can lead to misdiagnosis of a medical condition as it ... ...

Abstract	Unpaired domain translation models with distribution matching loss such as CycleGAN are now widely being used to shift domain in medical images. However, synthesizing medical images using CycleGAN can lead to misdiagnosis of a medical condition as it might hallucinate unwanted features, especially if theres a data bias. This can potentially change the original class of the input image, which is a very serious problem. In this paper, we have introduced a modified distribution matching loss for CycleGAN to eliminate feature hallucination on the malaria dataset. In the context of the malaria dataset, unintentional feature hallucination may introduce a facet that resembles a parasite or remove the parasite after the translation. Our proposed approach has enabled us to shift the domain of the malaria dataset without the risk of changing their corresponding class. We have presented experimental evidence that our modified loss significantly reduced feature hallucination by preserving original class labels. The experimental results are better in comparison to the baseline (classic CycleGAN) that targets the translating domain. We believe that our approach will expedite the process of developing unsupervised unpaired GAN that is safe for clinical use.
MeSH term(s)	Hallucinations ; Humans ; Image Processing, Computer-Assisted ; Malaria
Language	English
Publishing date	2021-07-07
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	2057-1976
ISSN (online)	2057-1976
DOI	10.1088/2057-1976/ac0e74
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Article ; Online: A novel customer churn prediction model for the telecommunication industry using data transformation methods and feature selection.

Sana, Joydeb Kumar / Abedin, Mohammad Zoynul / Rahman, M Sohel / Rahman, M Saifur

PloS one

2022 Volume 17, Issue 12, Page(s) e0278095

Abstract: Customer churn is one of the most critical issues faced by the telecommunication industry (TCI). Researchers and analysts leverage customer relationship management (CRM) data through the use of various machine learning models and data transformation ... ...

Abstract	Customer churn is one of the most critical issues faced by the telecommunication industry (TCI). Researchers and analysts leverage customer relationship management (CRM) data through the use of various machine learning models and data transformation methods to identify the customers who are likely to churn. While several studies have been conducted in the customer churn prediction (CCP) context in TCI, a review of performance of the various models stemming from these studies show a clear room for improvement. Therefore, to improve the accuracy of customer churn prediction in the telecommunication industry, we have investigated several machine learning models, as well as, data transformation methods. To optimize the prediction models, feature selection has been performed using univariate technique and the best hyperparameters have been selected using the grid search method. Subsequently, experiments have been conducted on several publicly available TCI datasets to assess the performance of our models in terms of the widely used evaluation metrics, such as AUC, precision, recall, and F-measure. Through a rigorous experimental study, we have demonstrated the benefit of applying data transformation methods as well as feature selection while training an optimized CCP model. Our proposed technique improved the prediction performance by up to 26.2% and 17% in terms of AUC and F-measure, respectively.
MeSH term(s)	Benchmarking ; Computer Systems ; Industry ; Telecommunications
Language	English
Publishing date	2022-12-01
Publishing country	United States
Document type	Journal Article
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0278095
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Article ; Online: Revisiting segmentation of lung tumors from CT images.

Farheen, Farhanaz / Shamil, Md Salman / Ibtehaz, Nabil / Rahman, M Sohel

Computers in biology and medicine

2022 Volume 144, Page(s) 105385

Abstract: Lung cancer is a leading cause of death throughout the world. Because the prompt diagnosis of tumors allows oncologists to discern their nature, type, and mode of treatment, tumor detection and segmentation from CT scan images is a crucial field of study. ...

Abstract	Lung cancer is a leading cause of death throughout the world. Because the prompt diagnosis of tumors allows oncologists to discern their nature, type, and mode of treatment, tumor detection and segmentation from CT scan images is a crucial field of study. This paper investigates lung tumor segmentation via a two-dimensional Discrete Wavelet Transform (DWT) on the LOTUS dataset (31,247 training, and 4458 testing samples) and a Deeply Supervised MultiResUNet model. Coupling the DWT, which is used to achieve a more meticulous textural analysis while integrating information from neighboring CT slices, with the deep supervision of the model architecture results in an improved dice coefficient of 0.8472. A key characteristic of our approach is its avoidance of 3D kernels (despite being used for a 3D segmentation task), thereby making it quite lightweight.
MeSH term(s)	Humans ; Image Processing, Computer-Assisted/methods ; Lung Neoplasms/diagnostic imaging ; Tomography, X-Ray Computed ; Wavelet Analysis
Language	English
Publishing date	2022-03-07
Publishing country	United States
Document type	Journal Article
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2022.105385
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Article ; Online: Align-gram: Rethinking the Skip-gram Model for Protein Sequence Analysis.

