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Article ; Online: A network-based systems biology approach for identification of shared Gene signatures between male and female in COVID-19 datasets

Md Shahjaman / Md Rezanur Rahman / Md Rabiul Auwul

Informatics in Medicine Unlocked, Vol 25, Iss , Pp 100702- (2021)

2021

Abstract: The novel coronavirus (SARS-CoV-2) has expanded rapidly worldwide. Now it has covered more than 150 countries worldwide. It is referred to as COVID-19. SARS-CoV-2 mainly affects the respiratory systems of humans that can lead up to serious illness or ... ...

Abstract	The novel coronavirus (SARS-CoV-2) has expanded rapidly worldwide. Now it has covered more than 150 countries worldwide. It is referred to as COVID-19. SARS-CoV-2 mainly affects the respiratory systems of humans that can lead up to serious illness or even death in the presence of different comorbidities. However, most COVID-19 infected people show mild to moderate symptoms, and no medication is suggested. Still, drugs of other diseases have been used to treat COVID-19. Nevertheless, the absence of vaccines and proper drugs against the COVID-19 virus has increased the mortality rate. Albeit sex is a risk factor for COVID-19, none of the studies considered this risk factor for identifying biomarkers from the RNASeq count dataset. Men are more likely to undertake severe symptoms with different comorbidities and show greater mortality compared with women. From this standpoint, we aim to identify shared gene signatures between males and females from the human COVID-19 RNAseq count dataset of peripheral blood cells using a robust voom approach. We identified 1341 overlapping DEGs between male and female datasets. The gene ontology (GO) annotation and pathway enrichment analysis revealed that DEGs are involved in various BP categories such as nucleosome assembly, DNA conformation change, DNA packaging, and different KEGG pathways such as cell cycle, ECM-receptor interaction, progesterone-mediated oocyte maturation, etc. Ten hub-proteins (UBC, KIAA0101, APP, CDK1, SUMO2, SP1, FN1, CDK2, E2F1, and TP53) were unveiled using PPI network analysis. The top three miRNAs (mir-17–5p, mir-20a-5p, mir-93–5p) and TFs (PPARG, E2F1 and KLF5) were uncovered. In conclusion, the top ten significant drugs (roscovitine, curcumin, simvastatin, fulvestrant, troglitazone, alvocidib, L-alanine, tamoxifen, serine, and doxorubicin) were retrieved using drug repurposing analysis of overlapping DEGs, which might be therapeutic agents of COVID-19.
Keywords	Coronavirus ; SARS-CoV-2 ; COVID-19 ; Sex-specific biomarkers ; Robust voom ; Hub-proteins ; Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	572
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Biodegradation of polyethylene and polystyrene by Zophobas atratus larvae from Bangladeshi source and isolation of two plastic-degrading gut bacteria.

Zaman, Ifthikhar / Turjya, Rafeed Rahman / Shakil, Md Salman / Al Shahariar, Mahruf / Emu, Md Rezanur Rahman Howlader / Ahmed, Akash / Hossain, M Mahboob

Environmental pollution (Barking, Essex : 1987)

2024 Volume 345, Page(s) 123446

Abstract: Plastic pollution has become a major environmental concern globally, and novel and eco-friendly approaches like bioremediation are essential to mitigate the impact. Low-density polyethylene (LDPE), linear low-density polyethylene (LLDPE), and expanded ... ...

