We develop awesome mobile apps for enterprises.
Mobile app development is multidisciplinary and requires product management, design, software development, project management, and quality assurance to achieve great products. Innovi Start is your starting point for discussing product development, product goals, features and functions, identify third-party integrations, create a high-level roadmap divided by phases, and budget.
At the end of the process our clients know the plan for arriving at their desired outcome and the costs to execute the plan.
PRICING: Innovi Start is a prerequisite for creating the plan your company will need to properly execute your mobile strategy. Innovi Start plans start at $6,000 for a one week engagement.
FULL SERVICE PROJECT TEAM
We use an Agile team approach to developing mobile applications. Our project team includes a user interface designer, project manager, and platform developers. Everyone works in unison - from inception to completion, and our projects are tracked using common Agile tools.
Projects are driven by development sprints that are typically 2 weeks apart. Our process for communication requires a single point of contact between your company and ours. We use two types of tools to communicate:
Urgent - Instant communication tools like Slack, Skype, and Google Hangouts for intra-day communications.
Important - Handled through email and video conference, as needed.
PRICING: The vast majority of projects that we undertake have starting budgets of $50,000 - $100,000. The apps we develop often have sophisticated business process and systems integration.
30 DAY GUARANTEE
We provide a 30 day guarantee post-launch. After the project is completed, we will be there with you every step of the way during the launch period.
After the initial 30 day period, ongoing support and maintenance is provided by our maintenance plans.
How did biologists learn that the circadian clock is controlled by a feedback loop?
How should we select the parameter k (representing the length of the motif) in motif-finding algorithms?
Why does the fact that there are 1000s of similar 15-mers fewer than 8 nucleotides apart in the Subtle Motif Problem prevent us from identifying the implanted motifs by pairwise comparisons?
Why does entropy represent a "measure of uncertainty"?
Why do the perfectly conserved columns in the motif logo have information content smaller than 2?
Why is computing Score(Motifs) row-by-row any better than computing this score column-by-column?
The section "From Motif Finding to Finding a Median String" introduces four different notions of distance. This is insane; how am I supposed to distinguish between them?
How can I encode infinity?
Can I see an example of GreedyMotifSearch on a sample dataset?
Sure! Below is an example, and you may also like to read this blog post by our student Graeme Benstead-Hume. Consider the following matrix Dna, and let us walk through GreedyMotifSearch(Dna, 4, 5) when we select the 4-mer ACCT from the first sequence in Dna as the first 4-mer in the growing collection Motifs.
Although GreedyMotifSearch(Dna, 4, 5) will analyze all possible 4-mers from the first sequence, we limit our analysis to a single 4-mer ACCT:
We first construct the matrix Profile(Motifs) of the chosen 4-mer ACCT:
Motifs A C C T
A: 1 0 0 0
C: 0 1 1 0
Profile(Motifs) G: 0 0 0 0
T: 0 0 0 1
Since Pr(Pattern|Profile) = 0 for all 4-mers in the second sequence in Dna, we select its first 4-mer AGGA as the Profile-most probable 4-mer, resulting in the following matrices Motifs and Profile:
Motifs A C C T
A G G A
A: 1 0 0 1/2
C: 0 1/2 1/2 0
Profile(Motifs) G: 0 1/2 1/2 0
T: 0 0 0 1/2
We now compute the probabilities of every 4-mer in the third sequence in Dna based on this profile. The only 4-mer with nonzero probability in the third sequence is ACGT, and so we add it to the growing set of 4-mers:
A C C T
Motifs A G G A
A C G T
A: 1 0 0 1/3
C: 0 2/3 1/3 0
Profile(Motifs) G: 0 1/3 2/3 0
T: 0 0 0 2/3
We now compute the probabilities of every 4-mer in the fourth sequence in Dna based on this profile and find that AGGT is the most probable 4-mer:
CAGC AGCA GCAA CAAG AAGG AGGT GGTG
0 1/27 0 0 0 4/27 0
After adding AGGT to the matrix Motifs, we obtain the following motif and profile matrices:
A C C T
Motifs A G G A
A C G T
A G G T
A: 1 0 0 1/4
C: 0 2/4 1/4 0
Profile(Motifs) G: 0 2/4 3/4 0
T: 0 0 0 3/4
We now compute the probabilities of every 4-mer in the fifth sequence in Dna based on this profile and find that AGCT is the most probable 4-mer:
CACC ACCT CCTG CTGA TGAT GAGT AGCT
0 6/64 0 0 0 0 18/64
After adding AGCT to the motif matrix, we obtain the following motif matrix with consensus AGGT:
A C C T
A G G A
Motifs A C G T
A G G T
A G C T
Why do we select the first k-mers in each string in Dna when we form the initial motif matrix BestMotifs in GreedyMotifSearch?
Aren’t we skewing the probability (compared to the true probabilities) when we add pseudocounts?
Isn't choosing a pseudocount value equal to 1 arbitrary? What would happen if we instead selected, say, 0.1?
Would GreedyMotifSearch (with pseudocounts) still find motifs if the first string in Dna contained no instances of the motif?
Can you give me an example of a Las Vegas algorithm?
What does a four-sided die look like?
Why don't we use pseudocounts in the pseudocode for RandomizedMotifSearch?
How can GibbsSampler be useful if it moves from motifs with better scores to motifs with worse scores?
Is there a way to decide that GibbsSampler has already found the correct motif and save time by stopping it?
Does it make sense for GibbsSampler to select exactly the same row for removal in consecutive iterations?
When solving the Subtle Motif Problem, why did we run RandomizedMotifSearch 100,000 times, but we ran GibbsSampler only 2,000 times?
How do motif finding algorithms deal with homonucleotide runs that may score higher than real motifs?
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