Ibtehaz, Nabil / Sourav, S M Shakhawat Hossain / Bayzid, Md Shamsuzzoha / Rahman, M Sohel

The protein journal

2023 Volume 42, Issue 2, Page(s) 135–146

Abstract: The inception of next generations sequencing technologies have exponentially increased the volume of biological sequence data. Protein sequences, being quoted as the 'language of life', has been analyzed for a multitude of applications and inferences. ... ...

Abstract	The inception of next generations sequencing technologies have exponentially increased the volume of biological sequence data. Protein sequences, being quoted as the 'language of life', has been analyzed for a multitude of applications and inferences. Owing to the rapid development of deep learning, in recent years there have been a number of breakthroughs in the domain of Natural Language Processing. Since these methods are capable of performing different tasks when trained with a sufficient amount of data, off-the-shelf models are used to perform various biological applications. In this study, we investigated the applicability of the popular Skip-gram model for protein sequence analysis and made an attempt to incorporate some biological insights into it. We propose a novel k-mer embedding scheme, Align-gram, which is capable of mapping the similar k-mers close to each other in a vector space. Furthermore, we experiment with other sequence-based protein representations and observe that the embeddings derived from Align-gram aids modeling and training deep learning models better. Our experiments with a simple baseline LSTM model and a much complex CNN model of DeepGoPlus shows the potential of Align-gram in performing different types of deep learning applications for protein sequence analysis.
MeSH term(s)	Sequence Analysis, Protein ; Amino Acid Sequence ; Proteins
Chemical Substances	Proteins
Language	English
Publishing date	2023-03-28
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2143071-8
ISSN	1875-8355 ; 1572-3887
ISSN (online)	1875-8355
ISSN	1572-3887
DOI	10.1007/s10930-023-10096-7
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Article ; Online: Succinylated lysine residue prediction revisited.

Ahmed, Shehab Sarar / Rifat, Zaara Tasnim / Rahman, M Saifur / Rahman, M Sohel

Briefings in bioinformatics

2022 Volume 24, Issue 1

Abstract: Lysine succinylation is a kind of post-translational modification (PTM) that plays a crucial role in regulating the cellular processes. Aberrant succinylation may cause inflammation, cancers, metabolism diseases and nervous system diseases. The ... ...

Abstract	Lysine succinylation is a kind of post-translational modification (PTM) that plays a crucial role in regulating the cellular processes. Aberrant succinylation may cause inflammation, cancers, metabolism diseases and nervous system diseases. The experimental methods to detect succinylation sites are time-consuming and costly. This thus calls for computational models with high efficacy, and attention has been given in the literature to develop such models, albeit with only moderate success in the context of different evaluation metrics. One crucial aspect in this context is the biochemical and physicochemical properties of amino acids, which appear to be useful as features for such computational predictors. However, some of the existing computational models did not use the biochemical and physicochemical properties of amino acids. In contrast, some others used them without considering the inter-dependency among the properties. The combinations of biochemical and physicochemical properties derived through our optimization process achieve better results than the results achieved by combining all the properties. We propose three deep learning architectures: CNN+Bi-LSTM (CBL), Bi-LSTM+CNN (BLC) and their combination (CBL_BLC). We find that CBL_BLC outperforms the other two. Ensembling of different models successfully improves the results. Notably, tuning the threshold of the ensemble classifiers further improves the results. Upon comparing our work with other existing works on two datasets, we successfully achieve better sensitivity and specificity by varying the threshold value.
MeSH term(s)	Lysine/metabolism ; Algorithms ; Amino Acids/chemistry ; Sensitivity and Specificity ; Protein Processing, Post-Translational
Chemical Substances	Lysine (K3Z4F929H6) ; Amino Acids
Language	English
Publishing date	2022-11-29
Publishing country	England
Document type	Journal Article
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbac510
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Article ; Online: MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation.

Ibtehaz, Nabil / Rahman, M Sohel

Neural networks : the official journal of the International Neural Network Society

2019 Volume 121, Page(s) 74–87

Abstract: In recent years Deep Learning has brought about a breakthrough in Medical Image Segmentation. In this regard, U-Net has been the most popular architecture in the medical imaging community. Despite outstanding overall performance in segmenting multimodal ... ...