Abstract	Plastic pollution has become a major environmental concern globally, and novel and eco-friendly approaches like bioremediation are essential to mitigate the impact. Low-density polyethylene (LDPE), linear low-density polyethylene (LLDPE), and expanded polystyrene (EPS) are three of the most frequently used plastic types. This study examined biodegradation of these using Zophobas atratus larvae, followed by isolation and whole genome sequencing of gut bacteria collected from larvae frass. Over 36 days, 24.04 % LDPE, 20.01 % EPS, and 15.12 % LLDPE were consumed on average by the larvae, with survival rates of 85 %, 90 %, and 87 %, respectively. Fourier transform infrared spectroscopy (FTIR) analysis of fresh plastic types, consumed plastics, and larvae frass showed proof of plastic oxidation in the gut. Frass bacteria were isolated and cultured in minimal salt media supplemented with plastics as the sole carbon source. Two isolates of bacteria were sampled from these cultures, designated PDB-1 and PDB-2. PDB-1 could survive on LDPE and LLDPE as carbon sources, whereas PDB-2 could survive on EPS. Scanning Electron Microscopy (SEM) provided proof of degradation in both cases. Both isolates were identified as strains of Pseudomonas aeruginosa, followed by sequencing, assembly, and annotation of their genomes. LDPE- and LLDPE-degrading enzymes e.g., P450 monooxygenase, alkane monooxygenase, alcohol dehydrogenase, etc. were identified in PDB-1. Similarly, phenylacetaldehyde dehydrogenase and other enzymes involved in EPS degradation were identified in PDB-2. Genes of both isolates were compared with genomes of known plastic-degrading P. aeruginosa strains. Virulence factors, antibiotic-resistance genes, and rhamnolipid biosurfactant biosynthesis genes were also identified in both isolates. This study indicated Zophobas atratus larvae as potential LDPE, LLDPE, and EPS biodegradation agent. Additionally, the isolated strains of Pseudomonas aeruginosa provide a more direct and eco-friendly solution for plastic degradation. Confirmation and modification of the plastic-degrading pathways in the bacteria may create scope for metabolic engineering in the future.
MeSH term(s)	Animals ; Polyethylene/chemistry ; Polystyrenes/metabolism ; Larva/metabolism ; Biodegradation, Environmental ; Coleoptera ; Bacteria/genetics ; Bacteria/metabolism ; Pseudomonas aeruginosa/metabolism ; Mixed Function Oxygenases/metabolism ; Carbon/metabolism ; Plastics/metabolism
Chemical Substances	Polyethylene (9002-88-4) ; Polystyrenes ; Mixed Function Oxygenases (EC 1.-) ; Carbon (7440-44-0) ; Plastics
Language	English
Publishing date	2024-01-29
Publishing country	England
Document type	Journal Article
ZDB-ID	280652-6
ISSN	1873-6424 ; 0013-9327 ; 0269-7491
ISSN (online)	1873-6424
ISSN	0013-9327 ; 0269-7491
DOI	10.1016/j.envpol.2024.123446
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Article ; Online: Integrated multi-omics approach identified molecular mechanism and pathogenetic processes of COVID-19 that affect patient with Parkinson’s disorder

Hongxia Zhao / Qinghua Zhang / Huifang Chen / Md Rezanur Rahman / Hossain Md Faruquee

Saudi Journal of Biological Sciences, Vol 28, Iss 12, Pp 6939-

2021 Volume 6945

Abstract: The novel coronavirus named SARS-CoV-2 has emerged at the end of 2019, which causes coronavirus disease (COVID-2019). Recent case reports of COVID-19 patients have revealed the onset of Parkinson's disease (PD) symptoms in patients who do not have a ... ...

Abstract	The novel coronavirus named SARS-CoV-2 has emerged at the end of 2019, which causes coronavirus disease (COVID-2019). Recent case reports of COVID-19 patients have revealed the onset of Parkinson's disease (PD) symptoms in patients who do not have a family history of the PD. However, till recently, no genetic impact or mechanisms that may induce Parkinsonism in COVID-19 patients or after COVID-19 have been found. This study aimed to detect the commonly dysregulated genes, transcriptional regulators, and pathways between PD and COVID-19. We integrated genome-wide transcriptomic datasets from peripheral blood mononuclear cells (PBMC) samples from COVID-19 and PD and associated pathways. Our study revealed 81 upregulated and 48 downregulated differentially expressed genes (DEGs) shared between PD and COVID-19. These dysregulated genes were involved in key pathways “mitochondrion structure organization”, “cell activation in immune response”, and “signalling by interleukins”. Our analysis showed RELA, TP53 and SP1 TFs that may regulate the upregulated DEGs. We have discovered key dysregulated genes and characterized the biological processes of commonly dysregulated in COVID-19 and PD, which could be used for the design of personalized treatment of PD following COVID-19.
Keywords	COVID-19 ; Parkinson’s disorder ; Blood gene expression ; Transcriptional signatures ; Molecular pathways ; Biology (General) ; QH301-705.5
Subject code	570
Language	English
Publishing date	2021-12-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
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Article ; Online: Identifying the function of methylated genes in Alzheimer’s disease to determine epigenetic signatures

