Jehad AldahdoohUniversity of Helsinki | HY
Jehad Aldahdooh
Master of Science
About
24
Publications
5,980
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552
Citations
Introduction
Additional affiliations
February 2017 - November 2017
June 2013 - January 2015
Education
February 2016 - February 2018
September 2008 - June 2013
Publications
Publications (24)
Drug combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent the emergence of drug resistance. However, discovery of synergistic and effective drug combinations has been a laborious and often serendipitous process. In recent years, identification of combination therapies has been accelerated due to the...
Bacterial processes necessary for adaption to stressful host environments are potential targets for new antimicrobials. Here, we report large-scale transcriptomic analyses of 32 human bacterial pathogens grown under 11 stress conditions mimicking human host environments. The potential relevance of the in vitro stress conditions and responses is sup...
Chemosensitivity assays are commonly used for preclinical drug discovery and clinical trial optimization. However, data from independent assays are often discordant, largely attributed to uncharacterized variation in the experimental materials and protocols. We report here the launching of Minimal Information for Chemosensitivity Assays (MICHA), ac...
In this research, we present our work participation for the DrugProt task of BioCreative VII challenge. Drug-target interactions (DTIs) are critical for drug discovery and repurposing, which are often manually extracted from the experimental articles. There are >32M biomedical articles on PubMed and manually extracting DTIs from such a huge knowled...
Background
Drug-target interactions (DTIs) are critical for drug repurposing and elucidation of drug mechanisms, and are manually curated by large databases, such as ChEMBL, BindingDB, DrugBank and DrugTargetCommons. However, the number of curated articles likely constitutes only a fraction of all the articles that contain experimentally determined...
Wireless sensor networks (WSNs) have been extensively used in various fields, such as health, defense, education, and industrial applications, to collect and transmit environmental data to the base station. However, energy efficiency is a significant challenge in WSNs, as data transmission is typically limited to a single route, leading to excessiv...
Drug-target interactions (DTIs) play a pivotal role in drug discovery, as it aims to identify potential drug targets and elucidate their mechanism of action. In recent years, the application of natural language processing (NLP), particularly when combined with pre-trained language models, has gained considerable momentum in the biomedical domain, w...
Managing traffic congestion is crucial for improving mobility, reducing fuel consumption, and mitigating environmental impacts in urban areas. To address this challenge, we present a novel framework named TCP-ACO for detecting traffic congestion that classifies congestion into three distinct types: expected, unexpected, and real-time. The framework...
The drug development process consumes 9–12 years and approximately one billion US dollars in costs. Due to the high finances and time costs required by the traditional drug discovery paradigm, repurposing old drugs to treat cancer and rare diseases is becoming popular. Computational approaches are mainly data-driven and involve a systematic analysi...
Cancer drugs often kill cells independent of their putative targets, suggesting the limitation of existing knowledge on the mechanisms of action. In this study, we explored whether the integration of loss-of-function genetic and drug sensitivity screening data can define a gene essentiality signature to better understand the drug target interaction...
Introduction/Background
Approximately 50% of high-grade serous ovarian, tubal, or primary peritoneal carcinomas (HGSC) harbor homologous recombination deficiency (HRD). HRD predicts sensitivity for platinum-based chemotherapy and is particularly crucial in selection of patients who could benefit from poly ADP-ribose polymerase inhibitor (PARPi) mai...
Drug development process consumes 9-12 years and approximately one billion US dollars in terms of costs. Due to high finances and time costs required by the traditional drug discovery paradigm, repurposing of the old drugs to treat cancer as well as rare diseases is becoming popular. Computational approaches are mainly data-driven and involve a sys...
Combinatorial therapies have been recently proposed to improve the efficacy of anticancer treatment. The SynergyFinder R package is a software used to analyze pre-clinical drug combination datasets. Here, we report the major updates to the SynergyFinder R package for improved interpretation and annotation of drug combination screening results. Unli...
In this research, we present our work participation for the DrugProt task of BioCreative VII challenge. Drug-target interactions (DTIs) are critical for drug discovery and repurposing, which are often manually extracted from the experimental articles. There are >32M biomedical articles on PubMed and manually extracting DTIs from such a huge knowled...
Background: Drug-target interactions (DTIs) are critical for drug repurposing and elucidation of drug mechanisms, and are manually curated by large databases, such as ChEMBL, BindingDB, DrugBank and DrugTargetCommons. However, the number of articles providing this data (~0.1 million) likely constitutes only a fraction of all articles on PubMed that...