Abstract	In recent years Deep Learning has brought about a breakthrough in Medical Image Segmentation. In this regard, U-Net has been the most popular architecture in the medical imaging community. Despite outstanding overall performance in segmenting multimodal medical images, through extensive experimentations on some challenging datasets, we demonstrate that the classical U-Net architecture seems to be lacking in certain aspects. Therefore, we propose some modifications to improve upon the already state-of-the-art U-Net model. Following these modifications, we develop a novel architecture, MultiResUNet, as the potential successor to the U-Net architecture. We have tested and compared MultiResUNet with the classical U-Net on a vast repertoire of multimodal medical images. Although only slight improvements in the cases of ideal images are noticed, remarkable gains in performance have been attained for the challenging ones. We have evaluated our model on five different datasets, each with their own unique challenges, and have obtained a relative improvement in performance of 10.15%, 5.07%, 2.63%, 1.41%, and 0.62% respectively. We have also discussed and highlighted some qualitatively superior aspects of MultiResUNet over classical U-Net that are not really reflected in the quantitative measures.
MeSH term(s)	Deep Learning ; Humans ; Image Processing, Computer-Assisted/methods ; Imaging, Three-Dimensional/methods ; Magnetic Resonance Imaging/methods ; Microscopy, Fluorescence/methods ; Neural Networks, Computer
Language	English
Publishing date	2019-09-04
Publishing country	United States
Document type	Journal Article
ZDB-ID	740542-x
ISSN	1879-2782 ; 0893-6080
ISSN (online)	1879-2782
ISSN	0893-6080
DOI	10.1016/j.neunet.2019.08.025
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Article ; Online: PRIEST: predicting viral mutations with immune escape capability of SARS-CoV-2 using temporal evolutionary information.

Saha, Gourab / Sawmya, Shashata / Saha, Arpita / Akil, Md Ajwad / Tasnim, Sadia / Rahman, Md Saifur / Rahman, M Sohel

Briefings in bioinformatics

2024 Volume 25, Issue 3

Abstract: The dynamic evolution of the severe acute respiratory syndrome coronavirus 2 virus is primarily driven by mutations in its genetic sequence, culminating in the emergence of variants with increased capability to evade host immune responses. Accurate ... ...

Abstract	The dynamic evolution of the severe acute respiratory syndrome coronavirus 2 virus is primarily driven by mutations in its genetic sequence, culminating in the emergence of variants with increased capability to evade host immune responses. Accurate prediction of such mutations is fundamental in mitigating pandemic spread and developing effective control measures. This study introduces a robust and interpretable deep-learning approach called PRIEST. This innovative model leverages time-series viral sequences to foresee potential viral mutations. Our comprehensive experimental evaluations underscore PRIEST's proficiency in accurately predicting immune-evading mutations. Our work represents a substantial step in utilizing deep-learning methodologies for anticipatory viral mutation analysis and pandemic response.
MeSH term(s)	SARS-CoV-2/genetics ; SARS-CoV-2/immunology ; Mutation ; Humans ; COVID-19/virology ; COVID-19/immunology ; COVID-19/genetics ; Immune Evasion/genetics ; Deep Learning ; Evolution, Molecular ; Pandemics
Language	English
Publishing date	2024-04-30
Publishing country	England
Document type	Journal Article
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbae218
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Book ; Online: Image Contrast Enhancement using Fuzzy Technique with Parameter Determination using Metaheuristics

Kabir, Mohimenul / Mobin, Jaiaid / Hassanat, Ahmad / Rahman, M. Sohel

2023

Abstract: In this work, we have presented a way to increase the contrast of an image. Our target is to find a transformation that will be image specific. We have used a fuzzy system as our transformation function. To tune the system according to an image, we have ... ...

Abstract	In this work, we have presented a way to increase the contrast of an image. Our target is to find a transformation that will be image specific. We have used a fuzzy system as our transformation function. To tune the system according to an image, we have used Genetic Algorithm and Hill Climbing in multiple ways to evolve the fuzzy system and conducted several experiments. Different variants of the method are tested on several images and two variants that are superior to others in terms of fitness are selected. We have also conducted a survey to assess the visual improvement of the enhancements made by the two variants. The survey indicates that one of the methods can enhance the contrast of the images visually. Comment: 14 pages, 7 figures, Image Processing, Computer Vision, Evolutionary Computation
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Neural and Evolutionary Computing
Subject code	006
Publishing date	2023-01-30
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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