Md Rezanur Rahman / Tania Islam / Esra Gov / Julian M.W. Quinn / Mohammad Ali Moni / Sourav Kolay

Experimental Results, Vol

a comprehensive bioinformatics analysis

2021 Volume 2

Abstract: Gene methylation is one means of controlling tissue gene expression, but it is unknown what pathways influencing Alzheimer’s disease (AD) are controlled this way. We compared normal and AD brain tissue data for gene expression (mRNAs) and gene ... ...

Abstract	Gene methylation is one means of controlling tissue gene expression, but it is unknown what pathways influencing Alzheimer’s disease (AD) are controlled this way. We compared normal and AD brain tissue data for gene expression (mRNAs) and gene methylation profiling. We identified methylated differentially expressed genes (MDEGs). Protein-protein interaction (PPI) of the MDEGs showed 18 hypermethylated low-expressed genes (Hyper-LGs) involved in cell signaling and metabolism; also 10 hypomethylated highly expressed (Hypo-HGs) were involved in regulation of transcription and development. Molecular pathways enriched in Hyper-LGs included neuroactive ligand-receptor interaction pathways. Hypo-HGs were notably enriched in pathways including hippo signaling. PPI analysis also identified both Hyper-LGs and Hypo-HGs, as hub proteins. Our analysis of AD datasets identified Hyper-LGs, Hypo-HGs, and transcription factors linked to these genes. These pathways, which may participate in Alzheimer’s disease development, may be affected by treatments that influence gene methylation patterns.
Keywords	Alzheimer’s disease ; epigenetics ; biomarkers ; methylated differentially expressed genes ; Technology ; T ; Medicine ; R ; Science ; Q
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Cambridge University Press
Document type	Article ; Online
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Article ; Online: Identification of molecular signatures and pathways common to blood cells and brain tissue of amyotrophic lateral sclerosis patients

Md. Rezanur Rahman / Tania Islam / Fazlul Huq / Julian M.W. Quinn / Mohammad Ali Moni

Informatics in Medicine Unlocked, Vol 16, Iss , Pp - (2019)

2019

Abstract: Amyotrophic lateral sclerosis (ALS) is a progressive motor neuron disease that is characterized by the death of neurons controlling voluntary muscles. Early diagnosis of ALS is difficult and detection is limited in sensitivity and specificity as well as ... ...

Abstract	Amyotrophic lateral sclerosis (ALS) is a progressive motor neuron disease that is characterized by the death of neurons controlling voluntary muscles. Early diagnosis of ALS is difficult and detection is limited in sensitivity and specificity as well as by cost. Therefore, detecting ALS from blood cell analysis could improve the early diagnosis and treatment of the disease. The present study aimed to identify blood cell transcripts that reflect brain expression levels of factors linked to ALS progression. We analyzed blood cell and brain transcriptomics gene expression datasets (RNA-seq and microarray) in blood and brain. We identified 13 differentially expressed genes (DEG; ALS versus controls) common to blood cells and brain (DNAH6, HLA-DMB, HLA-A, EHD2, CMKLR1, PROS1, GAPT, CCR1, THBS1, CDK2, RAB27A, ITGB3 and C1orf162) that were commonly dysregulated between ALS blood and brain tissues. These data revealed significant neurodegeneration-associated molecular pathways in the signaling systems. Integration of these different analyses revealed dysregulation of a number of transcription factors, namely SP1, MYC, TP3, CTCF and SRF. In addition, we identified microRNAs altered in ALS: miR-29c, miR-21, let-7a, miR-377, miR-103, miR-369-3p, miR-494, miR-204, miR-29a. Thus, we have identified possible new links between pathological processes in the brain and transcripts in blood cells in ALS subjects that may enable the use of blood samples to diagnose and monitor ALS onset and progression. Keywords: Amyotrophic lateral sclerosis, Molecular biomarkers, Blood-brain common gene, Differentially expressed genes, Protein-protein interaction, Transcription factors, microRNAs
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7
Language	English
Publishing date	2019-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A Robust Approach for Identification of Cancer Biomarkers and Candidate Drugs

