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Article: Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer.

Biswas, Nupur / Chakrabarti, Saikat

2020 Volume 10, Page(s) 588221

Abstract: Cancer is the manifestation of abnormalities of different physiological processes involving genes, DNAs, RNAs, proteins, and other biomolecules whose profiles are reflected in different omics data types. As these bio-entities are very much correlated, ... ...

Abstract	Cancer is the manifestation of abnormalities of different physiological processes involving genes, DNAs, RNAs, proteins, and other biomolecules whose profiles are reflected in different omics data types. As these bio-entities are very much correlated, integrative analysis of different types of omics data, multi-omics data, is required to understanding the disease from the tumorigenesis to the disease progression. Artificial intelligence (AI), specifically machine learning algorithms, has the ability to make decisive interpretation of "big"-sized complex data and, hence, appears as the most effective tool for the analysis and understanding of multi-omics data for patient-specific observations. In this review, we have discussed about the recent outcomes of employing AI in multi-omics data analysis of different types of cancer. Based on the research trends and significance in patient treatment, we have primarily focused on the AI-based analysis for determining cancer subtypes, disease prognosis, and therapeutic targets. We have also discussed about AI analysis of some non-canonical types of omics data as they have the capability of playing the determiner role in cancer patient care. Additionally, we have briefly discussed about the data repositories because of their pivotal role in multi-omics data storing, processing, and analysis.
Language	English
Publishing date	2020-10-14
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2649216-7
ISSN	2234-943X
ISSN	2234-943X
DOI	10.3389/fonc.2020.588221
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer

Nupur Biswas / Saikat Chakrabarti

Frontiers in Oncology, Vol

2020 Volume 10

Abstract	Cancer is the manifestation of abnormalities of different physiological processes involving genes, DNAs, RNAs, proteins, and other biomolecules whose profiles are reflected in different omics data types. As these bio-entities are very much correlated, integrative analysis of different types of omics data, multi-omics data, is required to understanding the disease from the tumorigenesis to the disease progression. Artificial intelligence (AI), specifically machine learning algorithms, has the ability to make decisive interpretation of “big”-sized complex data and, hence, appears as the most effective tool for the analysis and understanding of multi-omics data for patient-specific observations. In this review, we have discussed about the recent outcomes of employing AI in multi-omics data analysis of different types of cancer. Based on the research trends and significance in patient treatment, we have primarily focused on the AI-based analysis for determining cancer subtypes, disease prognosis, and therapeutic targets. We have also discussed about AI analysis of some non-canonical types of omics data as they have the capability of playing the determiner role in cancer patient care. Additionally, we have briefly discussed about the data repositories because of their pivotal role in multi-omics data storing, processing, and analysis.
Keywords	artificial intelligence (AI) ; multi-omics analyses ; cancer ; machine learning ; precision medicine ; Neoplasms. Tumors. Oncology. Including cancer and carcinogens ; RC254-282
Subject code	006
Language	English
Publishing date	2020-10-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Designing neoantigen cancer vaccines, trials, and outcomes.

Biswas, Nupur / Chakrabarti, Shweta / Padul, Vijay / Jones, Lawrence D / Ashili, Shashaanka

Frontiers in immunology

2023 Volume 14, Page(s) 1105420

Abstract: Neoantigen vaccines are based on epitopes of antigenic parts of mutant proteins expressed in cancer cells. These highly immunogenic antigens may trigger the immune system to combat cancer cells. Improvements in sequencing technology and computational ... ...

