Effect of sea surface temperature on monsoon rainfall in a coastal region of India

The Indian Monsoon, the onset and strength of which decides the fate of millions of people in the Indian sub continent, depends on a number of global parameters and phenomena – most of which are continuous. It is well established that almost all global climatic phenomena are inter-related. Sea Surface Temperature (SST) is one of the key factors influencing the Indian monsoon. Even roughly predicting the Indian Monsoon helps considerably the economies of the countries in the Indian sub continent – most of which are agriculture based. In the present study, an attempt is made to establish a relationship between Sea Surface Temperatures across the globe and monsoon rainfall in coastal region of Orissa State, India. This study aims at evolving and analyzing the correlation contour patterns using GrADS (Grid Analysis and Display System) software for different lead times and then interpret the salient features, to predict monsoon in Orissa based on SST anomaly data. The correlation contours generated in this study will prove quite useful for predicting the rainfall pattern over Coastal Orissa, based on variation in sea surface temperatures across the globe and in marking out the regions of influence

Replication Data for: Precision Medicine Approach to Signal Based Targeted Therapy of Cancer

The genetic changes appearing in the information system of the cells that program its unregulated growth and proliferation gradually lead to cancer manifestation and the treatment options must be guided accordingly. The critical roles played by some of the molecules associated with the specific signaling pathways and cell microenvironment that lead to oncogenesis and metastasis have been described precisely in recent years based on findings of the human genome project. Thus, precision oncology that relies on the genomic study of the cancer cells to better understand the prognosis and pathways involved with disease progression for the cure is destined to serve the purpose adequately. This article intends to comprehensively elucidate the foundations and frontiers of precision oncology in the context of recent advances in the field of cancer genomics and single-cell technology for efficient cancer treatment.</p

Replication Data for: Precision Oncology, Cell Signaling and Targeted Therapy: A Holistic Approach to Molecular Cancer Therapeutics

In recent decades, there has been a deluge in the large-scale production of anticancer agents, primarily due to advances in genomic technologies enabling precise targeting of oncogenic pathways involved in disease progression. This initiated a paradigm shift in cancer research and therapeutics based on the ability to study molecular changes throughout the genome. It provided a unique opportunity in the field of translational cancer research and have led to the concept of precision medicine in cancer therapy, raising hopes of developing better diagnostic and therapeutic means for the management of cancer. The purpose of this article is to briefly review the tools and techniques involved in precision oncology research and their applications in the field of cancer treatment.</p

The Precision Oncology Approach to Molecular Cancer Therapeutics Targeting Oncogenic Signaling Pathways is a Means to an End

Cancer is a deadly genetic disease with diverse aspects of complexity, including cancer immune evasion, treatment resistance, and recurrence requiring optimized treatment to be cured. Molecular studies have revealed that tumors are profoundly heterogeneous in nature, leading to the complexity of cancer progression that is ultimately linked to its genetic machinery. It is important to note that patients with the same types of cancer respond differently to cancer treatments, indicating the need for patient-specific treatment options. This requires an in-depth genomic study of the patient's tumors to fully understand the driving factors of cancer for effective targeted therapy. Precision oncology has evolved as a form of cancer therapy focused on genetic profiling of tumors to identify molecular alterations involved in cancer development for tailored individualized treatment of the disease. Whole genome sequencing, tumor and cell-free DNA profiling, transcriptomics, proteomics and exploration of the cancer immune system form the basis of this field of cancer research and treatment. This article aims to briefly explain the foundations and frontiers of precision oncology in the context of ongoing technological advancements in related fields of study in to assess its scope and importance in achieving effective cure against cancer.</p

The Precision Oncology Approach to Molecular Cancer Therapeutics Targeting Oncogenic Signaling Pathways is a Means to an End