Md. Shahjaman / Md. Rezanur Rahman / S. M. Shahinul Islam / Md. Nurul Haque Mollah

Medicina, Vol 55, Iss 6, p

2019 Volume 269

Abstract: Background and objectives: Identification of cancer biomarkers that are differentially expressed (DE) between two biological conditions is an important task in many microarray studies. There exist several methods in the literature in this regards and ... ...

Abstract	Background and objectives: Identification of cancer biomarkers that are differentially expressed (DE) between two biological conditions is an important task in many microarray studies. There exist several methods in the literature in this regards and most of these methods designed especially for unpaired samples, those are not suitable for paired samples. Furthermore, the traditional methods use p -values or fold change (FC) values to detect the DE genes. However, sometimes, p -value based results do not comply with FC based results due to the smaller pooled variance of gene expressions, which occurs when variance of each individual condition becomes smaller. There are some methods that combine both p -values and FC values to solve this problem. But, those methods also show weak performance for small sample cases in the presence of outlying expressions. To overcome this problem, in this paper, an attempt is made to propose a hybrid robust SAM-FC approach by combining rank of FC values and rank of p -values computed by SAM statistic using minimum β -divergence method, which is designed for paired samples. Materials and Methods : The proposed method introduces a weight function known as β -weight function. This weight function produces larger weights corresponding to usual and smaller weights for unusual expressions. The β -weight function plays the significant role on the performance of the proposed method. The proposed method uses β -weight function as a measure of outlier detection by setting β = 0.2. We unify both classical and robust estimates using β -weight function, such that maximum likelihood estimators (MLEs) are used in absence of outliers and minimum β -divergence estimators are used in presence of outliers to obtain reasonable p -values and FC values in the proposed method. Results: We examined the performance of proposed method in a comparison of some popular methods ( t -test, SAM, LIMMA, Wilcoxon, WAD, RP, and FCROS) using both simulated and real gene expression profiles for both small and large ...
Keywords	candidate drugs ; cancer biomarkers ; DEGs ; FC ; p -value ; paired samples ; minimum β -divergence estimation ; robustness ; Medicine (General) ; R5-920
Subject code	310
Language	English
Publishing date	2019-06-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Network-based approach to identify key candidate genes and pathways shared by thyroid cancer and chronic kidney disease

Md Ali Hossain / Tania Akter Asa / Md Rezanur Rahman / Mohammad Ali Moni

Informatics in Medicine Unlocked, Vol 16, Iss , Pp - (2019)

2019

Abstract: Thyroid cancer (TC) is one of the fastest-growing cancers in the world. Thyroid dysfunction has an influence on chronic kidney disease (CKD), but the possible relations between TC and CDK have not been explored yet. The present study aimed to identify ... ...

Abstract	Thyroid cancer (TC) is one of the fastest-growing cancers in the world. Thyroid dysfunction has an influence on chronic kidney disease (CKD), but the possible relations between TC and CDK have not been explored yet. The present study aimed to identify shared candidate genes and pathways between TC and CKD from the transcriptomics datasets to identify important clues to the pathological mechanisms in these diseases. The gene expression datasets of TC and CKD were obtained from Gene Expression Omnibus and analyzed to identify common genes between TC and CKD. We have detected 84 common differentially expressed genes (DEGs) between TC and CKD.We have integrated the DEGs with protein-protein interaction (PPI) network and identified ten significant hub proteins (TLE1, CTNNA1, TEK, TPM2, FGFR2, MMP2, SDC2, NRP1, TCF7L1, GSN) based on topological analysis. The integration of DEGs with biomolecular networks revealed transcription factors (FOXC1, GATA2, FOXL1, HINFP, LIMK2, E2F1, POU2F2, TFAP2A, YY1, RERE) and microRNAs (mir-335–5p, mir-26b-5p, mir-124–3p, mir-181a-5p, mir-98–5p, mir-16–5p, mir-7-5p, mir-129–5p, mir-8485, mir-1827). The present study identified biomolecules shared by TC and CKD at protein levels (hub proteins, transcription factors), and RNA levels (mRNAs, microRNAs). Basic biological experiments will be needed to establish them as biomarkers in TC and CKD. Keywords: Thyroid cancer, Kidney diseases, Comorbidity, Protein-protein interaction
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	570
Language	English
Publishing date	2019-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
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Article ; Online: Bioinformatics and system biology approaches to identify molecular pathogenesis of polycystic ovarian syndrome, type 2 diabetes, obesity, and cardiovascular disease that are linked to the progression of female infertility