Abstract	Neoantigen vaccines are based on epitopes of antigenic parts of mutant proteins expressed in cancer cells. These highly immunogenic antigens may trigger the immune system to combat cancer cells. Improvements in sequencing technology and computational tools have resulted in several clinical trials of neoantigen vaccines on cancer patients. In this review, we have looked into the design of the vaccines which are undergoing several clinical trials. We have discussed the criteria, processes, and challenges associated with the design of neoantigens. We searched different databases to track the ongoing clinical trials and their reported outcomes. We observed, in several trials, the vaccines boost the immune system to combat the cancer cells while maintaining a reasonable margin of safety. Detection of neoantigens has led to the development of several databases. Adjuvants also play a catalytic role in improving the efficacy of the vaccine. Through this review, we can conclude that the efficacy of vaccines can make it a potential treatment across different types of cancers.
MeSH term(s)	Humans ; Antigens, Neoplasm ; Cancer Vaccines ; Neoplasms ; Immune System ; Epitopes
Chemical Substances	Antigens, Neoplasm ; Cancer Vaccines ; Epitopes
Language	English
Publishing date	2023-02-09
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2606827-8
ISSN	1664-3224 ; 1664-3224
ISSN (online)	1664-3224
ISSN	1664-3224
DOI	10.3389/fimmu.2023.1105420
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Article: MicroRNA-21 Silencing in Diabetic Nephropathy: Insights on Therapeutic Strategies.

Dhas, Yogita / Arshad, Numair / Biswas, Nupur / Jones, Lawrence D / Ashili, Shashaanka

Biomedicines

2023 Volume 11, Issue 9

Abstract: In diabetes, possibly the most significant site of microvascular damage is the kidney. Due to diabetes and/or other co-morbidities, such as hypertension and age-related nephron loss, a significant number of people with diabetes suffer from kidney ... ...

Abstract	In diabetes, possibly the most significant site of microvascular damage is the kidney. Due to diabetes and/or other co-morbidities, such as hypertension and age-related nephron loss, a significant number of people with diabetes suffer from kidney diseases. Improved diabetic care can reduce the prevalence of diabetic nephropathy (DN); however, innovative treatment approaches are still required. MicroRNA-21 (miR-21) is one of the most studied multipotent microRNAs (miRNAs), and it has been linked to renal fibrosis and exhibits significantly altered expression in DN. Targeting miR-21 offers an advantage in DN. Currently, miR-21 is being pharmacologically silenced through various methods, all of which are in early development. In this review, we summarize the role of miR-21 in the molecular pathogenesis of DN and several therapeutic strategies to use miR-21 as a therapeutic target in DN. The existing experimental interventions offer a way to rectify the lower miRNA levels as well as to reduce the higher levels. Synthetic miRNAs also referred to as miR-mimics, can compensate for abnormally low miRNA levels. Furthermore, strategies like oligonucleotides can be used to alter the miRNA levels. It is reasonable to target miR-21 for improved results because it directly contributes to the pathological processes of kidney diseases, including DN.
Language	English
Publishing date	2023-09-20
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2720867-9
ISSN	2227-9059
ISSN	2227-9059
DOI	10.3390/biomedicines11092583
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Article ; Online: MicroRNA-21 Silencing in Diabetic Nephropathy

Yogita Dhas / Numair Arshad / Nupur Biswas / Lawrence D. Jones / Shashaanka Ashili

Biomedicines, Vol 11, Iss 2583, p

Insights on Therapeutic Strategies

2023 Volume 2583

Abstract	In diabetes, possibly the most significant site of microvascular damage is the kidney. Due to diabetes and/or other co-morbidities, such as hypertension and age-related nephron loss, a significant number of people with diabetes suffer from kidney diseases. Improved diabetic care can reduce the prevalence of diabetic nephropathy (DN); however, innovative treatment approaches are still required. MicroRNA-21 (miR-21) is one of the most studied multipotent microRNAs (miRNAs), and it has been linked to renal fibrosis and exhibits significantly altered expression in DN. Targeting miR-21 offers an advantage in DN. Currently, miR-21 is being pharmacologically silenced through various methods, all of which are in early development. In this review, we summarize the role of miR-21 in the molecular pathogenesis of DN and several therapeutic strategies to use miR-21 as a therapeutic target in DN. The existing experimental interventions offer a way to rectify the lower miRNA levels as well as to reduce the higher levels. Synthetic miRNAs also referred to as miR-mimics, can compensate for abnormally low miRNA levels. Furthermore, strategies like oligonucleotides can be used to alter the miRNA levels. It is reasonable to target miR-21 for improved results because it directly contributes to the pathological processes of kidney diseases, including DN.
Keywords	nephropathy ; chronic kidney disease ; diabetes ; pharmacological silencing of miR-21 ; LNA-21 ; antagomirs ; Biology (General) ; QH301-705.5
Subject code	500
Language	English
Publishing date	2023-09-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Impact of vaccination on SARS-CoV-2 infection