Cancer is a deadly genetic disease with diverse aspects of complexity, including cancer immune evasion, treatment resistance, and recurrence requiring optimized treatment to be cured. Molecular studies have revealed that tumors are profoundly heterogeneous in nature, leading to the complexity of cancer progression that is ultimately linked to its genetic machinery. It is important to note that patients with the same types of cancer respond differently to cancer treatments, indicating the need for patient-specific treatment options. This requires an in-depth genomic study of the patient's tumors to fully understand the driving factors of cancer for effective targeted therapy. Precision oncology has evolved as a form of cancer therapy focused on genetic profiling of tumors to identify molecular alterations involved in cancer development for tailored individualized treatment of the disease. Whole genome sequencing, tumor and cell-free DNA profiling, transcriptomics, proteomics and exploration of the cancer immune system form the basis of this field of cancer research and treatment. This article aims to briefly explain the foundations and frontiers of precision oncology in the context of ongoing technological advancements in related fields of study in to assess its scope and importance in achieving effective cure against cancer.</p

Prediction of zinc binding sites in proteins using sequence derived information

<p>Zinc is one the most abundant catalytic cofactor and also an important structural component of a large number of metallo-proteins. Hence prediction of zinc metal binding sites in proteins can be a significant step in annotation of molecular function of a large number of proteins. Majority of existing methods for zinc-binding site predictions are based on a data-set of proteins, which has been compiled nearly a decade ago. Hence there is a need to develop zinc-binding site prediction system using the current updated data to include recently added proteins. Herein, we propose a support vector machine-based method, named as ZincBinder, for prediction of zinc metal-binding site in a protein using sequence profile information. The predictor was trained using fivefold cross validation approach and achieved 85.37% sensitivity with 86.20% specificity during training. Benchmarking on an independent non-redundant data-set, which was not used during training, showed better performance of ZincBinder vis-à-vis existing methods. Executable versions, source code, sample datasets, and usage instructions are available at <a href="http://proteininformatics.org/mkumar/znbinder/" target="_blank">http://proteininformatics.org/mkumar/znbinder/</a></p

PalmPred: An SVM Based Palmitoylation Prediction Method Using Sequence Profile Information

<div><p>Protein palmitoylation is the covalent attachment of the 16-carbon fatty acid palmitate to a cysteine residue. It is the most common acylation of protein and occurs only in eukaryotes. Palmitoylation plays an important role in the regulation of protein subcellular localization, stability, translocation to lipid rafts and many other protein functions. Hence, the accurate prediction of palmitoylation site(s) can help in understanding the molecular mechanism of palmitoylation and also in designing various related experiments. Here we present a novel <i>in silico</i> predictor called ‘PalmPred’ to identify palmitoylation sites from protein sequence information using a support vector machine model. The best performance of PalmPred was obtained by incorporating sequence conservation features of peptide of window size 11 using a leave-one-out approach. It helped in achieving an accuracy of 91.98%, sensitivity of 79.23%, specificity of 94.30%, and Matthews Correlation Coefficient of 0.71. PalmPred outperformed existing palmitoylation site prediction methods – IFS-Palm and WAP-Palm on an independent dataset. Based on these measures it can be anticipated that PalmPred will be helpful in identifying candidate palmitoylation sites. All the source datasets, standalone and web-server are available at <a href="http://14.139.227.92/mkumar/palmpred/" target="_blank">http://14.139.227.92/mkumar/palmpred/</a>.</p></div

Prediction of PalmPred on dataset D4_ind taken from Oku et al. 2013.

<p>Prediction of PalmPred on dataset D4_ind taken from Oku et al. 2013.</p

Performance of discrete amino acid and coupled amino acid composition based SVM models during FFCV at 1^st tier.

<p>Performance of discrete amino acid and coupled amino acid composition based SVM models during FFCV at 1^st tier.</p

Performance of discrete amino acid and coupled amino acid composition based SVM models during LOOCV at 2^nd tier.

<p>Performance of discrete amino acid and coupled amino acid composition based SVM models during LOOCV at 2^nd tier.</p