Md Arju Hossain / Md Al Amin / Md Imran Hasan / Md Sohel / Md Akash Ahammed / S.M. Hasan Mahmud / Md Rezanur Rahman / Md Habibur Rahman

Informatics in Medicine Unlocked, Vol 30, Iss , Pp 100960- (2022)

2022

Abstract: Female infertility may be caused by several medical conditions and environmental factors that induce fallopian tube damage or hormonal difficulties. Several metabolic comorbidities like polycystic ovarian syndrome (PCOS), type 2 diabetes (T2D), obesity, ... ...

Abstract	Female infertility may be caused by several medical conditions and environmental factors that induce fallopian tube damage or hormonal difficulties. Several metabolic comorbidities like polycystic ovarian syndrome (PCOS), type 2 diabetes (T2D), obesity, and cardiovascular disease (CVD) have been associated with female infertility (FI). However, the causal connection and molecular features are still unknown. Here, we have used differentially expressed genes (DEGs) to uncover various biological targets for a better understanding of FI and metabolic comorbidities development. We have also identified 1201 DEGs in FI patients relative to healthy controls sharing a total of 112, 126, 99, and 176 DEGs with PCOS, T2D, obesity, and CVD. Besides, most of the shared pathways including calcium ion transport, regulation of acute inflammatory response, adipocytokine signaling, and MAPK family signaling cascades were identified from GO and KEGG analysis. Furthermore, we have discovered the two most significant hub proteins (C3 and AGTR1), significant transcription factors (USF2, CREB1, FOXL1, and E2F1), and potential drugs (oxacillin, cefotaxime, and valproic acid) which were directly related to FI and other metabolic diseases. Our computational analysis findings revealed the common molecular pathogenesis of FI and metabolic comorbidities which may direct new avenues of therapy and warrant future experimental validation of the key targets.
Keywords	Infertility ; Polycystic ovarian syndrome ; Type 2 diabetes ; Cardiovascular disease ; Obesity ; Metabolic comorbidities ; Computer applications to medicine. Medical informatics ; R858-859.7
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Drug repositioning and biomarkers in low-grade glioma via bioinformatics approach

Tania Islam / Md Rezanur Rahman / Md Abu Hena Shuvo / Md Shahjaman / Md Rafiqul Islam / Md Rezaul Karim

Informatics in Medicine Unlocked, Vol 17, Iss , Pp - (2019)

2019

Abstract: Aim: Diffuse low-grade glioma (LGG) patients have a tendency to develop glioblastoma multiforme within 5–10 years, with only 15 months life expectancy. The present study aimed to decode a diagnostic and prognostic gene signature in LGG and identify ... ...