Nupur Pal / Debalina Nag / Jayeeta Halder / Aritra Biswas / Raja Ray / Avijit Hazra / Chitrita Chatterjee

Asian Pacific Journal of Tropical Medicine, Vol 15, Iss 2, Pp 90-

Experience from a tertiary care hospital

2022 Volume 92

Keywords	Arctic medicine. Tropical medicine ; RC955-962
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Wolters Kluwer Medknow Publications
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Impact of Action Taken in Response to Stillbirth Audit: A Success Story.

Kumar, Manisha / Puri, Manju / Suka, Millo / Chawla, Nupur / Kaur, Gagan Preet / Yadav, Reena / Agrawal, Kiran / Biswas, Ratna

Journal of obstetrics and gynaecology of India

2023 Volume 73, Issue Suppl 1, Page(s) 61–68

Abstract: Objectives: Study the impact of intra-facility interventions on the modifiable factors causing stillbirths (SB), using point-of-care quality improvement (POCQI) methodology.: Material and methods: Stillbirth data during the 9 months pre-intervention ... ...

Abstract	Objectives: Study the impact of intra-facility interventions on the modifiable factors causing stillbirths (SB), using point-of-care quality improvement (POCQI) methodology. Material and methods: Stillbirth data during the 9 months pre-intervention period were reviewed to identify the common preventable causes. Two interventions, namely, ultrasound at 34-36 weeks gestation and intrapartum monitoring on a common customized labor chart for all health-care providers, were done. Post-intervention data were collected to observe the impact of the interventions. Results: The stillbirth rate reduced from 212/5940 deliveries (35.7/1000) in the pre-intervention period to 165/5993 deliveries (27.7/1000) in the post-intervention period ( Conclusion: Reviewing the perinatal death surveillance response (PDSR) data, identifying gaps in care, and using improvement methodology for instituting corrective measures play an important role in reducing intramural stillbirths.
Language	English
Publishing date	2023-08-23
Publishing country	India
Document type	Journal Article
ZDB-ID	410688-x
ISSN	0971-9202 ; 0022-3190
ISSN	0971-9202 ; 0022-3190
DOI	10.1007/s13224-023-01808-1
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Article: Smart Consumer Wearables as Digital Diagnostic Tools: A Review.

Chakrabarti, Shweta / Biswas, Nupur / Jones, Lawrence D / Kesari, Santosh / Ashili, Shashaanka

Diagnostics (Basel, Switzerland)

2022 Volume 12, Issue 9

Abstract: The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services. These devices, which carry different types of sensors, have emerged as ... ...

Abstract	The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services. These devices, which carry different types of sensors, have emerged as personalized digital diagnostic tools. Data from such devices have enabled the prediction and detection of various physiological as well as psychological conditions and diseases. In this review, we have focused on the diagnostic applications of wrist-worn wearables to detect multiple diseases such as cardiovascular diseases, neurological disorders, fatty liver diseases, and metabolic disorders, including diabetes, sleep quality, and psychological illnesses. The fruitful usage of wearables requires fast and insightful data analysis, which is feasible through machine learning. In this review, we have also discussed various machine-learning applications and outcomes for wearable data analyses. Finally, we have discussed the current challenges with wearable usage and data, and the future perspectives of wearable devices as diagnostic tools for research and personalized healthcare domains.
Language	English
Publishing date	2022-08-31
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2662336-5
ISSN	2075-4418
ISSN	2075-4418
DOI	10.3390/diagnostics12092110
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Article ; Online: Binned Data Provide Better Imputation of Missing Time Series Data from Wearables.