Background
Drug-target interactions (DTIs) are critical for drug repurposing and elucidation of drug mechanisms, and they are collected in large databases, such as ChEMBL, BindingDB, DrugBank and DrugTargetCommons. However, the number of studies providing this data (~0.1 million) likely constitutes only a fraction of all studies on PubMed that cont...
Combinatorial therapies have recently been proposed for improving anticancer treatment efficacy. The SynergyFinder R package is a software tool to analyse pre-clinical drug combination datasets. We report the major updates to the R package to improve the interpretation and annotation of drug combination screening results. Compared to the existing i...
Combinatorial therapies that target multiple pathways have shown great promises for treating complex diseases. DrugComb (https://drugcomb.org/) is a web-based portal for the deposition and analysis of drug combination screening datasets. Since its first release, DrugComb has received continuous updates on the coverage of data resources, as well as...
Combinatorial therapies that target multiple pathways have shown great promises for treating complex diseases. DrugComb (https://drugcomb.org/) is a web-based portal for the deposition and analysis of drug combination screening datasets. Since its first release, DrugComb has received continuous updates on the coverage of data resources, as well as...
Chemosensitivity assays are commonly used for preclinical drug discovery and clinical trial optimization. However, data from independent assays are often discordant, largely attributed to uncharacterized variation in the experimental materials and protocols. Spurred by the annotation of minimum information (MI) for ensuring data reproducibility, we...
Despite being genetically diverse, bacterial pathogens can adapt to similar stressful environments in human host, but how this diversity allows them to achive this is yet not fully understood. Knowledge gained through comparative genomics is insufficient as it lacks the level of gene expression reflecting gene usage. To fill this gap, we investigat...
Studying relationships among gene products by expression profile analysis is a common approach in systems biology. Many studies have generalized the outcomes to the different levels of central dogma information flow and assumed a correlation of transcript and protein expression levels. However, the relation between the various types of interaction...
As a holistic medical school, Iranian traditional medicine (ITM) considers the human body as a dynamic and intricate network of interconnecting processes. Currently, systems biology and more precisely systems medicine and pharmacology can be an aid in providing rationalizations for many traditional medications and treatments and elucidating a great...
Drug combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent the emergence of drug resistance. However, discovery of synergistic and effective drug combinations has been a laborious and often serendipitous process. In recent years, identification of combination therapies has been accelerated due to the...
Questions
Questions (5)
What is citical for VoIP in 802.11g wireless network. End-To-End delay or wireless LAN delay?I mean it should be less than 150 msec.
Why I didn't have the same result when I calculate the balance accuracy in two different ways?
Method 1 :
balance_accuracy=recall_score(ytest,y_predicted, average='macro')
Method 2:
fpr1, tpr1, thresholds = metrics.roc_curve(ytest, y_predicted, pos_label=1.0)
spec=1-fpr1[1]
sens=tpr1[1]
bc=(sens+spec)/2
Thanks a lot
Why I didn't have the same result when I calculate the balance accuracy in two different ways?
Method 1 :
balance_accuracy=recall_score(ytest,y_predicted, average='macro')
Method 2:
fpr1, tpr1, thresholds = metrics.roc_curve(ytest, y_predicted, pos_label=1.0)
spec=1-fpr1[1]
sens=tpr1[1]
bc=(sens+spec)/2
Thanks a lot
I want to ask if average_precision_score is enough and no need to calculate precision_score,recall_score?
I did a k-fold cross validation and for each fold I calculate auc,average_precision_score , but I'm asking if I should calculate precision_score,recall_score?
as you know in precision_score,recall_score you pass y_predicted as a parameter, but in average_precision_score you pass y_score?
Thanks
I want to ask you if my code in this way is correct or no. I mean for average_precision_score calculation I pass y_score not y_predicted , Is that true?
for f measure I got a lot of zeros. It seem that my data-sets are imbalanced, How I can deal with the following warning:
UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
Is there any modification for f measure that can handle the imbalance problem in this case?
Is my calculation for accuracy in this way true?
Here is my code:
rf = RandomForestClassifier()
rf.fit( X_train, y_train)
y_pred = rf.predict(X_test)
y_score = rf.predict_proba(X_test)[:, 1]
Average_precision_recall = average_precision_score(y_test, y_score)
accuracy = rf.score(X_test, y_test)
Classification_auc = roc_auc_score(y_test, y_score)
F_measure=metrics.f1_score(y_test, y_pred,average='weighted')
matthews_corr=matthews_corrcoef(y_test, y_pred)
coh_kapp=cohen_kappa_score(y_test,temp_y_test_pred)
waiting for answers!
Thanks in advance