ISLAND: in-silico proteins binding affinity prediction using sequence information

Abbasi, Wajid Arshad; Yaseen, Adiba; Hassan, Fahad; Andleeb, Saiqa; Minhas, Fayyaz ul Amir Afsar

doi:10.1186/s13040-020-00231-w

Cited by 52 publications

(56 citation statements)

References 56 publications

(124 reference statements)

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“…In this study, we have optimized RF for the number of decision trees in the forest, the maximum number of features considered for splitting a node, the maximum number of levels in each decision tree, and a minimum number of samples required to split. We have also seen this classification technique effectively in action in many other studies [ [55] , [56] , [57] , [58] ].…”

Section: Methodsmentioning

confidence: 89%

COVIDC: An expert system to diagnose COVID-19 and predict its severity using chest CT scans: Application in radiology

Abbasi

Abbas

Andleeb

et al. 2021

Informatics in Medicine Unlocked

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Early diagnosis of Coronavirus disease 2019 (COVID-19) is significantly important, especially in the absence or inadequate provision of a specific vaccine, to stop the surge of this lethal infection by advising quarantine. This diagnosis is challenging as most of the patients having COVID-19 infection stay asymptomatic while others showing symptoms are hard to distinguish from patients having different respiratory infections such as severe flu and Pneumonia. Due to cost and time-consuming wet-lab diagnostic tests for COVID-19, there is an utmost requirement for some alternate, non-invasive, rapid, and discounted automatic screening system. A chest CT scan can effectively be used as an alternative modality to detect and diagnose the COVID-19 infection. In this study, we present an automatic COVID-19 diagnostic and severity prediction system called COVIDC (COVID-19 detection using CT scans) that uses deep feature maps from the chest CT scans for this purpose. Our newly proposed system not only detects COVID-19 but also predicts its severity by using a two-phase classification approach (COVID vs non-COVID, and COVID-19 severity) with deep feature maps and different shallow supervised classification algorithms such as SVMs and random forest to handle data scarcity. We performed a stringent COVIDC performance evaluation not only through 10-fold cross-validation and an external validation dataset but also in a real setting under the supervision of an experienced radiologist. In all the evaluation settings, COVIDC outperformed all the existing state-of-the-art methods designed to detect COVID-19 with an F1 score of 0.94 on the validation dataset and justified its use to diagnose COVID-19 effectively in the real setting by classifying correctly 9 out of 10 COVID-19 CT scans. We made COVIDC openly accessible through a cloud-based webserver and python code available at https://sites.google.com/view/wajidarshad/software and https://github.com/wajidarshad/covidc.

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Section: Methodsmentioning

confidence: 89%

COVIDC: An expert system to diagnose COVID-19 and predict its severity using chest CT scans: Application in radiology

Abbasi

Abbas

Andleeb

et al. 2021

Informatics in Medicine Unlocked

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“…Because the affinity is driven by structure, we believe the PDB classifier might also be optimised for approximate affinity prediction, although better methods of modelling the mutations into the structures might have to be explored. However, this is known to be a very hard problem, which is only starting to become tractable on simpler systems (see, for instance, Leidner et al, 2019;Abbasi et al, 2020;Jiang et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Predicting T Cell Receptor Antigen Specificity From Structural Features Derived From Homology Models of Receptor-Peptide-Major Histocompatibility Complexes

2021

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The physical interaction between the T cell receptor (TCR) and its cognate antigen causes T cells to activate and participate in the immune response. Understanding this physical interaction is important in predicting TCR binding to a target epitope, as well as potential cross-reactivity. Here, we propose a way of collecting informative features of the binding interface from homology models of T cell receptor-peptide-major histocompatibility complex (TCR-pMHC) complexes. The information collected from these structures is sufficient to discriminate binding from non-binding TCR-pMHC pairs in multiple independent datasets. The classifier is limited by the number of crystal structures available for the homology modelling and by the size of the training set. However, the classifier shows comparable performance to sequence-based classifiers requiring much larger training sets.

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“…In this study, we have optimized RF for the number of decision trees in the forest, the maximum number of features considered for splitting a node, the maximum number of levels in each decision tree, and a minimum number of samples required to split. We have also seen this machine learning technique effectively in use in many other studies [ 36 – 39 ].…”

Section: Methodsmentioning

confidence: 99%

ESIDE: A computationally intelligent method to identify earthworm species (E. fetida) from digital images: Application in taxonomy

et al. 2021

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Earthworms (Crassiclitellata) being ecosystem engineers significantly affect the physical, chemical, and biological properties of the soil by recycling organic material, increasing nutrient availability, and improving soil structure. The efficiency of earthworms in ecology varies along with species. Therefore, the role of taxonomy in earthworm study is significant. The taxonomy of earthworms cannot reliably be established through morphological characteristics because the small and simple body plan of the earthworm does not have anatomical complex and highly specialized structures. Recently, molecular techniques have been adopted to accurately classify the earthworm species but these techniques are time-consuming and costly. To combat this issue, in this study, we propose a machine learning-based earthworm species identification model that uses digital images of earthworms. We performed a stringent performance evaluation not only through 10-fold cross-validation and on an external validation dataset but also in real settings by involving an experienced taxonomist. In all the evaluation settings, our proposed model has given state-of-the-art performance and justified its use to aid earthworm taxonomy studies. We made this model openly accessible through a cloud-based webserver and python code available at https://sites.google.com/view/wajidarshad/software and https://github.com/wajidarshad/ESIDE.

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ISLAND: in-silico proteins binding affinity prediction using sequence information

Cited by 52 publications

References 56 publications

COVIDC: An expert system to diagnose COVID-19 and predict its severity using chest CT scans: Application in radiology

COVIDC: An expert system to diagnose COVID-19 and predict its severity using chest CT scans: Application in radiology

Predicting T Cell Receptor Antigen Specificity From Structural Features Derived From Homology Models of Receptor-Peptide-Major Histocompatibility Complexes

ESIDE: A computationally intelligent method to identify earthworm species (E. fetida) from digital images: Application in taxonomy

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