Chakrabarti, Shweta / Biswas, Nupur / Karnani, Khushi / Padul, Vijay / Jones, Lawrence D / Kesari, Santosh / Ashili, Shashaanka

Sensors (Basel, Switzerland)

2023 Volume 23, Issue 3

Abstract: The presence of missing values in a time-series dataset is a very common and well-known problem. Various statistical and machine learning methods have been developed to overcome this problem, with the aim of filling in the missing values in the data. ... ...

Abstract	The presence of missing values in a time-series dataset is a very common and well-known problem. Various statistical and machine learning methods have been developed to overcome this problem, with the aim of filling in the missing values in the data. However, the performances of these methods vary widely, showing a high dependence on the type of data and correlations within the data. In our study, we performed some of the well-known imputation methods, such as expectation maximization, k-nearest neighbor, iterative imputer, random forest, and simple imputer, to impute missing data obtained from smart, wearable health trackers. In this manuscript, we proposed the use of data binning for imputation. We showed that the use of data binned around the missing time interval provides a better imputation than the use of a whole dataset. Imputation was performed for 15 min and 1 h of continuous missing data. We used a dataset with different bin sizes, such as 15 min, 30 min, 45 min, and 1 h, and we carried out evaluations using root mean square error (RMSE) values. We observed that the expectation maximization algorithm worked best for the use of binned data. This was followed by the simple imputer, iterative imputer, and k-nearest neighbor, whereas the random forest method had no effect on data binning during imputation. Moreover, the smallest bin sizes of 15 min and 1 h were observed to provide the lowest RMSE values for the majority of the time frames during the imputation of 15 min and 1 h of missing data, respectively. Although applicable to digital health data, we think that this method will also find applicability in other domains.
MeSH term(s)	Time Factors ; Algorithms ; Random Forest ; Wearable Electronic Devices
Language	English
Publishing date	2023-01-28
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s23031454
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Article ; Online: Integrating Multi-Omics Data to Construct Reliable Interconnected Models of Signaling, Gene Regulatory, and Metabolic Pathways.

Kumar, Krishna / Bhowmik, Debaleena / Mandloi, Sapan / Gautam, Anupam / Lahiri, Abhishake / Biswas, Nupur / Paul, Sandip / Chakrabarti, Saikat

Methods in molecular biology (Clifton, N.J.)

2023 Volume 2634, Page(s) 139–151

Abstract: Alteration of the status of the metabolic enzymes could be a probable way to regulate metabolic reprogramming, which is a critical cellular adaptation mechanism especially for cancer cells. Coordination among biological pathways, such as gene-regulatory, ...

Abstract	Alteration of the status of the metabolic enzymes could be a probable way to regulate metabolic reprogramming, which is a critical cellular adaptation mechanism especially for cancer cells. Coordination among biological pathways, such as gene-regulatory, signaling, and metabolic pathways is crucial for regulating metabolic adaptation. Also, incorporation of resident microbial metabolic potential in human body can influence the interplay between the microbiome and the systemic or tissue metabolic environments. Systemic framework for model-based integration of multi-omics data can ultimately improve our understanding of metabolic reprogramming at holistic level. However, the interconnectivity and novel meta-pathway regulatory mechanisms are relatively lesser explored and understood. Hence, we propose a computational protocol that utilizes multi-omics data to identify probable cross-pathway regulatory and protein-protein interaction (PPI) links connecting signaling proteins or transcription factors or miRNAs to metabolic enzymes and their metabolites using network analysis and mathematical modeling. These cross-pathway links were shown to play important roles in metabolic reprogramming in cancer scenarios.
MeSH term(s)	Humans ; Multiomics ; MicroRNAs/genetics ; Signal Transduction ; Metabolic Networks and Pathways ; Neoplasms/genetics
Chemical Substances	MicroRNAs
Language	English
Publishing date	2023-04-19
Publishing country	United States
Document type	Journal Article
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-0716-3008-2_6
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