@article {45425,
	title = {Perspectives on leishmania species and stage-specific adaptive mechanisms},
	journal = {Trends In Parasitology},
	volume = {34},
	year = {2018},
	month = {DEC},
	pages = {10.1016/j.pt.2018.09.004},
	type = {Article},
	abstract = {The hurdles in drug and vaccine development pipelines for leishmaniasis, a complex, multifaceted disease, are largely due to the digenetic lifecycle, differential clinical manifestations of the parasite, and the incomplete understanding of its adaptations within its hosts. Here, we discuss the distinct computational and experimental techniques employed to identify the species and stage-specific adaptive mechanisms at different levels of biological organization, the progress made so far, and limitations in comprehending leishmaniasis as a systems biology disease. Based on the available perspectives, we also provide suggestions and requirements to tackle the growing challenges for bridging the genotype with the phenotype. A systems perspective can be instrumental in understanding the complexities of the disease and can provide insights for targeted control.},
	issn = { 1471-4922 },
	doi = {10.1016/j.pt.2018.09.004},
	author = {Subramanian, Abhishek and Sarkar, Ram Rup}
}
@article {46720,
	title = {Prediction of survival rate and effect of drugs on cancer patients with somatic mutations of genes: an AI-based approach},
	journal = {Chemical Biology \& Drug Design},
	volume = {96},
	year = {2020},
	month = {SEP},
	pages = {1005-1019},
	type = {Article},
	abstract = {<p>The causal role of somatic mutation and its interrelationship with gene expression profile during tumor development has already been observed, which plays a major role to decide the cancer grades and overall survival. Accurate and robust prediction of tumor grades and patients{\textquoteright} overall survival are important for prognosis, risk factors identification and betterment of the treatment strategy, especially for highly lethal tumors, like gliomas. Here, with the help of more accurate and widely used machine learning-based approaches, we propose an integrative computational pipeline that incorporates somatic mutations and gene expression profile for survival and grade prediction of glioma patients and simultaneously relates it to the drugs to be administered. This study gives us a clear understanding that the same drug is not effective for the treatment of same grade of cancer if the gene mutations are different. The alteration in a specific gene plays a very important role in tumor progression and should also be considered for the selection of appropriate drugs. This proposed framework includes all the necessary factors required for enhancement of therapeutic designs and could be useful for clinicians in determining an accurate and personalized treatment strategy for individual patients suffering from different life threatening diseases.</p>
},
	keywords = {gene expression and copy number variation, gliomas, grade and survival prediction, machine learning strategy, significant gene prediction and effect of drugs, somatic mutation},
	issn = {1747-0277},
	doi = {10.1111/cbdd.13668},
	author = {Saurabh, Rochi and Nandi, Sutanu and Sinha, Noopur and Shukla, Mudita and Sarkar, Ram Rup}
}