Abstract	Aim: Diffuse low-grade glioma (LGG) patients have a tendency to develop glioblastoma multiforme within 5–10 years, with only 15 months life expectancy. The present study aimed to decode a diagnostic and prognostic gene signature in LGG and identify repositioned potential candidate biomarker therapeutics. Methods: We have utilized a publicly available transcriptomics dataset to identify differentially expressed genes (DEGs) using Limma. The functional annotation of the DEGs was performed to identify Gene Ontology and pathways. The protein-protein interaction (PPI) network of the (DEGs) encoding proteins was analyzed utilizing STRING via NetworkAnalyst. The ROC analysis and survival analysis of the hub gene was also performed in R and SurvExpress-a biomarker validation tool. We have utilized the L1000CDS2 to identify candidate small molecules or drugs to target the diagnostic and prognostic biomarkers. Results: We have identified 238 genes as DEGs in LGG compared to non-LGG samples involved in the biological process of signaling and metabolic systems. We then identified molecular pathways enriched by the DEGs including the VEGF signaling pathway, Fc epsilon RI signaling pathway, and p38 signaling mediated by MAPKAP kinases. We identified hub proteins (MAPK11, MRPL1, RAF1, CDC20, NOP2, TUBB4B, CTNND1, PWP2, FGR, BMP2) from PPI analysis. The ROC and survival analysis of the hub genes revealed that these biomarkers can be utilized as diagnostic and prognostic biomarker signatures in LGG. Several novel candidate drugs were repositioned; among them penfluridol, perheiline maleate is used for purposes other than cancer. Other repositioned drugs: AG14361, NCGC00185094-01, BRD-K61717269, C8273, BRD-A28970875, KIN001-127, BYL719, JW-7-24-1, HG-6-64-01, radicicol, MK-2206, and WZ-4-14 have not been described in the literature for treatment purposes. Conclusions: Our analysis of LGG datasets identified 10 hub genes as diagnostic and prognostic signatures, and the candidate repositioned drugs linked to these biomarkers. These ...
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	572
Language	English
Publishing date	2019-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: The influence of depression on ovarian cancer

Md. Rezanur Rahman / Tania Islam / Md. Abdullah Al-Mamun / Toyfiquz Zaman / Md. Rezaul Karim / Mohammad Ali Moni

Informatics in Medicine Unlocked, Vol 16, Iss , Pp - (2019)

Discovering molecular pathways that identify novel biomarkers and therapeutic targets

2019

Abstract: Depressive illness is a significant risk factor for ovarian cancer development (OC). The underlying mechanism is unclear (perhaps involving altered neuroendocrine factors), but identifying associated alterations in gene expression of OC in depression ... ...

Abstract	Depressive illness is a significant risk factor for ovarian cancer development (OC). The underlying mechanism is unclear (perhaps involving altered neuroendocrine factors), but identifying associated alterations in gene expression of OC in depression sufferers may uncover novel factors that affect OC progression. We thus analyzed microarray gene expression data from OC tissue taken from patients diagnosed with and without depression. We identified 34 differentially expressed genes (DEGs) of depression from OC patients. Gene ontology (GO) and KEGG pathway analyses indicated several molecular pathways including complement and coagulation cascades, the hippo signaling pathway, ether lipid metabolism, the MAPK signaling pathway, and antigen processing and presentation were overrepresented among DEGs. Subsequent, protein-protein interaction (PPI) analysis revealed pathway hub proteins (FOS, EGR1, JUNB, HSPA1B, FGFR3, TRIB1, CTSB, SERPINE1), regulatory transcription factors (TFs; FOS, EGR1, JUNB) and one miRNA, miR-101-5p from TFs-miRNAs coregulatory networks. The prognostic survival analysis of the DEGs revealed CXCL12, ARL4C, NQO2 associated with worse OC survival outcomes. Protein-metabolite interaction network analysis showed that upregulated protein CHPT1 (a choline phosphotransferase) interacts with four important phospholipid synthesis and signaling metabolites. Protein-drug interaction analysis revealed SERPINE1 and PLAT proteins interact with compounds that include thrombolytic drugs, plasmin inhibitors, thiazolidinediones, NSAIDs, and hypertension treatments. Thus, we identified evidence for factors and pathways altered in OC tissue when patient depression is evident, and characterized important aspects of these OC-depression links that may be instrumental in developing new treatments for OC patients. Keywords: Ovarian cancer, Depression, Drug targets, Biomarker signatures, Differentially expressed genes, Protein-protein interaction, Protein-metabolite interaction, Protein-drug interactions, Survival analysis
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	570
Language	English
Publishing date	2019-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
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